system

Abstract

Provide a system. A means for providing a user interface; means for inputting household chore data from a user; means for transmitting the input data to a server; means for storing and analyzing the input data in a server; A means for generating an efficient way of performing housework based on the analysis results; A means to notify users of the generated efficient housework methods. A system including:

Description

[Technical Field]

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

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

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

[0004] In modern life, housework is an important daily task that requires time and effort, but systems for improving its efficiency are not yet fully developed. In particular, there are only a limited number of systems that provide users with specific suggestions on how to perform housework efficiently. Another issue is that traditional cooking methods passed down in local areas are becoming obsolete. The purpose of this invention is to solve these issues and achieve the efficiency of housework and the preservation of culture. [Means for solving the problem]

[0005] The present invention is a system that includes a means for providing a user interface, a means for inputting data related to housework from a user, a means for transmitting the input data to a server, a means for storing and analyzing the input data in the server, a means for generating efficient ways of performing housework based on the analysis results, and a means for notifying the user of the generated efficient ways of performing housework.The system further includes a means for learning efficient suggestions based on data collected from users and evolving the suggestions, and a means for collecting traditional local cooking methods from users and storing them in a database, thereby simultaneously improving the efficiency of housework and preserving culture.

[0006] A "user interface" is a screen or operating means by which a user interacts with a system and inputs and obtains data.

[0007] "Housework data" refers to detailed information about the housework a user performs, including, for example, the start and end times for washing dishes, and the cooking methods and ingredients used.

[0008] A "server" is a computer system whose role is to receive, store, and analyze data sent by users.

[0009] A "database" is an information storage area within a server that is used to store data about household chores and traditional local cooking methods.

[0010] The "analysis means" refers to algorithms and programs that process data within the server and derive efficient ways to carry out housework.

[0011] "Efficient ways to do housework" are specific techniques and suggestions for completing housework in a shorter time or with less effort.

[0012] The "means for notifying the user" refers to a communication means for conveying the generated proposals and information to the user, and includes, for example, a notification function of the terminal or email.

[0013] "Traditional cooking methods" are unique cooking methods and recipes that are passed down in a particular region or culture.

[0014] The "means of learning" is a mechanism that allows the system to improve and evolve the optimal way of performing housework based on data collected from users. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram illustrating a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0019] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0020] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] System Overview

[0037] This system provides users with suggestions for efficiently completing household chores while also collecting and storing traditional local recipes. The system includes the following functions: user interface, data input, data transmission, data storage and analysis on the server, efficient suggestion generation, suggestion notification, and storage of traditional recipes.

[0038] Providing a user interface

[0039] The device hosts an application and displays a screen where the user can enter data about the chore, such as the start and end times of the chore, the type of chore performed, and the cooking methods and ingredients used.

[0040] Data transmission and storage

[0041] The household chore data entered by the user is sent from the device to a server, which stores the received data in a database for later analysis.

[0042] Analyzing data and generating recommendations

[0043] The server analyzes the stored data. For example, it analyzes how long it took to wash dishes and which cooking method was most efficient. Based on the analysis results, it runs an algorithm that suggests how to proceed with housework more efficiently next time. For example, it might suggest, "If you have a lot of dishes, using the dishwasher next time will reduce the time by 30%."

[0044] Proposal Notification

[0045] The generated suggestions for efficient housework are sent from the server to the device and displayed to the user, who can use these suggestions to perform housework more efficiently.

[0046] Collecting and preserving traditional recipes

[0047] Users can also input traditional local recipes into the system. For example, they can enter detailed recipes such as "traditional ozōni made using my grandmother's recipe." The input data is sent from the device to the server and saved in a database. The saved traditional recipes can then be shared with other users.

[0048] Specific examples

[0049] For example, consider a user washing dishes. The user does the following:

[0050] 1. Launch the application and log in.

[0051] 2. Access the household data entry screen and enter the start time for washing dishes (18:00).

[0052] 3. When you finish washing the dishes, enter the finish time (18:15).

[0053] 4. Click the Send button to send the data to the server.

[0054] The server receives the data and stores it in a database. The server then analyzes the data and generates suggestions, such as "Next time, use an automatic dishwasher to wash dishes more efficiently." The generated suggestions are sent to the device and displayed to the user.

[0055] Also, if users want to preserve traditional cooking methods,

[0056] 1. Access the traditional recipe entry screen and enter the specific steps and ingredients along with the title "Traditional Ozoni made using my grandmother's recipe."

[0057] 2. Click the submit button to send the data to the server.

[0058] The server stores the data and makes it accessible to other users.

[0059] In this way, the system helps users streamline their household chores while preserving traditional local cooking methods.

[0060] The processing flow will be explained below.

[0061] Step 1:

[0062] Terminal: The user launches the application, which then displays the login screen.

[0063] Step 2:

[0064] User: Enters login information and clicks the Login button.

[0065] Step 3:

[0066] Server: Receives login information and performs user authentication. If authentication is successful, sends the user's housework data entry screen to the terminal.

[0067] Step 4:

[0068] Terminal: Displays the housework data entry screen and allows the user to enter housework data.

[0069] Step 5:

[0070] User: Enters household chore data. For example, the start and end times of washing dishes and the cooking method used.

[0071] Step 6:

[0072] Terminal: The entered data is temporarily saved and sent to the server when the send button is pressed.

[0073] Step 7:

[0074] Terminal: When the send button for housework data is pressed, the data is sent to the server. The data includes the user ID and detailed housework information.

[0075] Step 8:

[0076] Server: Stores the received housework data in a database.

[0077] Step 9:

[0078] Server: Analyzes the user's past housework data based on the data stored in the database. For example, calculates the average time for each housework task and the most frequently used cooking method.

[0079] Step 10:

[0080] Server: Based on the analysis results, the server suggests efficient ways to do housework next time. For example, it generates a suggestion such as, "If you use a fully automatic dishwasher next time, you can reduce the time it takes to wash dishes by 30%."

[0081] Step 11:

[0082] Server: Sends the generated proposal to the device.

[0083] Step 12:

[0084] Device: Receives the suggestion and notifies the user, which is displayed on the device screen.

[0085] Step 13:

[0086] User: Review the suggestions and decide whether to incorporate them into your next chore.

[0087] Step 14:

[0088] User: When entering a traditional local recipe, the user accesses the recipe entry screen and enters the specific recipe and steps.

[0089] Step 15:

[0090] Terminal: Sends the inputted traditional recipe data to the server.

[0091] Step 16:

[0092] Server: Stores the received data in a database and manages the stored data so that it can be provided to other users.

[0093] The above is the specific processing flow of this system.

[0094] Example 1

[0095] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0096] In today's busy lifestyles, it is difficult to provide specific methods for efficiently performing household chores individually. Also, considering the current situation where traditional local cooking methods are being lost, there is a need to efficiently collect and preserve these methods and provide an environment where other users can access them.

[0097] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0098] In this invention, the server includes means for providing a user interface, means for inputting data related to housework from a user, means for transmitting the input data to the server, means for storing and analyzing the input data in the server, means for generating efficient housework methods based on the analysis results, means for notifying the user of the generated efficient housework methods, means for inputting local traditional recipes from a user, means for transmitting the input traditional recipes to the server and storing them in a database, and means for managing the stored traditional recipes so that other users can access them, thereby enabling users to efficiently complete housework while saving and sharing local traditional recipes.

[0099] A "user interface" is a collection of screens and controls that allow a user to interact with a system.

[0100] "Data related to housework" refers to information such as the start and end times of housework, the type of housework performed, and the cooking methods and ingredients used.

[0101] "Server" means a computer system that receives, stores, analyzes data from users, and generates and notifies results.

[0102] A "database" is a system that allows a server to efficiently store and manage data received.

[0103] "Analysis" is the process by which the server analyzes the data it receives and stores, and extracts useful information and suggestions.

[0104] "Efficient ways to do housework" refers to optimal methods and suggestions for doing housework efficiently.

[0105] "Notification" is an action that informs the user of the generated efficient way to perform housework.

[0106] "Traditional cooking methods" are cooking methods that are historically and culturally important and have been passed down in local areas.

[0107] "Storage" refers to the act of recording data and information from users on a server so that it can be retrieved as needed.

[0108] "Management" is the process of organizing stored data and making it quickly accessible when needed.

[0109] "Learning" is the process by which the system uses data collected from users to improve its algorithms to make better suggestions next time.

[0110] "Evolution" refers to the way the suggestions are improved through learning, enabling more advanced advice.

[0111] "Transmission" is the process of transmitting data or information entered by the user from the terminal to the server.

[0112] This invention is a system that provides users with suggestions for efficiently completing housework and collects and stores traditional local recipes. The system mainly consists of the following functions: user interface, data input, data transmission, data storage and analysis on the server, efficient suggestion generation, suggestion notification, and storage of traditional recipes.

[0113] Providing a user interface

[0114] The terminal provides an application and displays a screen for users to enter data about housework. The application runs on devices such as smartphones and tablets and can be downloaded from the Android (registered trademark) or iOS application stores. Through an intuitive interface, users can enter data such as the start and end times of housework, the contents of the housework, and the cooking methods and ingredients used. The interface is built using web technologies such as HTML, CSS, and JavaScript (registered trademark).

[0115] Data transmission and storage

[0116] When the user enters household chore data and clicks the send button, the device sends the entered data to the server. Communication is performed using the secure SSL / TLS protocol, and the data is serialized in JSON format. The server then stores the received data in a database such as MySQL (registered trademark) or PostgreSQL.

[0117] Analyzing data and generating recommendations

[0118] The stored data is analyzed on the server. Python libraries such as Pandas and NumPy are used to analyze the data. For example, the difference between the start and end times of housework is calculated to determine how long each chore took. Based on the insights gained from the data, machine learning algorithms are used to suggest efficient ways to perform the chore. The machine learning models used are trained using libraries such as Scikit-learn and TENSORFLOW (registered trademark).

[0119] Proposal Notification

[0120] The generated suggestions are sent from the server to the device via WebSocket or push notification. The device receives the notification and displays its contents to the user. An example of a notification might include specific advice such as "Next time, use the dishwasher to reduce dishwashing time by 30%."

[0121] Collecting and preserving traditional recipes

[0122] Users can enter traditional local recipes through a dedicated screen within the application. For example, they can enter a title like "Traditional Ozoni made using my grandmother's recipe," along with specific steps and ingredients. The entered data is sent from the device to a server and stored in a database. This recipe data is managed so that other users can search and refer to it.

[0123] Specific examples and examples of input prompts to the generative AI model

[0124] For example, if a user washes dishes:

[0125] 1. The user launches the application and logs in.

[0126] 2. Access the household data entry screen and enter the start time (18:00) and end time (18:15) for washing dishes.

[0127] 3. Click the submit button to send the data to the server.

[0128] 4. The server receives the data and stores it in a database.

[0129] 5. The server analyzes the data and generates a suggestion such as, "Next time, use the fully automatic dishwasher to wash dishes more efficiently."

[0130] 6. The generated suggestions are sent to the device and displayed to the user.

[0131] Also, if users want to save traditional recipes:

[0132] 1. Access the traditional recipe entry screen and enter the specific steps and ingredients along with the title "Traditional Ozoni made using my grandmother's recipe."

[0133] 2. Click the submit button to send the data to the server.

[0134] 3. The server stores the data and manages it so that other users can access it.

[0135] Example of an input prompt for a generative AI model:

[0136] 1. "Generate suggestions for efficient ways to wash dishes. I have data that shows a user washed dishes from 18:00 to 18:15. What can they do next time to save time?"

[0137] 2. "Please provide detailed instructions for making traditional ozoni using my grandmother's recipe. Include ingredients and specific steps."

[0138] This allows users to efficiently carry out household chores while preserving and sharing traditional local cooking methods.

[0139] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0140] Step 1:

[0141] The device runs the application and provides a user interface, which displays a form for entering household data. This interface is built using HTML, CSS, and JavaScript.

[0142] Step 2:

[0143] The user logs in to the application and accesses the housework data input screen. The user enters the start time, end time, the chore performed, and the cooking method and ingredients used. Examples of input data include start time "18:00," end time "18:15," chore "washing dishes," cooking method "hand washing," and ingredient "detergent A."

[0144] Step 3:

[0145] The terminal receives user input, triggers a click event on the "Submit" button, serializes the input data, converts it to JSON format, and sends the converted data to the server. The transmission is performed as an HTTP POST request.

[0146] Step 4:

[0147] The server receives the HTTP request, deserializes the data in JSON format, and stores it in a MySQL or PostgreSQL database. The stored data includes the user ID, start time, end time, chore details, and ingredients used.

[0148] Step 5:

[0149] The server periodically analyzes the stored data, using Python's Pandas and NumPy. The server calculates the difference between the start and end times of each chore to calculate the time it took to complete the chore and determine how long each chore took. It compares the chore data with past data and prepares to generate useful suggestions.

[0150] Step 6:

[0151] The server runs a machine learning algorithm based on the analysis data. The algorithm is trained using libraries such as Scikit-learn and TensorFlow. Based on the analysis results, the server generates efficient housework methods. For example, it generates a suggestion that "using the dishwasher next time will reduce dishwashing time by 30%."

[0152] Step 7:

[0153] The server serializes the generated suggestions into JSON format and sends them to the device via WebSocket or push notification. The notification message includes the specific instructions for completing the generated chores.

[0154] Step 8:

[0155] The device receives the notification and displays the suggestions to the user. The notification can be a push notification or a pop-up within the app. The user can then view the suggestions and use them to help them with their next household chore.

[0156] Step 9:

[0157] Users access a dedicated screen for entering traditional recipes, enter a title (e.g., "Traditional Ozoni made using my grandmother's recipe"), specific steps, and ingredients used, and then click the "Submit" button.

[0158] Step 10:

[0159] The terminal serializes the input traditional recipe data into JSON format and sends it to the server as an HTTP POST request.

[0160] Step 11:

[0161] The server receives traditional recipe data and deserializes it in JSON format, which is then stored in a database. The stored data includes the title, steps, and ingredients.

[0162] Step 12:

[0163] The server manages the stored traditional recipe data so that other users can access it. When a user searches for a recipe, the server retrieves the appropriate data from the database and sends it to the user.

[0164] Examples of input prompts for generative AI models:

[0165] "Generate suggestions on how to wash dishes more efficiently. I have data that shows a user washed dishes from 18:00 to 18:15. What can I do to save time next time?"

[0166] "Please provide detailed instructions for making your grandmother's traditional ozoni recipe, including ingredients and specific steps."

[0167] (Application example 1)

[0168] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0169] There are currently a wide variety of methods and systems to support efficient household chores, but most rely on manual input from users, which can be time-consuming and laborious to obtain efficient suggestions. There is also a lack of methods for passing on traditional local cooking methods to the next generation. Therefore, there is a need for a system that allows for efficient household chores at home and preserves and practices traditional cooking methods.

[0170] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0171] In this invention, the server includes means for providing a user interface, means for inputting data related to housework from a user, means for transmitting the input data to the server, means for storing and analyzing the input data in the server, means for generating efficient ways to perform the housework based on the analysis results, means for notifying the user of the generated efficient ways to perform the housework, means for the household robot to monitor the progress of the housework, and means for the household robot to practice traditional cooking methods. This makes it possible for the household robot to reduce the user's workload and support efficient housework while passing on traditional local cooking methods to the next generation.

[0172] A "user interface" is the means, including a display screen and input devices, by which a user interacts with a system.

[0173] "Housework data" refers to information such as the progress of various household tasks performed by the user, the tools and materials used, and the time required.

[0174] "Means for transmitting input data to a server" refers to a function for transmitting data input by a user to a server via a network.

[0175] "Means for storing and analyzing" refers to the technology that the server uses to store the transmitted data in a database and perform analysis.

[0176] "Means for generating efficient ways of performing housework" refers to an algorithm that generates suggestions for optimizing ways of performing housework based on the analysis results.

[0177] "Means for notifying the user of the generated efficient ways to perform housework" refers to the message and notification functions that inform the user of the suggestions generated by the server.

[0178] "Means for a domestic robot to monitor the progress of household chores" refers to technologies, including sensors and cameras, that monitor the progress of a user's household chores and collect necessary data.

[0179] "Means for domestic robots to practice traditional cooking methods" refers to means including operations and techniques that enable the robot to actually cook the traditional cooking methods collected from users.

[0180] System configuration

[0181] This invention is a system in which a home robot efficiently assists users with household chores and also practices traditional local cooking methods. The system mainly consists of the following components:

[0182] 1. User Interface: This includes the display screen and input devices that allow the user to interact with the system. This is done through the home robot's screen or a smartphone app.

[0183] 2. Data input means: A function that allows users to input data about housework, such as start and end times, tools and materials used, etc.

[0184] 3. Communication method: Network communication function for sending input data to the server. Uses Wi-Fi or internet connection.

[0185] 4. Storage and analysis means: The server stores the transmitted data in a database and analyzes it using database software and analysis algorithms.

[0186] 5. Means for generating efficiency suggestions: Based on the analysis results on the server, an algorithm is run to generate suggestions for optimizing how housework is performed.

[0187] 6. Notification: A function to notify users of efficient housework suggestions generated by the server. This uses the home robot's display and smartphone notification functions.

[0188] 7. Monitoring: The home robot will monitor the user's housework progress and collect necessary data using sensors and cameras.

[0189] 8. Cooking execution means: The function of the household robot to actually cook the traditional recipes collected from the user. This includes software to control the robot arm and cooking equipment.

[0190] Program processing

[0191] The server program is written in Python and is responsible for collecting, storing, analyzing, generating recommendations, and notifying users. It mainly uses the following libraries and technologies:

[0192] requests library: Used to perform network communication.

[0193] datetime module: Used to record the start and end times of chores.

[0194] Database software: Data is stored using MySQL, PostgreSQL, etc.

[0195] Analytical algorithms: Use AI models and machine learning algorithms to generate efficient housekeeping routines.

[0196] Specific examples

[0197] 1. Suggestions for making dishwashing more efficient:

[0198] When a user inputs the start and end times for washing dishes into a home robot, the data is sent to a server, where it is analyzed and a suggestion is made, such as "Use the dishwasher next time to reduce the time by 30%," and this is notified to the home robot or smartphone.

[0199] 2. Practicing traditional cooking methods:

[0200] The user inputs the cooking instructions for "Ozoni (rice soup) made using Grandma's recipe" into the home robot. Based on this data, the robot prepares the ingredients and carries out the specific cooking steps. When the cooking is complete, the user is notified.

[0201] Prompt Sentence Examples

[0202] An example of input to the generative AI model is also set as follows:

[0203] "Write a program for a home robot that records household chore data and generates efficiency suggestions."

[0204] "Tell us a program that preserves traditional cooking methods and lets the robot actually do the cooking."

[0205] This will enable household robots to reduce the workload of users, provide efficient household support, and pass on traditional local cooking methods to the next generation.

[0206] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0207] Step 1:

[0208] The user inputs data about household chores (e.g., start and finish times for washing dishes, tools and ingredients used) through the user interface of the home robot or smartphone app. This input data is entered manually by the user.

[0209] Input: Housework data (start time, end time, tools used)

[0210] Output: Data entry confirmation

[0211] Step 2:

[0212] The device sends the input data to the server via network communication, using Wi-Fi or an internet connection.

[0213] Input: Household data entered by the user

[0214] Output: Send data to the server

[0215] Step 3:

[0216] The server stores the received data in a database, which is then used for analysis. The database software used is MySQL or PostgreSQL.

[0217] Input: Housework data sent from the device

[0218] Output: Saved database entries

[0219] Step 4:

[0220] The server runs an analytical algorithm to analyze the stored data, using machine learning and AI models to obtain information that suggests efficient ways to perform housework.

[0221] Input: Saved housework data

[0222] Output: Analysis results (efficient housework suggestions)

[0223] Step 5:

[0224] The server generates efficient housework suggestions based on the analysis results and notifies the home robot or smartphone of the suggestions using voice synthesis and display functions.

[0225] Input: Analysis results

[0226] Output: Notification of efficient housework suggestions

[0227] Step 6:

[0228] A home robot monitors the user's progress as they perform household chores. The robot uses sensors and cameras to monitor and collect data on users' activities in real time.

[0229] Input: Real-time monitoring data

[0230] Output: Housework progress data

[0231] Step 7:

[0232] The user inputs traditional cooking instructions into the domestic robot, specifically, the cooking steps and ingredients, through a user interface.

[0233] Input: Traditional recipe data (cooking steps, ingredients)

[0234] Output: Registration of traditional recipes

[0235] Step 8:

[0236] The server stores data on traditional recipes in a database, allowing the home robot to perform cooking based on that data.

[0237] Input: Traditional recipe data

[0238] Output: Saved recipe database

[0239] Step 9:

[0240] The domestic robot will actually cook traditional recipes based on an existing database, preparing ingredients and cooking according to the specified steps.

[0241] Input: Saved recipe data

[0242] Output: Finished dish

[0243] Step 10:

[0244] The server records the progress of the home robot as monitoring data and notifies the user of the progress as needed, allowing the user to check the progress of cooking in real time.

[0245] Input: Robot progress data

[0246] Output: Progress notification

[0247] This process flow allows the home robot to reduce the user's workload, provide efficient household support, and pass on traditional local cooking methods to the next generation.

[0248] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0249] System Overview

[0250] This system makes suggestions to help users efficiently complete housework, while recognizing the user's emotions and optimizing the housework suggestions based on those emotions.The system includes the following functions: user interface, data input and transmission, server data storage and analysis, efficient suggestion generation, suggestion notification, and an emotion engine.

[0251] Providing a user interface

[0252] The device hosts an application and displays a screen where the user can enter data about the chore, such as the start and end times of the chore, the type of chore performed, and the cooking methods and ingredients used.

[0253] Data transmission and storage

[0254] The household chore data entered by the user is sent from the device to a server, which stores the received data in a database for later analysis.

[0255] Analyzing data and generating recommendations

[0256] The server analyzes the stored data. For example, it analyzes how long it took to wash dishes and which cooking method was most efficient. Based on the analysis results, it runs an algorithm that suggests how to proceed with housework more efficiently next time. For example, it might suggest, "If you have a lot of dishes, using the dishwasher next time will reduce the time by 30%."

[0257] Combining Emotion Engines

[0258] The device is equipped with an emotion engine that recognizes emotions from the user's facial expressions, tone of voice, etc. This emotion engine analyzes the user's stress level and satisfaction in real time.

[0259] Sending and analyzing emotional data

[0260] The device also sends the recognized emotion data to the server, which stores the emotion data in a database and analyzes it in conjunction with the household chore data.

[0261] Generating Sentiment-Based Recommendations

[0262] The server uses emotional data to determine the user's stress level and satisfaction level, and adjusts housework suggestions accordingly. For example, if the user is feeling stressed, it might suggest ways to complete the chore quickly or suggest listening to relaxing music while doing the chore.

[0263] Proposal Notification

[0264] The generated housework suggestions are sent from the server to the device and displayed to the user, who can use these suggestions to perform housework more efficiently and stress-free.

[0265] Specific examples

[0266] For example, consider a user washing dishes. The user does the following:

[0267] 1. Launch the application and log in.

[0268] 2. Access the household data entry screen and enter the start time for washing dishes (18:00).

[0269] 3. When you finish washing the dishes, enter the finish time (18:15) and click the send button.

[0270] The terminal transmits this household chore data to the server.

[0271] In parallel, the device's emotion engine analyzes the user's facial expressions and tone of voice and determines, for example, that the user is tired. This emotion data is also sent to the server.

[0272] The server stores the received data in a database and analyzes it. As a result, it generates suggestions such as "Use the automatic dishwasher next time" or "Listen to relaxing music to reduce stress." These suggestions are notified to the device and displayed to the user.

[0273] In this way, the system can make housework more efficient for users and, by making optimal suggestions based on emotions, reduce stress for users and make housework more comfortable.

[0274] The processing flow will be explained below.

[0275] Step 1:

[0276] Terminal: The user launches the application, which then displays the login screen.

[0277] Step 2:

[0278] User: Enters login information and clicks the Login button.

[0279] Step 3:

[0280] Server: Receives login information and performs user authentication. If authentication is successful, sends the user's housework data entry screen to the terminal.

[0281] Step 4:

[0282] Terminal: Displays the housework data entry screen and allows the user to enter housework data.

[0283] Step 5:

[0284] User: Enters household chore data. For example, the start and end times of washing dishes and the cooking method used.

[0285] Step 6:

[0286] Terminal: The entered data is temporarily saved and sent to the server when the send button is pressed.

[0287] Step 7:

[0288] Terminal: When the send button for housework data is pressed, the data is sent to the server. The data includes the user ID and detailed housework information.

[0289] Step 8:

[0290] Server: Stores the received housework data in a database.

[0291] Step 9:

[0292] Server: Analyzes the user's past housework data based on the data stored in the database. For example, calculates the average time for each housework task and the most frequently used cooking method.

[0293] Step 10:

[0294] Server: Based on the analysis results, the server suggests efficient ways to do housework next time. For example, it generates a suggestion such as, "If you use a fully automatic dishwasher next time, you can reduce the time it takes to wash dishes by 30%."

[0295] Step 11:

[0296] Device: Uses an emotion engine to recognize the user's facial expressions and tone of voice in real time to determine stress levels and satisfaction.

[0297] Step 12:

[0298] Device: Sends emotional data to the server, including the user's stress level, satisfaction level, etc.

[0299] Step 13:

[0300] Server: Receives emotion data and stores it in a database.

[0301] Step 14:

[0302] Server: Combines and analyzes emotional data and housework data, and recalculates efficient ways to perform housework that take the user's emotional state into account.

[0303] Step 15:

[0304] Server: For example, if the user is under high stress, the server will suggest ways to complete housework in a short amount of time or ways to relax, such as listening to music.

[0305] Step 16:

[0306] Server: Sends the generated proposal to the device.

[0307] Step 17:

[0308] Device: Receives the suggestion and notifies the user, which is displayed on the device screen.

[0309] Step 18:

[0310] User: Review the suggestions and decide whether to incorporate them into your next chore.

[0311] Step 19:

[0312] User: When entering a traditional local recipe, the user accesses the recipe entry screen and enters the specific recipe and steps.

[0313] Step 20:

[0314] Terminal: Sends the inputted traditional recipe data to the server.

[0315] Step 21:

[0316] Server: Stores the received data in a database and manages the stored data so that it can be provided to other users.

[0317] The above is the specific processing flow of this system.

[0318] Example 2

[0319] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0320] Conventional housework suggestion systems are limited to suggestions based on user input data and are unable to provide optimal suggestions that reflect the user's emotions and stress levels. This makes it difficult to reduce user stress and provide a comfortable housework environment. Furthermore, there is a lack of suggestions that take into account local traditional methods and the characteristics of individual users.

[0321] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0322] In this invention, the server includes a means for providing a user interface, a means for inputting data related to housework from a user, a means for transmitting the input data to the server, a means for storing and analyzing the input data in the server, a means for generating efficient housework methods based on the analysis results, a means for notifying the user of the generated efficient housework methods, a means for recognizing the user's emotions, a means for transmitting the recognized emotion data to the server, a means for storing and analyzing the emotion data, and a means for optimizing efficient suggestions based on the emotion data. This enables optimal housework suggestions that take into account the user's emotional state and stress level in real time, thereby reducing the user's stress and providing a comfortable housework environment. Furthermore, suggestions are effectively made that take into account local traditional methods and the characteristics of each individual user.

[0323] A "user interface" is the means by which a user interacts with a system and inputs data.

[0324] "Housework data" refers to information such as the type of housework the user performed, the start time, the end time, and the tools and materials used.

[0325] "Server" means a computing device that receives, stores, and analyzes data submitted by users and generates and provides recommendations to users.

[0326] "Means for recognizing emotions" refers to a set of emotion engines and related hardware and software for determining emotions from the user's facial expressions, tone of voice, etc.

[0327] "Efficient ways to do housework" are methods and means for doing housework more efficiently that are suggested to users based on data analyzed by the server.

[0328] "Emotional data" refers to data collected by the emotion engine regarding the user's emotional state, such as stress level and satisfaction.

[0329] "Means of notification" refers to the method by which the server-generated proposal is communicated to the user, such as a push notification or a screen display in the application.

[0330] "Means to learn and evolve" refers to a method of continuously improving the content of proposals using machine learning algorithms based on collected data.

[0331] "Traditional local methods" refer to the household and cooking methods that have been historically practiced in a particular region, and include the means of storing them in a database.

[0332] The "optimizing means" is a method for analyzing emotional data and generating optimal suggestions corresponding to the user's emotional state.

[0333] This invention relates to a system that provides suggestions to help users perform housework efficiently and comfortably. The system includes the following functions: user interface, data input and transmission, server data storage and analysis, efficient suggestion generation, suggestion notification, and emotion engine.

[0334] Providing a user interface

[0335] The device, like a smartphone or tablet, provides an application and displays a screen where users can input data about their household chores. The interface includes fields for inputting the start and end times of chores, specific chore tasks, and the tools and materials used.

[0336] Data transmission and storage

[0337] The household chore data entered by the user is sent from the device via the internet to a server, which stores the received data in a database for later analysis.

[0338] Analyzing data and generating recommendations

[0339] The server analyzes the stored housework data. Specifically, it evaluates the time required for housework and the efficiency of the tools used, and then suggests how to proceed with the housework more efficiently next time. For example, it analyzes the time it takes to wash dishes and generates a suggestion that using an automatic dishwasher next time can reduce the time by 30%.

[0340] Combining Emotion Engines

[0341] The device is equipped with an emotion engine that analyzes the user's facial expressions and tone of voice, and in combination with hardware such as a camera and microphone, it analyzes the user's stress level and satisfaction in real time.

[0342] Sending and analyzing emotional data

[0343] The device sends the recognized emotion data to a server via the Internet. The server stores this emotion data in a database and analyzes it in conjunction with housework data. For example, it can analyze the user's stress level after doing housework.

[0344] Generating Sentiment-Based Recommendations

[0345] The server uses emotional data to assess the user's stress level and satisfaction level, and adjusts housework suggestions accordingly: if the user is tired, it suggests shorter chores or chores to be done while listening to relaxing music.

[0346] Proposal Notification

[0347] The system's generated suggestions are sent from the server to the device and displayed to the user, who can use these suggestions to perform housework more efficiently and stress-free.

[0348] Specific examples

[0349] For example, if a user wants to wash dishes, they might do the following:

[0350] 1. The user launches the application and logs in.

[0351] 2. The user accesses the housework data entry screen and enters the start time (18:00) and end time (18:15) for washing dishes.

[0352] 3. The device sends this household data to the server.

[0353] 4. The device's emotion engine analyzes the user's facial expressions and tone of voice and determines that the user is tired. This emotion data is also sent to the server.

[0354] 5. The server stores the received data in a database, analyzes it, and generates recommendations such as "Use an automatic dishwasher next time" or "Listen to relaxing music to reduce stress."

[0355] 6. This suggestion is notified to the device and displayed to the user.

[0356] Prompt Sentence Examples

[0357] Please suggest efficient ways to do housework. Specifically, I would like the following information: which chores to do and in what order, efficient tools and methods, and things to be careful about. Also, please suggest what to do if you are feeling stressed.

[0358] This allows the system to make housework more efficient for users, and by making optimal suggestions based on emotions, it reduces stress for users and makes housework more comfortable.

[0359] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0360] Step 1: Displaying the User Interface

[0361] The device launches the application and displays a screen where the user can enter data about the chore. Input fields include the start and end times of the chore, the specific chore, and the tools and materials used, making it easy for the user to enter the required data.

[0362] Input: Launch application

[0363] Output: Display of data entry screen

[0364] How it works: The application invokes the interface and provides the necessary input fields.

[0365] Step 2: Enter household chore data

[0366] The user enters the start and end times of the chore, the chore performed, and the tools and materials used in the input fields. For example, the user enters the start time (18:00) and end time (18:15) of washing dishes, and the detergent used.

[0367] Input: Manually entered data by the user

[0368] Output: Input housework data

[0369] How it works: The user enters the required data into the application's input fields and presses the confirm button.

[0370] Step 3: Sending data

[0371] The device sends the household data entered by the user to a server via the Internet, with encryption protocols such as SSL used to ensure security during transmission.

[0372] Input: Entered housework data

[0373] Output: Send data to the server

[0374] What it does: After the submit button is pressed, the data is sent as an HTTP request to the server.

[0375] Step 4: Save your data

[0376] The server stores the received housework data in a database, including detailed information such as the date and time, the contents of the housework, the time required, and the tools used.

[0377] Input: Submitted housekeeping data

[0378] Output: Housework data stored in a database

[0379] How it works: The server parses the data and stores it in the appropriate tables in the database.

[0380] Step 5: Analyze the data

[0381] The server analyzes the efficiency of housework based on the stored housework data, for example, evaluating the time it took to wash dishes and the efficiency of the tools used, and generates suggestions for the next time.

[0382] Input: Saved housework data

[0383] Output: Analysis results on housework efficiency

[0384] How it works: The server analyzes the data using statistical methods and machine learning models to generate results.

[0385] Step 6: Generate proposals

[0386] The server then makes suggestions based on the analysis results on how to efficiently carry out housework, such as suggesting that you use an automatic dishwasher next time.

[0387] Input: Analysis results on housework efficiency

[0388] Output: Suggestions for efficient housework

[0389] Operation: The server runs the proposal algorithm and stores the generated proposals in a database.

[0390] Step 7: Collect emotion data

[0391] The device uses an emotion engine to analyze the user's facial expressions and tone of voice to collect emotional data, such as measuring the user's stress level and satisfaction in real time using the camera and microphone.

[0392] Input: User's facial expression, tone of voice

[0393] Output: Collected emotion data

[0394] How it works: The emotion engine analyzes the user's video and audio and quantifies their emotional state.

[0395] Step 8: Sending Emotion Data

[0396] The device sends the collected emotion data to a server via the internet, using encryption protocols such as SSL to ensure security.

[0397] Input: Collected emotion data

[0398] Output: Send emotion data to the server

[0399] How it works: After the send button is pressed, the emotion data is sent as an HTTP request to the server.

[0400] Step 9: Store and analyze sentiment data

[0401] The server stores the received emotion data in a database and analyzes it in conjunction with housework data, for example, to analyze the user's stress level after doing housework.

[0402] Input: Emotion data sent

[0403] Output: Emotion data stored in a database, analysis results including emotion data

[0404] Operation: The server stores the emotion data in the appropriate table in the database, integrates it with the household chore data, and performs analysis.

[0405] Step 10: Generate sentiment-based suggestions

[0406] The server optimizes efficient suggestions based on emotion data. If the user is tired, it suggests ways to complete the chore quickly or to do it while listening to relaxing music.

[0407] Input: Analysis results including sentiment data

[0408] Output: Optimized suggestions based on emotions

[0409] Operation: The server runs the proposal algorithm and stores the generated proposals in a database.

[0410] Step 11: Proposal Notification

[0411] The server notifies the device of new suggestions generated and displays them to the user, for example, by using the push notification function of a smartphone.

[0412] Input: Generated proposals

[0413] Output: Proposal notification to device, suggestion display to user

[0414] How it works: The server uses the Notifications API to send suggestions to the device, which then displays them to the user as push notifications.

[0415] Through these processing steps, the system can streamline the user's housework and provide optimal suggestions based on the user's emotional state.

[0416] (Application example 2)

[0417] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0418] In today's world, the use of food delivery services is rapidly increasing, but the user experience is uniform and does not take into account the emotions and circumstances of individual users. This can lead to dissatisfaction and a decrease in repeat customers. It can also cause users to feel stressed and the service to be inefficient. Therefore, there was a need for a system that could use user emotional data to optimize suggestions and provide feedback tailored to individual needs.

[0419] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0420] In this invention, the server includes means for providing a user interface, means for inputting data related to housework from a user, means for transmitting the input data to the server, means for storing and analyzing the input data in the server, means for generating efficient housework methods based on the analysis results, means for notifying the user of the generated efficient housework methods, means for acquiring user emotion data, means for transmitting the acquired emotion data to the server, and means for analyzing the emotion data and optimizing suggestions for housework methods. This enables optimal suggestions based on the emotions of each individual user, thereby improving satisfaction and reuse rates of food delivery services.

[0421] A "user interface" is the part of a system that provides a screen or operating means for a user to input data or view information.

[0422] "Data" refers to recorded content such as information, numbers, text, etc. obtained from a user.

[0423] A "server" is a computer system for storing, analyzing, and processing data.

[0424] "Emotion data" is information indicating the emotional state of the user obtained from facial expressions, voice, etc.

[0425] "Analysis" is the act of generating useful information and suggestions based on collected data using methods such as statistical processing and machine learning.

[0426] "Suggestions" refer to useful information or guidelines for action that the server provides to the user based on the analysis results.

[0427] "Notification" refers to the act of sending the generated suggestion to the user's terminal and displaying it to the user.

[0428] "Optimization" is the act of adjusting a proposal to achieve the most effective and efficient results under specific conditions.

[0429] A "food delivery service" is a service that allows you to order food and drinks and have them delivered to a specified location.

[0430] To implement this invention, a system using a user interface, data acquisition and transmission, data analysis and suggestion generation by a server, and an emotion recognition engine is required.

[0431] composition

[0432] User Interface

[0433] The device provides an interface for users to input data. This interface includes screens where users can enter chore and meal data. Users enter data such as start and end times for chores, meal details, and ingredients used.

[0434] Data transmission and storage

[0435] The device sends the data entered by the user to a server, where it is stored in a database for later analysis.

[0436] Analyzing data and generating recommendations

[0437] The server generates suggestions for efficient housework and food delivery based on the stored data. For example, suggestions for housework include "using the dishwasher to save time" and for food delivery include "listening to relaxing music while doing the job." The server performs analysis using Python and R.

[0438] Emotion Engine

[0439] The device is equipped with an emotion engine that analyzes the user's facial expressions and tone of voice to obtain emotional data, which is also sent to a server and used to optimize suggestions for housework and food delivery.

[0440] Sending and analyzing emotional data

[0441] The device sends the acquired emotional data to the server, which stores this emotional data in a database and analyzes it in conjunction with the household chore data. By using the emotional data, the suggestions are optimized to match the user's emotional state.

[0442] Proposal Notification

[0443] The server then notifies the user of the generated suggestions via push notifications or in-app notifications.

[0444] Hardware and software used

[0445] Hardware:

[0446] Camera (webcam or smartphone camera): To capture the user's facial expressions.

[0447] Smartphone or PC: To provide the user interface and enter data.

[0448] software:

[0449] OpenCV: To capture and preprocess the user's facial expressions.

[0450] TensorFlow / Keras: To run the emotion recognition model.

[0451] Flask or Django: For parsing and saving data server-side.

[0452] Pandas / Numpy: To perform data analysis.

[0453] Requests: To send data.

[0454] Specific examples

[0455] For example, consider a user ordering food delivery. The user does the following:

[0456] 1. Launch the application and log in.

[0457] 2. Access the meal data entry screen and enter the details of the meal you ordered (e.g., pizza, salad) and the delivery time (e.g., 18:30).

[0458] 3. Use the device's camera to capture the user's facial expression.

[0459] The device sends this data and emotional data to a server, which stores and analyzes the data. As a result, it generates suggestions, such as "Next time, serve a relaxing dinner set." These suggestions are then communicated to the user via push notifications or in-app messages.

[0460] Prompt Sentence Examples

[0461] "I'd like to develop a food delivery app, but I'd like to add a feature that suggests meals based on the user's emotions. Specifically, the application should capture and analyze the user's facial expressions and tone of voice, send that data to a server, and notify the server with generated suggestions."

[0462] This will enable optimal proposals tailored to each user's individual circumstances, which is expected to improve satisfaction with the service.

[0463] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0464] Step 1:

[0465] The user launches the application and logs in.

[0466] Input: User credentials (username, password)

[0467] Output: The user is authenticated and redirected to the main page.

[0468] Operation: The terminal sends the authentication information entered by the user to the server, which then checks it against a database to perform the authentication process.

[0469] Step 2:

[0470] The user accesses the meal data entry screen and enters the details of the ordered meal and the delivery time.

[0471] Input: Meal details (e.g., pizza, salad), delivery time (e.g., 18:30)

[0472] Output: The entered meal data is saved on the device and a submit button is displayed.

[0473] How it works: The terminal's user interface provides the user with fields for data entry and a submit button.

[0474] Step 3:

[0475] It uses the device's camera to capture the user's facial expressions.

[0476] Input: An image of the user's face

[0477] Output: Captured face image data

[0478] What it does: The device activates the camera, captures the user's facial expressions, and saves them as image data.

[0479] Step 4:

[0480] Analyze emotions from facial image data.

[0481] Input: Face image data

[0482] Output: Numerical data of the user's emotions (e.g., emotion scores such as happiness, sadness, anger, etc.)

[0483] How it works: The device preprocesses facial images using OpenCV and inputs them into a TensorFlow / Keras model to analyze emotions.

[0484] Step 5:

[0485] The input food data and emotion data are sent to the server.

[0486] Input: Food data, emotion data

[0487] Output: User's food and emotion data stored on the server

[0488] How it works: The device converts this data into JSON format and sends it to the server, which stores it in a database.

[0489] Step 6:

[0490] The server analyzes the received data and generates efficient meal suggestions.

[0491] Input: Stored food and emotion data

[0492] Output: Optimized meal recommendations (e.g., menu recommendations for your next order and time discount information)

[0493] How it works: The server uses Pandas and Numpy to analyze the data and generate recommendations that take into account the user's past order history and emotional state.

[0494] Step 7:

[0495] The suggestions will be sent to the user's device.

[0496] Input: Optimized meal suggestions

[0497] Output: Proposal notification displayed on the user's device

[0498] How it works: The server sends the generated suggestions to the user's device as push notifications or in-app messages.

[0499] This will provide the optimal food delivery experience according to the user's emotional state, and is expected to improve service satisfaction and increase repeat rates.

[0500] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0501] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0502] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0503] [Second embodiment]

[0504] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0505] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0506] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0507] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0508] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0509] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0510] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0511] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0512] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0513] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0514] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0515] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0516] System Overview

[0517] This system provides users with suggestions for efficiently completing household chores while also collecting and storing traditional local recipes. The system includes the following functions: user interface, data input, data transmission, data storage and analysis on the server, efficient suggestion generation, suggestion notification, and storage of traditional recipes.

[0518] Providing a user interface

[0519] The device hosts an application and displays a screen where the user can enter data about the chore, such as the start and end times of the chore, the type of chore performed, and the cooking methods and ingredients used.

[0520] Data transmission and storage

[0521] The household chore data entered by the user is sent from the device to a server, which stores the received data in a database for later analysis.

[0522] Analyzing data and generating recommendations

[0523] The server analyzes the stored data. For example, it analyzes how long it took to wash dishes and which cooking method was most efficient. Based on the analysis results, it runs an algorithm that suggests how to proceed with housework more efficiently next time. For example, it might suggest, "If you have a lot of dishes, using the dishwasher next time will reduce the time by 30%."

[0524] Proposal Notification

[0525] The generated suggestions for efficient housework are sent from the server to the device and displayed to the user, who can use these suggestions to perform housework more efficiently.

[0526] Collecting and preserving traditional recipes

[0527] Users can also input traditional local recipes into the system. For example, they can enter detailed recipes such as "traditional ozōni made using my grandmother's recipe." The input data is sent from the device to the server and saved in a database. The saved traditional recipes can then be shared with other users.

[0528] Specific examples

[0529] For example, consider a user washing dishes. The user does the following:

[0530] 1. Launch the application and log in.

[0531] 2. Access the household data entry screen and enter the start time for washing dishes (18:00).

[0532] 3. When you finish washing the dishes, enter the finish time (18:15).

[0533] 4. Click the Send button to send the data to the server.

[0534] The server receives the data and stores it in a database. The server then analyzes the data and generates suggestions, such as "Next time, use an automatic dishwasher to wash dishes more efficiently." The generated suggestions are sent to the device and displayed to the user.

[0535] Also, if users want to preserve traditional cooking methods,

[0536] 1. Access the traditional recipe entry screen and enter the specific steps and ingredients along with the title "Traditional Ozoni made using my grandmother's recipe."

[0537] 2. Click the submit button to send the data to the server.

[0538] The server stores the data and makes it accessible to other users.

[0539] In this way, the system helps users streamline their household chores while preserving traditional local cooking methods.

[0540] The processing flow will be explained below.

[0541] Step 1:

[0542] Terminal: The user launches the application, which then displays the login screen.

[0543] Step 2:

[0544] User: Enters login information and clicks the Login button.

[0545] Step 3:

[0546] Server: Receives login information and performs user authentication. If authentication is successful, sends the user's housework data entry screen to the terminal.

[0547] Step 4:

[0548] Terminal: Displays the housework data entry screen and allows the user to enter housework data.

[0549] Step 5:

[0550] User: Enters household chore data. For example, the start and end times of washing dishes and the cooking method used.

[0551] Step 6:

[0552] Terminal: The entered data is temporarily saved and sent to the server when the send button is pressed.

[0553] Step 7:

[0554] Terminal: When the send button for housework data is pressed, the data is sent to the server. The data includes the user ID and detailed housework information.

[0555] Step 8:

[0556] Server: Stores the received housework data in a database.

[0557] Step 9:

[0558] Server: Analyzes the user's past housework data based on the data stored in the database. For example, calculates the average time for each housework task and the most frequently used cooking method.

[0559] Step 10:

[0560] Server: Based on the analysis results, the server suggests efficient ways to do housework next time. For example, it generates a suggestion such as, "If you use a fully automatic dishwasher next time, you can reduce the time it takes to wash dishes by 30%."

[0561] Step 11:

[0562] Server: Sends the generated proposal to the device.

[0563] Step 12:

[0564] Device: Receives the suggestion and notifies the user, which is displayed on the device screen.

[0565] Step 13:

[0566] User: Review the suggestions and decide whether to incorporate them into your next chore.

[0567] Step 14:

[0568] User: When entering a traditional local recipe, the user accesses the recipe entry screen and enters the specific recipe and steps.

[0569] Step 15:

[0570] Terminal: Sends the inputted traditional recipe data to the server.

[0571] Step 16:

[0572] Server: Stores the received data in a database and manages the stored data so that it can be provided to other users.

[0573] The above is the specific processing flow of this system.

[0574] Example 1

[0575] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0576] In today's busy lifestyles, it is difficult to provide specific methods for efficiently performing household chores individually. Also, considering the current situation where traditional local cooking methods are being lost, there is a need to efficiently collect and preserve these methods and provide an environment where other users can access them.

[0577] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0578] In this invention, the server includes means for providing a user interface, means for inputting data related to housework from a user, means for transmitting the input data to the server, means for storing and analyzing the input data in the server, means for generating efficient housework methods based on the analysis results, means for notifying the user of the generated efficient housework methods, means for inputting local traditional recipes from a user, means for transmitting the input traditional recipes to the server and storing them in a database, and means for managing the stored traditional recipes so that other users can access them, thereby enabling users to efficiently complete housework while saving and sharing local traditional recipes.

[0579] A "user interface" is a collection of screens and controls that allow a user to interact with a system.

[0580] "Data related to housework" refers to information such as the start and end times of housework, the type of housework performed, and the cooking methods and ingredients used.

[0581] "Server" means a computer system that receives, stores, analyzes data from users, and generates and notifies results.

[0582] A "database" is a system that allows a server to efficiently store and manage data received.

[0583] "Analysis" is the process by which the server analyzes the data it receives and stores, and extracts useful information and suggestions.

[0584] "Efficient ways to do housework" refers to optimal methods and suggestions for doing housework efficiently.

[0585] "Notification" is an action that informs the user of the generated efficient way to perform housework.

[0586] "Traditional cooking methods" are cooking methods that are historically and culturally important and have been passed down in local areas.

[0587] "Storage" refers to the act of recording data and information from users on a server so that it can be retrieved as needed.

[0588] "Management" is the process of organizing stored data and making it quickly accessible when needed.

[0589] "Learning" is the process by which the system uses data collected from users to improve its algorithms to make better suggestions next time.

[0590] "Evolution" refers to the way the suggestions are improved through learning, enabling more advanced advice.

[0591] "Transmission" is the process of transmitting data or information entered by the user from the terminal to the server.

[0592] This invention is a system that provides users with suggestions for efficiently completing housework and collects and stores traditional local recipes. The system mainly consists of the following functions: user interface, data input, data transmission, data storage and analysis on the server, efficient suggestion generation, suggestion notification, and storage of traditional recipes.

[0593] Providing a user interface

[0594] The terminal provides an application and displays a screen for users to enter data about their household chores. The application runs on devices such as smartphones and tablets and can be downloaded from Android or iOS application stores. Through an intuitive interface, users can enter data such as the start and end times of chores, the chore content, and the cooking methods and ingredients used. The interface is built using web technologies such as HTML, CSS, and JavaScript.

[0595] Data transmission and storage

[0596] When a user enters household chore data and clicks the send button, the device sends the data to the server. The communication is secure using the SSL / TLS protocol, and the data is serialized in JSON format. The server then stores the received data in a database such as MySQL or PostgreSQL.

[0597] Analyzing data and generating recommendations

[0598] The stored data is analyzed on the server. Python libraries such as Pandas and NumPy are used to analyze the data. For example, the difference between the start and end times of housework is calculated to determine how long each chore took. Based on the insights gained from the data, a machine learning algorithm is used to suggest efficient ways to perform the chore. The machine learning model used is trained using libraries such as Scikit-learn and TensorFlow.

[0599] Proposal Notification

[0600] The generated suggestions are sent from the server to the device via WebSocket or push notification. The device receives the notification and displays its contents to the user. An example of a notification might include specific advice such as "Next time, use the dishwasher to reduce dishwashing time by 30%."

[0601] Collecting and preserving traditional recipes

[0602] Users can enter traditional local recipes through a dedicated screen within the application. For example, they can enter a title like "Traditional Ozoni made using my grandmother's recipe," along with specific steps and ingredients. The entered data is sent from the device to a server and stored in a database. This recipe data is managed so that other users can search and refer to it.

[0603] Specific examples and examples of input prompts to the generative AI model

[0604] For example, if a user washes dishes:

[0605] 1. The user launches the application and logs in.

[0606] 2. Access the household data entry screen and enter the start time (18:00) and end time (18:15) for washing dishes.

[0607] 3. Click the submit button to send the data to the server.

[0608] 4. The server receives the data and stores it in a database.

[0609] 5. The server analyzes the data and generates a suggestion such as, "Next time, use the fully automatic dishwasher to wash dishes more efficiently."

[0610] 6. The generated suggestions are sent to the device and displayed to the user.

[0611] Also, if users want to save traditional recipes:

[0612] 1. Access the traditional recipe entry screen and enter the specific steps and ingredients along with the title "Traditional Ozoni made using my grandmother's recipe."

[0613] 2. Click the submit button to send the data to the server.

[0614] 3. The server stores the data and manages it so that other users can access it.

[0615] Example of an input prompt for a generative AI model:

[0616] 1. "Generate suggestions for efficient ways to wash dishes. I have data that shows a user washed dishes from 18:00 to 18:15. What can they do next time to save time?"

[0617] 2. "Please provide detailed instructions for making traditional ozoni using my grandmother's recipe. Include ingredients and specific steps."

[0618] This allows users to efficiently carry out household chores while preserving and sharing traditional local cooking methods.

[0619] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0620] Step 1:

[0621] The device runs the application and provides a user interface, which displays a form for entering household data. This interface is built using HTML, CSS, and JavaScript.

[0622] Step 2:

[0623] The user logs in to the application and accesses the housework data input screen. The user enters the start time, end time, the chore performed, and the cooking method and ingredients used. Examples of input data include start time "18:00," end time "18:15," chore "washing dishes," cooking method "hand washing," and ingredient "detergent A."

[0624] Step 3:

[0625] The terminal receives user input, triggers a click event on the "Submit" button, serializes the input data, converts it to JSON format, and sends the converted data to the server. The transmission is performed as an HTTP POST request.

[0626] Step 4:

[0627] The server receives the HTTP request, deserializes the data in JSON format, and stores it in a MySQL or PostgreSQL database. The stored data includes the user ID, start time, end time, chore details, and ingredients used.

[0628] Step 5:

[0629] The server periodically analyzes the stored data, using Python's Pandas and NumPy. The server calculates the difference between the start and end times of each chore to calculate the time it took to complete the chore and determine how long each chore took. It compares the chore data with past data and prepares to generate useful suggestions.

[0630] Step 6:

[0631] The server runs a machine learning algorithm based on the analysis data. The algorithm is trained using libraries such as Scikit-learn and TensorFlow. Based on the analysis results, the server generates efficient housework methods. For example, it generates a suggestion that "using the dishwasher next time will reduce dishwashing time by 30%."

[0632] Step 7:

[0633] The server serializes the generated suggestions into JSON format and sends them to the device via WebSocket or push notification. The notification message includes the specific instructions for completing the generated chores.

[0634] Step 8:

[0635] The device receives the notification and displays the suggestions to the user. The notification can be a push notification or a pop-up within the app. The user can then view the suggestions and use them to help them with their next household chore.

[0636] Step 9:

[0637] Users access a dedicated screen for entering traditional recipes, enter a title (e.g., "Traditional Ozoni made using my grandmother's recipe"), specific steps, and ingredients used, and then click the "Submit" button.

[0638] Step 10:

[0639] The terminal serializes the input traditional recipe data into JSON format and sends it to the server as an HTTP POST request.

[0640] Step 11:

[0641] The server receives traditional recipe data and deserializes it in JSON format, which is then stored in a database. The stored data includes the title, steps, and ingredients.

[0642] Step 12:

[0643] The server manages the stored traditional recipe data so that other users can access it. When a user searches for a recipe, the server retrieves the appropriate data from the database and sends it to the user.

[0644] Examples of input prompts for generative AI models:

[0645] "Generate suggestions on how to wash dishes more efficiently. I have data that shows a user washed dishes from 18:00 to 18:15. What can I do to save time next time?"

[0646] "Please provide detailed instructions for making your grandmother's traditional ozoni recipe, including ingredients and specific steps."

[0647] (Application example 1)

[0648] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0649] There are currently a wide variety of methods and systems to support efficient household chores, but most rely on manual input from users, which can be time-consuming and laborious to obtain efficient suggestions. There is also a lack of methods for passing on traditional local cooking methods to the next generation. Therefore, there is a need for a system that allows for efficient household chores at home and preserves and practices traditional cooking methods.

[0650] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0651] In this invention, the server includes means for providing a user interface, means for inputting data related to housework from a user, means for transmitting the input data to the server, means for storing and analyzing the input data in the server, means for generating efficient ways to perform the housework based on the analysis results, means for notifying the user of the generated efficient ways to perform the housework, means for the household robot to monitor the progress of the housework, and means for the household robot to practice traditional cooking methods. This makes it possible for the household robot to reduce the user's workload and support efficient housework while passing on traditional local cooking methods to the next generation.

[0652] A "user interface" is the means, including a display screen and input devices, by which a user interacts with a system.

[0653] "Housework data" refers to information such as the progress of various household tasks performed by the user, the tools and materials used, and the time required.

[0654] "Means for transmitting input data to a server" refers to a function for transmitting data input by a user to a server via a network.

[0655] "Means for storing and analyzing" refers to the technology that the server uses to store the transmitted data in a database and perform analysis.

[0656] "Means for generating efficient ways of performing housework" refers to an algorithm that generates suggestions for optimizing ways of performing housework based on the analysis results.

[0657] "Means for notifying the user of the generated efficient ways to perform housework" refers to the message and notification functions that inform the user of the suggestions generated by the server.

[0658] "Means for a domestic robot to monitor the progress of household chores" refers to technologies, including sensors and cameras, that monitor the progress of a user's household chores and collect necessary data.

[0659] "Means for domestic robots to practice traditional cooking methods" refers to means including operations and techniques that enable the robot to actually cook the traditional cooking methods collected from users.

[0660] System configuration

[0661] This invention is a system in which a home robot efficiently assists users with household chores and also practices traditional local cooking methods. The system mainly consists of the following components:

[0662] 1. User Interface: This includes the display screen and input devices that allow the user to interact with the system. This is done through the home robot's screen or a smartphone app.

[0663] 2. Data input means: A function that allows users to input data about housework, such as start and end times, tools and materials used, etc.

[0664] 3. Communication method: Network communication function for sending input data to the server. Uses Wi-Fi or internet connection.

[0665] 4. Storage and analysis means: The server stores the transmitted data in a database and analyzes it using database software and analysis algorithms.

[0666] 5. Means for generating efficiency suggestions: Based on the analysis results on the server, an algorithm is run to generate suggestions for optimizing how housework is performed.

[0667] 6. Notification: A function to notify users of efficient housework suggestions generated by the server. This uses the home robot's display and smartphone notification functions.

[0668] 7. Monitoring: The home robot will monitor the user's housework progress and collect necessary data using sensors and cameras.

[0669] 8. Cooking execution means: The function of the household robot to actually cook the traditional recipes collected from the user. This includes software to control the robot arm and cooking equipment.

[0670] Program processing

[0671] The server program is written in Python and is responsible for collecting, storing, analyzing, generating recommendations, and notifying users. It mainly uses the following libraries and technologies:

[0672] requests library: Used to perform network communication.

[0673] datetime module: Used to record the start and end times of chores.

[0674] Database software: Data is stored using MySQL, PostgreSQL, etc.

[0675] Analytical algorithms: Use AI models and machine learning algorithms to generate efficient housekeeping routines.

[0676] Specific examples

[0677] 1. Suggestions for making dishwashing more efficient:

[0678] When a user inputs the start and end times for washing dishes into a home robot, the data is sent to a server, where it is analyzed and a suggestion is made, such as "Use the dishwasher next time to reduce the time by 30%," and this is notified to the home robot or smartphone.

[0679] 2. Practicing traditional cooking methods:

[0680] The user inputs the cooking instructions for "Ozoni (rice soup) made using Grandma's recipe" into the home robot. Based on this data, the robot prepares the ingredients and carries out the specific cooking steps. When the cooking is complete, the user is notified.

[0681] Prompt Sentence Examples

[0682] An example of input to the generative AI model is also set as follows:

[0683] "Write a program for a home robot that records household chore data and generates efficiency suggestions."

[0684] "Tell us a program that preserves traditional cooking methods and lets the robot actually do the cooking."

[0685] This will enable household robots to reduce the workload of users, provide efficient household support, and pass on traditional local cooking methods to the next generation.

[0686] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0687] Step 1:

[0688] The user inputs data about household chores (e.g., start and finish times for washing dishes, tools and ingredients used) through the user interface of the home robot or smartphone app. This input data is entered manually by the user.

[0689] Input: Housework data (start time, end time, tools used)

[0690] Output: Data entry confirmation

[0691] Step 2:

[0692] The device sends the input data to the server via network communication, using Wi-Fi or an internet connection.

[0693] Input: Household data entered by the user

[0694] Output: Send data to the server

[0695] Step 3:

[0696] The server stores the received data in a database, which is then used for analysis. The database software used is MySQL or PostgreSQL.

[0697] Input: Housework data sent from the device

[0698] Output: Saved database entries

[0699] Step 4:

[0700] The server runs an analytical algorithm to analyze the stored data, using machine learning and AI models to obtain information that suggests efficient ways to perform housework.

[0701] Input: Saved housework data

[0702] Output: Analysis results (efficient housework suggestions)

[0703] Step 5:

[0704] The server generates efficient housework suggestions based on the analysis results and notifies the home robot or smartphone of the suggestions using voice synthesis and display functions.

[0705] Input: Analysis results

[0706] Output: Notification of efficient housework suggestions

[0707] Step 6:

[0708] A home robot monitors the user's progress as they perform household chores. The robot uses sensors and cameras to monitor and collect data on users' activities in real time.

[0709] Input: Real-time monitoring data

[0710] Output: Housework progress data

[0711] Step 7:

[0712] The user inputs traditional cooking instructions into the domestic robot, specifically, the cooking steps and ingredients, through a user interface.

[0713] Input: Traditional recipe data (cooking steps, ingredients)

[0714] Output: Registration of traditional recipes

[0715] Step 8:

[0716] The server stores data on traditional recipes in a database, allowing the home robot to perform cooking based on that data.

[0717] Input: Traditional recipe data

[0718] Output: Saved recipe database

[0719] Step 9:

[0720] The domestic robot will actually cook traditional recipes based on an existing database, preparing ingredients and cooking according to the specified steps.

[0721] Input: Saved recipe data

[0722] Output: Finished dish

[0723] Step 10:

[0724] The server records the progress of the home robot as monitoring data and notifies the user of the progress as needed, allowing the user to check the progress of cooking in real time.

[0725] Input: Robot progress data

[0726] Output: Progress notification

[0727] This process flow allows the home robot to reduce the user's workload, provide efficient household support, and pass on traditional local cooking methods to the next generation.

[0728] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0729] System Overview

[0730] This system makes suggestions to help users efficiently complete housework, while recognizing the user's emotions and optimizing the housework suggestions based on those emotions.The system includes the following functions: user interface, data input and transmission, server data storage and analysis, efficient suggestion generation, suggestion notification, and an emotion engine.

[0731] Providing a user interface

[0732] The device hosts an application and displays a screen where the user can enter data about the chore, such as the start and end times of the chore, the type of chore performed, and the cooking methods and ingredients used.

[0733] Data transmission and storage

[0734] The household chore data entered by the user is sent from the device to a server, which stores the received data in a database for later analysis.

[0735] Analyzing data and generating recommendations

[0736] The server analyzes the stored data. For example, it analyzes how long it took to wash dishes and which cooking method was most efficient. Based on the analysis results, it runs an algorithm that suggests how to proceed with housework more efficiently next time. For example, it might suggest, "If you have a lot of dishes, using the dishwasher next time will reduce the time by 30%."

[0737] Combining Emotion Engines

[0738] The device is equipped with an emotion engine that recognizes emotions from the user's facial expressions, tone of voice, etc. This emotion engine analyzes the user's stress level and satisfaction in real time.

[0739] Sending and analyzing emotional data

[0740] The device also sends the recognized emotion data to the server, which stores the emotion data in a database and analyzes it in conjunction with the household chore data.

[0741] Generating Sentiment-Based Recommendations

[0742] The server uses emotional data to determine the user's stress level and satisfaction level, and adjusts housework suggestions accordingly. For example, if the user is feeling stressed, it might suggest ways to complete the chore quickly or suggest listening to relaxing music while doing the chore.

[0743] Proposal Notification

[0744] The generated housework suggestions are sent from the server to the device and displayed to the user, who can use these suggestions to perform housework more efficiently and stress-free.

[0745] Specific examples

[0746] For example, consider a user washing dishes. The user does the following:

[0747] 1. Launch the application and log in.

[0748] 2. Access the household data entry screen and enter the start time for washing dishes (18:00).

[0749] 3. When you finish washing the dishes, enter the finish time (18:15) and click the send button.

[0750] The terminal transmits this household chore data to the server.

[0751] In parallel, the device's emotion engine analyzes the user's facial expressions and tone of voice and determines, for example, that the user is tired. This emotion data is also sent to the server.

[0752] The server stores the received data in a database and analyzes it. As a result, it generates suggestions such as "Use the automatic dishwasher next time" or "Listen to relaxing music to reduce stress." These suggestions are notified to the device and displayed to the user.

[0753] In this way, the system can make housework more efficient for users and, by making optimal suggestions based on emotions, reduce stress for users and make housework more comfortable.

[0754] The processing flow will be explained below.

[0755] Step 1:

[0756] Terminal: The user launches the application, which then displays the login screen.

[0757] Step 2:

[0758] User: Enters login information and clicks the Login button.

[0759] Step 3:

[0760] Server: Receives login information and performs user authentication. If authentication is successful, sends the user's housework data entry screen to the terminal.

[0761] Step 4:

[0762] Terminal: Displays the housework data entry screen and allows the user to enter housework data.

[0763] Step 5:

[0764] User: Enters household chore data. For example, the start and end times of washing dishes and the cooking method used.

[0765] Step 6:

[0766] Terminal: The entered data is temporarily saved and sent to the server when the send button is pressed.

[0767] Step 7:

[0768] Terminal: When the send button for housework data is pressed, the data is sent to the server. The data includes the user ID and detailed housework information.

[0769] Step 8:

[0770] Server: Stores the received housework data in a database.

[0771] Step 9:

[0772] Server: Analyzes the user's past housework data based on the data stored in the database. For example, calculates the average time for each housework task and the most frequently used cooking method.

[0773] Step 10:

[0774] Server: Based on the analysis results, the server suggests efficient ways to do housework next time. For example, it generates a suggestion such as, "If you use a fully automatic dishwasher next time, you can reduce the time it takes to wash dishes by 30%."

[0775] Step 11:

[0776] Device: Uses an emotion engine to recognize the user's facial expressions and tone of voice in real time to determine stress levels and satisfaction.

[0777] Step 12:

[0778] Device: Sends emotional data to the server, including the user's stress level, satisfaction level, etc.

[0779] Step 13:

[0780] Server: Receives emotion data and stores it in a database.

[0781] Step 14:

[0782] Server: Combines and analyzes emotional data and housework data, and recalculates efficient ways to perform housework that take the user's emotional state into account.

[0783] Step 15:

[0784] Server: For example, if the user is under high stress, the server will suggest ways to complete housework in a short amount of time or ways to relax, such as listening to music.

[0785] Step 16:

[0786] Server: Sends the generated proposal to the device.

[0787] Step 17:

[0788] Device: Receives the suggestion and notifies the user, which is displayed on the device screen.

[0789] Step 18:

[0790] User: Review the suggestions and decide whether to incorporate them into your next chore.

[0791] Step 19:

[0792] User: When entering a traditional local recipe, the user accesses the recipe entry screen and enters the specific recipe and steps.

[0793] Step 20:

[0794] Terminal: Sends the inputted traditional recipe data to the server.

[0795] Step 21:

[0796] Server: Stores the received data in a database and manages the stored data so that it can be provided to other users.

[0797] The above is the specific processing flow of this system.

[0798] Example 2

[0799] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0800] Conventional housework suggestion systems are limited to suggestions based on user input data and are unable to provide optimal suggestions that reflect the user's emotions and stress levels. This makes it difficult to reduce user stress and provide a comfortable housework environment. Furthermore, there is a lack of suggestions that take into account local traditional methods and the characteristics of individual users.

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

[0802] In this invention, the server includes a means for providing a user interface, a means for inputting data related to housework from a user, a means for transmitting the input data to the server, a means for storing and analyzing the input data in the server, a means for generating efficient housework methods based on the analysis results, a means for notifying the user of the generated efficient housework methods, a means for recognizing the user's emotions, a means for transmitting the recognized emotion data to the server, a means for storing and analyzing the emotion data, and a means for optimizing efficient suggestions based on the emotion data. This enables optimal housework suggestions that take into account the user's emotional state and stress level in real time, thereby reducing the user's stress and providing a comfortable housework environment. Furthermore, suggestions are effectively made that take into account local traditional methods and the characteristics of each individual user.

[0803] A "user interface" is the means by which a user interacts with a system and inputs data.

[0804] "Housework data" refers to information such as the type of housework the user performed, the start time, the end time, and the tools and materials used.

[0805] "Server" means a computing device that receives, stores, and analyzes data submitted by users and generates and provides recommendations to users.

[0806] "Means for recognizing emotions" refers to a set of emotion engines and related hardware and software for determining emotions from the user's facial expressions, tone of voice, etc.

[0807] "Efficient ways to do housework" are methods and means for doing housework more efficiently that are suggested to users based on data analyzed by the server.

[0808] "Emotional data" refers to data collected by the emotion engine regarding the user's emotional state, such as stress level and satisfaction.

[0809] "Means of notification" refers to the method by which the server-generated proposal is communicated to the user, such as a push notification or a screen display in the application.

[0810] "Means to learn and evolve" refers to a method of continuously improving the content of proposals using machine learning algorithms based on collected data.

[0811] "Traditional local methods" refer to the household and cooking methods that have been historically practiced in a particular region, and include the means of storing them in a database.

[0812] The "optimizing means" is a method for analyzing emotional data and generating optimal suggestions corresponding to the user's emotional state.

[0813] This invention relates to a system that provides suggestions to help users perform housework efficiently and comfortably. The system includes the following functions: user interface, data input and transmission, server data storage and analysis, efficient suggestion generation, suggestion notification, and emotion engine.

[0814] Providing a user interface

[0815] The device, like a smartphone or tablet, provides an application and displays a screen where users can input data about their household chores. The interface includes fields for inputting the start and end times of chores, specific chore tasks, and the tools and materials used.

[0816] Data transmission and storage

[0817] The household chore data entered by the user is sent from the device via the internet to a server, which stores the received data in a database for later analysis.

[0818] Analyzing data and generating recommendations

[0819] The server analyzes the stored housework data. Specifically, it evaluates the time required for housework and the efficiency of the tools used, and then suggests how to proceed with the housework more efficiently next time. For example, it analyzes the time it takes to wash dishes and generates a suggestion that using an automatic dishwasher next time can reduce the time by 30%.

[0820] Combining Emotion Engines

[0821] The device is equipped with an emotion engine that analyzes the user's facial expressions and tone of voice, and in combination with hardware such as a camera and microphone, it analyzes the user's stress level and satisfaction in real time.

[0822] Sending and analyzing emotional data

[0823] The device sends the recognized emotion data to a server via the Internet. The server stores this emotion data in a database and analyzes it in conjunction with housework data. For example, it can analyze the user's stress level after doing housework.

[0824] Generating Sentiment-Based Recommendations

[0825] The server uses emotional data to assess the user's stress level and satisfaction level, and adjusts housework suggestions accordingly: if the user is tired, it suggests shorter chores or chores to be done while listening to relaxing music.

[0826] Proposal Notification

[0827] The system's generated suggestions are sent from the server to the device and displayed to the user, who can use these suggestions to perform housework more efficiently and stress-free.

[0828] Specific examples

[0829] For example, if a user wants to wash dishes, they might do the following:

[0830] 1. The user launches the application and logs in.

[0831] 2. The user accesses the housework data entry screen and enters the start time (18:00) and end time (18:15) for washing dishes.

[0832] 3. The device sends this household data to the server.

[0833] 4. The device's emotion engine analyzes the user's facial expressions and tone of voice and determines that the user is tired. This emotion data is also sent to the server.

[0834] 5. The server stores the received data in a database, analyzes it, and generates recommendations such as "Use an automatic dishwasher next time" or "Listen to relaxing music to reduce stress."

[0835] 6. This suggestion is notified to the device and displayed to the user.

[0836] Prompt Sentence Examples

[0837] Please suggest efficient ways to do housework. Specifically, I would like the following information: which chores to do and in what order, efficient tools and methods, and things to be careful about. Also, please suggest what to do if you are feeling stressed.

[0838] This allows the system to make housework more efficient for users, and by making optimal suggestions based on emotions, it reduces stress for users and makes housework more comfortable.

[0839] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0840] Step 1: Displaying the User Interface

[0841] The device launches the application and displays a screen where the user can enter data about the chore. Input fields include the start and end times of the chore, the specific chore, and the tools and materials used, making it easy for the user to enter the required data.

[0842] Input: Launch application

[0843] Output: Display of data entry screen

[0844] How it works: The application invokes the interface and provides the necessary input fields.

[0845] Step 2: Enter household chore data

[0846] The user enters the start and end times of the chore, the chore performed, and the tools and materials used in the input fields. For example, the user enters the start time (18:00) and end time (18:15) of washing dishes, and the detergent used.

[0847] Input: Manually entered data by the user

[0848] Output: Input housework data

[0849] How it works: The user enters the required data into the application's input fields and presses the confirm button.

[0850] Step 3: Sending data

[0851] The device sends the household data entered by the user to a server via the Internet, with encryption protocols such as SSL used to ensure security during transmission.

[0852] Input: Entered housework data

[0853] Output: Send data to the server

[0854] What it does: After the submit button is pressed, the data is sent as an HTTP request to the server.

[0855] Step 4: Save your data

[0856] The server stores the received housework data in a database, including detailed information such as the date and time, the contents of the housework, the time required, and the tools used.

[0857] Input: Submitted housekeeping data

[0858] Output: Housework data stored in a database

[0859] How it works: The server parses the data and stores it in the appropriate tables in the database.

[0860] Step 5: Analyze the data

[0861] The server analyzes the efficiency of housework based on the stored housework data, for example, evaluating the time it took to wash dishes and the efficiency of the tools used, and generates suggestions for the next time.

[0862] Input: Saved housework data

[0863] Output: Analysis results on housework efficiency

[0864] How it works: The server analyzes the data using statistical methods and machine learning models to generate results.

[0865] Step 6: Generate proposals

[0866] The server then makes suggestions based on the analysis results on how to efficiently carry out housework, such as suggesting that you use an automatic dishwasher next time.

[0867] Input: Analysis results on housework efficiency

[0868] Output: Suggestions for efficient housework

[0869] Operation: The server runs the proposal algorithm and stores the generated proposals in a database.

[0870] Step 7: Collect emotion data

[0871] The device uses an emotion engine to analyze the user's facial expressions and tone of voice to collect emotional data, such as measuring the user's stress level and satisfaction in real time using the camera and microphone.

[0872] Input: User's facial expression, tone of voice

[0873] Output: Collected emotion data

[0874] How it works: The emotion engine analyzes the user's video and audio and quantifies their emotional state.

[0875] Step 8: Sending Emotion Data

[0876] The device sends the collected emotion data to a server via the internet, using encryption protocols such as SSL to ensure security.

[0877] Input: Collected emotion data

[0878] Output: Send emotion data to the server

[0879] How it works: After the send button is pressed, the emotion data is sent as an HTTP request to the server.

[0880] Step 9: Store and analyze sentiment data

[0881] The server stores the received emotion data in a database and analyzes it in conjunction with housework data, for example, to analyze the user's stress level after doing housework.

[0882] Input: Emotion data sent

[0883] Output: Emotion data stored in a database, analysis results including emotion data

[0884] Operation: The server stores the emotion data in the appropriate table in the database, integrates it with the household chore data, and performs analysis.

[0885] Step 10: Generate sentiment-based suggestions

[0886] The server optimizes efficient suggestions based on emotion data. If the user is tired, it suggests ways to complete the chore quickly or to do it while listening to relaxing music.

[0887] Input: Analysis results including sentiment data

[0888] Output: Optimized suggestions based on emotions

[0889] Operation: The server runs the proposal algorithm and stores the generated proposals in a database.

[0890] Step 11: Proposal Notification

[0891] The server notifies the device of new suggestions generated and displays them to the user, for example, by using the push notification function of a smartphone.

[0892] Input: Generated proposals

[0893] Output: Proposal notification to device, suggestion display to user

[0894] How it works: The server uses the Notifications API to send suggestions to the device, which then displays them to the user as push notifications.

[0895] Through these processing steps, the system can streamline the user's housework and provide optimal suggestions based on the user's emotional state.

[0896] (Application example 2)

[0897] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0898] In today's world, the use of food delivery services is rapidly increasing, but the user experience is uniform and does not take into account the emotions and circumstances of individual users. This can lead to dissatisfaction and a decrease in repeat customers. It can also cause users to feel stressed and the service to be inefficient. Therefore, there was a need for a system that could use user emotional data to optimize suggestions and provide feedback tailored to individual needs.

[0899] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0900] In this invention, the server includes means for providing a user interface, means for inputting data related to housework from a user, means for transmitting the input data to the server, means for storing and analyzing the input data in the server, means for generating efficient housework methods based on the analysis results, means for notifying the user of the generated efficient housework methods, means for acquiring user emotion data, means for transmitting the acquired emotion data to the server, and means for analyzing the emotion data and optimizing suggestions for housework methods. This enables optimal suggestions based on the emotions of each individual user, thereby improving satisfaction and reuse rates of food delivery services.

[0901] A "user interface" is the part of a system that provides a screen or operating means for a user to input data or view information.

[0902] "Data" refers to recorded content such as information, numbers, text, etc. obtained from a user.

[0903] A "server" is a computer system for storing, analyzing, and processing data.

[0904] "Emotion data" is information indicating the emotional state of the user obtained from facial expressions, voice, etc.

[0905] "Analysis" is the act of generating useful information and suggestions based on collected data using methods such as statistical processing and machine learning.

[0906] "Suggestions" refer to useful information or guidelines for action that the server provides to the user based on the analysis results.

[0907] "Notification" refers to the act of sending the generated suggestion to the user's terminal and displaying it to the user.

[0908] "Optimization" is the act of adjusting a proposal to achieve the most effective and efficient results under specific conditions.

[0909] A "food delivery service" is a service that allows you to order food and drinks and have them delivered to a specified location.

[0910] To implement this invention, a system using a user interface, data acquisition and transmission, data analysis and suggestion generation by a server, and an emotion recognition engine is required.

[0911] composition

[0912] User Interface

[0913] The device provides an interface for users to input data. This interface includes screens where users can enter chore and meal data. Users enter data such as start and end times for chores, meal details, and ingredients used.

[0914] Data transmission and storage

[0915] The device sends the data entered by the user to a server, where it is stored in a database for later analysis.

[0916] Analyzing data and generating recommendations

[0917] The server generates suggestions for efficient housework and food delivery based on the stored data. For example, suggestions for housework include "using the dishwasher to save time" and for food delivery include "listening to relaxing music while doing the job." The server performs analysis using Python and R.

[0918] Emotion Engine

[0919] The device is equipped with an emotion engine that analyzes the user's facial expressions and tone of voice to obtain emotional data, which is also sent to a server and used to optimize suggestions for housework and food delivery.

[0920] Sending and analyzing emotional data

[0921] The device sends the acquired emotional data to the server, which stores this emotional data in a database and analyzes it in conjunction with the household chore data. By using the emotional data, the suggestions are optimized to match the user's emotional state.

[0922] Proposal Notification

[0923] The server then notifies the user of the generated suggestions via push notifications or in-app notifications.

[0924] Hardware and software used

[0925] Hardware:

[0926] Camera (webcam or smartphone camera): To capture the user's facial expressions.

[0927] Smartphone or PC: To provide the user interface and enter data.

[0928] software:

[0929] OpenCV: To capture and preprocess the user's facial expressions.

[0930] TensorFlow / Keras: To run the emotion recognition model.

[0931] Flask or Django: For parsing and saving data server-side.

[0932] Pandas / Numpy: To perform data analysis.

[0933] Requests: To send data.

[0934] Specific examples

[0935] For example, consider a user ordering food delivery. The user does the following:

[0936] 1. Launch the application and log in.

[0937] 2. Access the meal data entry screen and enter the details of the meal you ordered (e.g., pizza, salad) and the delivery time (e.g., 18:30).

[0938] 3. Use the device's camera to capture the user's facial expression.

[0939] The device sends this data and emotional data to a server, which stores and analyzes the data. As a result, it generates suggestions, such as "Next time, serve a relaxing dinner set." These suggestions are then communicated to the user via push notifications or in-app messages.

[0940] Prompt Sentence Examples

[0941] "I'd like to develop a food delivery app, but I'd like to add a feature that suggests meals based on the user's emotions. Specifically, the application should capture and analyze the user's facial expressions and tone of voice, send that data to a server, and notify the server with generated suggestions."

[0942] This will enable optimal proposals tailored to each user's individual circumstances, which is expected to improve satisfaction with the service.

[0943] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0944] Step 1:

[0945] The user launches the application and logs in.

[0946] Input: User credentials (username, password)

[0947] Output: The user is authenticated and redirected to the main page.

[0948] Operation: The terminal sends the authentication information entered by the user to the server, which then checks it against a database to perform the authentication process.

[0949] Step 2:

[0950] The user accesses the meal data entry screen and enters the details of the ordered meal and the delivery time.

[0951] Input: Meal details (e.g., pizza, salad), delivery time (e.g., 18:30)

[0952] Output: The entered meal data is saved on the device and a submit button is displayed.

[0953] How it works: The terminal's user interface provides the user with fields for data entry and a submit button.

[0954] Step 3:

[0955] It uses the device's camera to capture the user's facial expressions.

[0956] Input: An image of the user's face

[0957] Output: Captured face image data

[0958] What it does: The device activates the camera, captures the user's facial expressions, and saves them as image data.

[0959] Step 4:

[0960] Analyze emotions from facial image data.

[0961] Input: Face image data

[0962] Output: Numerical data of the user's emotions (e.g., emotion scores such as happiness, sadness, anger, etc.)

[0963] How it works: The device preprocesses facial images using OpenCV and inputs them into a TensorFlow / Keras model to analyze emotions.

[0964] Step 5:

[0965] The input food data and emotion data are sent to the server.

[0966] Input: Food data, emotion data

[0967] Output: User's food and emotion data stored on the server

[0968] How it works: The device converts this data into JSON format and sends it to the server, which stores it in a database.

[0969] Step 6:

[0970] The server analyzes the received data and generates efficient meal suggestions.

[0971] Input: Stored food and emotion data

[0972] Output: Optimized meal recommendations (e.g., menu recommendations for your next order and time discount information)

[0973] How it works: The server uses Pandas and Numpy to analyze the data and generate recommendations that take into account the user's past order history and emotional state.

[0974] Step 7:

[0975] The suggestions will be sent to the user's device.

[0976] Input: Optimized meal suggestions

[0977] Output: Proposal notification displayed on the user's device

[0978] How it works: The server sends the generated suggestions to the user's device as push notifications or in-app messages.

[0979] This will provide the optimal food delivery experience according to the user's emotional state, and is expected to improve service satisfaction and increase repeat rates.

[0980] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0981] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0982] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0983] [Third embodiment]

[0984] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0985] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0986] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0987] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0988] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0989] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0990] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0991] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0992] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0993] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0994] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0995] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0996] System Overview

[0997] This system provides users with suggestions for efficiently completing household chores while also collecting and storing traditional local recipes. The system includes the following functions: user interface, data input, data transmission, data storage and analysis on the server, efficient suggestion generation, suggestion notification, and storage of traditional recipes.

[0998] Providing a user interface

[0999] The device hosts an application and displays a screen where the user can enter data about the chore, such as the start and end times of the chore, the type of chore performed, and the cooking methods and ingredients used.

[1000] Data transmission and storage

[1001] The household chore data entered by the user is sent from the device to a server, which stores the received data in a database for later analysis.

[1002] Analyzing data and generating recommendations

[1003] The server analyzes the stored data. For example, it analyzes how long it took to wash dishes and which cooking method was most efficient. Based on the analysis results, it runs an algorithm that suggests how to proceed with housework more efficiently next time. For example, it might suggest, "If you have a lot of dishes, using the dishwasher next time will reduce the time by 30%."

[1004] Proposal Notification

[1005] The generated suggestions for efficient housework are sent from the server to the device and displayed to the user, who can use these suggestions to perform housework more efficiently.

[1006] Collecting and preserving traditional recipes

[1007] Users can also input traditional local recipes into the system. For example, they can enter detailed recipes such as "traditional ozōni made using my grandmother's recipe." The input data is sent from the device to the server and saved in a database. The saved traditional recipes can then be shared with other users.

[1008] Specific examples

[1009] For example, consider a user washing dishes. The user does the following:

[1010] 1. Launch the application and log in.

[1011] 2. Access the household data entry screen and enter the start time for washing dishes (18:00).

[1012] 3. When you finish washing the dishes, enter the finish time (18:15).

[1013] 4. Click the Send button to send the data to the server.

[1014] The server receives the data and stores it in a database. The server then analyzes the data and generates suggestions, such as "Next time, use an automatic dishwasher to wash dishes more efficiently." The generated suggestions are sent to the device and displayed to the user.

[1015] Also, if users want to preserve traditional cooking methods,

[1016] 1. Access the traditional recipe entry screen and enter the specific steps and ingredients along with the title "Traditional Ozoni made using my grandmother's recipe."

[1017] 2. Click the submit button to send the data to the server.

[1018] The server stores the data and makes it accessible to other users.

[1019] In this way, the system helps users streamline their household chores while preserving traditional local cooking methods.

[1020] The processing flow will be explained below.

[1021] Step 1:

[1022] Terminal: The user launches the application, which then displays the login screen.

[1023] Step 2:

[1024] User: Enters login information and clicks the Login button.

[1025] Step 3:

[1026] Server: Receives login information and performs user authentication. If authentication is successful, sends the user's housework data entry screen to the terminal.

[1027] Step 4:

[1028] Terminal: Displays the housework data entry screen and allows the user to enter housework data.

[1029] Step 5:

[1030] User: Enters household chore data. For example, the start and end times of washing dishes and the cooking method used.

[1031] Step 6:

[1032] Terminal: The entered data is temporarily saved and sent to the server when the send button is pressed.

[1033] Step 7:

[1034] Terminal: When the send button for housework data is pressed, the data is sent to the server. The data includes the user ID and detailed housework information.

[1035] Step 8:

[1036] Server: Stores the received housework data in a database.

[1037] Step 9:

[1038] Server: Analyzes the user's past housework data based on the data stored in the database. For example, calculates the average time for each housework task and the most frequently used cooking method.

[1039] Step 10:

[1040] Server: Based on the analysis results, the server suggests efficient ways to do housework next time. For example, it generates a suggestion such as, "If you use a fully automatic dishwasher next time, you can reduce the time it takes to wash dishes by 30%."

[1041] Step 11:

[1042] Server: Sends the generated proposal to the device.

[1043] Step 12:

[1044] Device: Receives the suggestion and notifies the user, which is displayed on the device screen.

[1045] Step 13:

[1046] User: Review the suggestions and decide whether to incorporate them into your next chore.

[1047] Step 14:

[1048] User: When entering a traditional local recipe, the user accesses the recipe entry screen and enters the specific recipe and steps.

[1049] Step 15:

[1050] Terminal: Sends the inputted traditional recipe data to the server.

[1051] Step 16:

[1052] Server: Stores the received data in a database and manages the stored data so that it can be provided to other users.

[1053] The above is the specific processing flow of this system.

[1054] Example 1

[1055] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1056] In today's busy lifestyles, it is difficult to provide specific methods for efficiently performing household chores individually. Also, considering the current situation where traditional local cooking methods are being lost, there is a need to efficiently collect and preserve these methods and provide an environment where other users can access them.

[1057] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1058] In this invention, the server includes means for providing a user interface, means for inputting data related to housework from a user, means for transmitting the input data to the server, means for storing and analyzing the input data in the server, means for generating efficient housework methods based on the analysis results, means for notifying the user of the generated efficient housework methods, means for inputting local traditional recipes from a user, means for transmitting the input traditional recipes to the server and storing them in a database, and means for managing the stored traditional recipes so that other users can access them, thereby enabling users to efficiently complete housework while saving and sharing local traditional recipes.

[1059] A "user interface" is a collection of screens and controls that allow a user to interact with a system.

[1060] "Data related to housework" refers to information such as the start and end times of housework, the type of housework performed, and the cooking methods and ingredients used.

[1061] "Server" means a computer system that receives, stores, analyzes data from users, and generates and notifies results.

[1062] A "database" is a system that allows a server to efficiently store and manage data received.

[1063] "Analysis" is the process by which the server analyzes the data it receives and stores, and extracts useful information and suggestions.

[1064] "Efficient ways to do housework" refers to optimal methods and suggestions for doing housework efficiently.

[1065] "Notification" is an action that informs the user of the generated efficient way to perform housework.

[1066] "Traditional cooking methods" are cooking methods that are historically and culturally important and have been passed down in local areas.

[1067] "Storage" refers to the act of recording data and information from users on a server so that it can be retrieved as needed.

[1068] "Management" is the process of organizing stored data and making it quickly accessible when needed.

[1069] "Learning" is the process by which the system uses data collected from users to improve its algorithms to make better suggestions next time.

[1070] "Evolution" refers to the way the suggestions are improved through learning, enabling more advanced advice.

[1071] "Transmission" is the process of transmitting data or information entered by the user from the terminal to the server.

[1072] This invention is a system that provides users with suggestions for efficiently completing housework and collects and stores traditional local recipes. The system mainly consists of the following functions: user interface, data input, data transmission, data storage and analysis on the server, efficient suggestion generation, suggestion notification, and storage of traditional recipes.

[1073] Providing a user interface

[1074] The terminal provides an application and displays a screen for users to enter data about their household chores. The application runs on devices such as smartphones and tablets and can be downloaded from Android or iOS application stores. Through an intuitive interface, users can enter data such as the start and end times of chores, the chore content, and the cooking methods and ingredients used. The interface is built using web technologies such as HTML, CSS, and JavaScript.

[1075] Data transmission and storage

[1076] When a user enters household chore data and clicks the send button, the device sends the data to the server. The communication is secure using the SSL / TLS protocol, and the data is serialized in JSON format. The server then stores the received data in a database such as MySQL or PostgreSQL.

[1077] Analyzing data and generating recommendations

[1078] The stored data is analyzed on the server. Python libraries such as Pandas and NumPy are used to analyze the data. For example, the difference between the start and end times of housework is calculated to determine how long each chore took. Based on the insights gained from the data, a machine learning algorithm is used to suggest efficient ways to perform the chore. The machine learning model used is trained using libraries such as Scikit-learn and TensorFlow.

[1079] Proposal Notification

[1080] The generated suggestions are sent from the server to the device via WebSocket or push notification. The device receives the notification and displays its contents to the user. An example of a notification might include specific advice such as "Next time, use the dishwasher to reduce dishwashing time by 30%."

[1081] Collecting and preserving traditional recipes

[1082] Users can enter traditional local recipes through a dedicated screen within the application. For example, they can enter a title like "Traditional Ozoni made using my grandmother's recipe," along with specific steps and ingredients. The entered data is sent from the device to a server and stored in a database. This recipe data is managed so that other users can search and refer to it.

[1083] Specific examples and examples of input prompts to the generative AI model

[1084] For example, if a user washes dishes:

[1085] 1. The user launches the application and logs in.

[1086] 2. Access the household data entry screen and enter the start time (18:00) and end time (18:15) for washing dishes.

[1087] 3. Click the submit button to send the data to the server.

[1088] 4. The server receives the data and stores it in a database.

[1089] 5. The server analyzes the data and generates a suggestion such as, "Next time, use the fully automatic dishwasher to wash dishes more efficiently."

[1090] 6. The generated suggestions are sent to the device and displayed to the user.

[1091] Also, if users want to save traditional recipes:

[1092] 1. Access the traditional recipe entry screen and enter the specific steps and ingredients along with the title "Traditional Ozoni made using my grandmother's recipe."

[1093] 2. Click the submit button to send the data to the server.

[1094] 3. The server stores the data and manages it so that other users can access it.

[1095] Example of an input prompt for a generative AI model:

[1096] 1. "Generate suggestions for efficient ways to wash dishes. I have data that shows a user washed dishes from 18:00 to 18:15. What can they do next time to save time?"

[1097] 2. "Please provide detailed instructions for making traditional ozoni using my grandmother's recipe. Include ingredients and specific steps."

[1098] This allows users to efficiently carry out household chores while preserving and sharing traditional local cooking methods.

[1099] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1100] Step 1:

[1101] The device runs the application and provides a user interface, which displays a form for entering household data. This interface is built using HTML, CSS, and JavaScript.

[1102] Step 2:

[1103] The user logs in to the application and accesses the housework data input screen. The user enters the start time, end time, the chore performed, and the cooking method and ingredients used. Examples of input data include start time "18:00," end time "18:15," chore "washing dishes," cooking method "hand washing," and ingredient "detergent A."

[1104] Step 3:

[1105] The terminal receives user input, triggers a click event on the "Submit" button, serializes the input data, converts it to JSON format, and sends the converted data to the server. The transmission is performed as an HTTP POST request.

[1106] Step 4:

[1107] The server receives the HTTP request, deserializes the data in JSON format, and stores it in a MySQL or PostgreSQL database. The stored data includes the user ID, start time, end time, chore details, and ingredients used.

[1108] Step 5:

[1109] The server periodically analyzes the stored data, using Python's Pandas and NumPy. The server calculates the difference between the start and end times of each chore to calculate the time it took to complete the chore and determine how long each chore took. It compares the chore data with past data and prepares to generate useful suggestions.

[1110] Step 6:

[1111] The server runs a machine learning algorithm based on the analysis data. The algorithm is trained using libraries such as Scikit-learn and TensorFlow. Based on the analysis results, the server generates efficient housework methods. For example, it generates a suggestion that "using the dishwasher next time will reduce dishwashing time by 30%."

[1112] Step 7:

[1113] The server serializes the generated suggestions into JSON format and sends them to the device via WebSocket or push notification. The notification message includes the specific instructions for completing the generated chores.

[1114] Step 8:

[1115] The device receives the notification and displays the suggestions to the user. The notification can be a push notification or a pop-up within the app. The user can then view the suggestions and use them to help them with their next household chore.

[1116] Step 9:

[1117] Users access a dedicated screen for entering traditional recipes, enter a title (e.g., "Traditional Ozoni made using my grandmother's recipe"), specific steps, and ingredients used, and then click the "Submit" button.

[1118] Step 10:

[1119] The terminal serializes the input traditional recipe data into JSON format and sends it to the server as an HTTP POST request.

[1120] Step 11:

[1121] The server receives traditional recipe data and deserializes it in JSON format, which is then stored in a database. The stored data includes the title, steps, and ingredients.

[1122] Step 12:

[1123] The server manages the stored traditional recipe data so that other users can access it. When a user searches for a recipe, the server retrieves the appropriate data from the database and sends it to the user.

[1124] Examples of input prompts for generative AI models:

[1125] "Generate suggestions on how to wash dishes more efficiently. I have data that shows a user washed dishes from 18:00 to 18:15. What can I do to save time next time?"

[1126] "Please provide detailed instructions for making your grandmother's traditional ozoni recipe, including ingredients and specific steps."

[1127] (Application example 1)

[1128] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1129] There are currently a wide variety of methods and systems to support efficient household chores, but most rely on manual input from users, which can be time-consuming and laborious to obtain efficient suggestions. There is also a lack of methods for passing on traditional local cooking methods to the next generation. Therefore, there is a need for a system that allows for efficient household chores at home and preserves and practices traditional cooking methods.

[1130] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1131] In this invention, the server includes means for providing a user interface, means for inputting data related to housework from a user, means for transmitting the input data to the server, means for storing and analyzing the input data in the server, means for generating efficient ways to perform the housework based on the analysis results, means for notifying the user of the generated efficient ways to perform the housework, means for the household robot to monitor the progress of the housework, and means for the household robot to practice traditional cooking methods. This makes it possible for the household robot to reduce the user's workload and support efficient housework while passing on traditional local cooking methods to the next generation.

[1132] A "user interface" is the means, including a display screen and input devices, by which a user interacts with a system.

[1133] "Housework data" refers to information such as the progress of various household tasks performed by the user, the tools and materials used, and the time required.

[1134] "Means for transmitting input data to a server" refers to a function for transmitting data input by a user to a server via a network.

[1135] "Means for storing and analyzing" refers to the technology that the server uses to store the transmitted data in a database and perform analysis.

[1136] "Means for generating efficient ways of performing housework" refers to an algorithm that generates suggestions for optimizing ways of performing housework based on the analysis results.

[1137] "Means for notifying the user of the generated efficient ways to perform housework" refers to the message and notification functions that inform the user of the suggestions generated by the server.

[1138] "Means for a domestic robot to monitor the progress of household chores" refers to technologies, including sensors and cameras, that monitor the progress of a user's household chores and collect necessary data.

[1139] "Means for domestic robots to practice traditional cooking methods" refers to means including operations and techniques that enable the robot to actually cook the traditional cooking methods collected from users.

[1140] System configuration

[1141] This invention is a system in which a home robot efficiently assists users with household chores and also practices traditional local cooking methods. The system mainly consists of the following components:

[1142] 1. User Interface: This includes the display screen and input devices that allow the user to interact with the system. This is done through the home robot's screen or a smartphone app.

[1143] 2. Data input means: A function that allows users to input data about housework, such as start and end times, tools and materials used, etc.

[1144] 3. Communication method: Network communication function for sending input data to the server. Uses Wi-Fi or internet connection.

[1145] 4. Storage and analysis means: The server stores the transmitted data in a database and analyzes it using database software and analysis algorithms.

[1146] 5. Means for generating efficiency suggestions: Based on the analysis results on the server, an algorithm is run to generate suggestions for optimizing how housework is performed.

[1147] 6. Notification: A function to notify users of efficient housework suggestions generated by the server. This uses the home robot's display and smartphone notification functions.

[1148] 7. Monitoring: The home robot will monitor the user's housework progress and collect necessary data using sensors and cameras.

[1149] 8. Cooking execution means: The function of the household robot to actually cook the traditional recipes collected from the user. This includes software to control the robot arm and cooking equipment.

[1150] Program processing

[1151] The server program is written in Python and is responsible for collecting, storing, analyzing, generating recommendations, and notifying users. It mainly uses the following libraries and technologies:

[1152] requests library: Used to perform network communication.

[1153] datetime module: Used to record the start and end times of chores.

[1154] Database software: Data is stored using MySQL, PostgreSQL, etc.

[1155] Analytical algorithms: Use AI models and machine learning algorithms to generate efficient housekeeping routines.

[1156] Specific examples

[1157] 1. Suggestions for making dishwashing more efficient:

[1158] When a user inputs the start and end times for washing dishes into a home robot, the data is sent to a server, where it is analyzed and a suggestion is made, such as "Use the dishwasher next time to reduce the time by 30%," and this is notified to the home robot or smartphone.

[1159] 2. Practicing traditional cooking methods:

[1160] The user inputs the cooking instructions for "Ozoni (rice soup) made using Grandma's recipe" into the home robot. Based on this data, the robot prepares the ingredients and carries out the specific cooking steps. When the cooking is complete, the user is notified.

[1161] Prompt Sentence Examples

[1162] An example of input to the generative AI model is also set as follows:

[1163] "Write a program for a home robot that records household chore data and generates efficiency suggestions."

[1164] "Tell us a program that preserves traditional cooking methods and lets the robot actually do the cooking."

[1165] This will enable household robots to reduce the workload of users, provide efficient household support, and pass on traditional local cooking methods to the next generation.

[1166] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1167] Step 1:

[1168] The user inputs data about household chores (e.g., start and finish times for washing dishes, tools and ingredients used) through the user interface of the home robot or smartphone app. This input data is entered manually by the user.

[1169] Input: Housework data (start time, end time, tools used)

[1170] Output: Data entry confirmation

[1171] Step 2:

[1172] The device sends the input data to the server via network communication, using Wi-Fi or an internet connection.

[1173] Input: Household data entered by the user

[1174] Output: Send data to the server

[1175] Step 3:

[1176] The server stores the received data in a database, which is then used for analysis. The database software used is MySQL or PostgreSQL.

[1177] Input: Housework data sent from the device

[1178] Output: Saved database entries

[1179] Step 4:

[1180] The server runs an analytical algorithm to analyze the stored data, using machine learning and AI models to obtain information that suggests efficient ways to perform housework.

[1181] Input: Saved housework data

[1182] Output: Analysis results (efficient housework suggestions)

[1183] Step 5:

[1184] The server generates efficient housework suggestions based on the analysis results and notifies the home robot or smartphone of the suggestions using voice synthesis and display functions.

[1185] Input: Analysis results

[1186] Output: Notification of efficient housework suggestions

[1187] Step 6:

[1188] A home robot monitors the user's progress as they perform household chores. The robot uses sensors and cameras to monitor and collect data on users' activities in real time.

[1189] Input: Real-time monitoring data

[1190] Output: Housework progress data

[1191] Step 7:

[1192] The user inputs traditional cooking instructions into the domestic robot, specifically, the cooking steps and ingredients, through a user interface.

[1193] Input: Traditional recipe data (cooking steps, ingredients)

[1194] Output: Registration of traditional recipes

[1195] Step 8:

[1196] The server stores data on traditional recipes in a database, allowing the home robot to perform cooking based on that data.

[1197] Input: Traditional recipe data

[1198] Output: Saved recipe database

[1199] Step 9:

[1200] The domestic robot will actually cook traditional recipes based on an existing database, preparing ingredients and cooking according to the specified steps.

[1201] Input: Saved recipe data

[1202] Output: Finished dish

[1203] Step 10:

[1204] The server records the progress of the home robot as monitoring data and notifies the user of the progress as needed, allowing the user to check the progress of cooking in real time.

[1205] Input: Robot progress data

[1206] Output: Progress notification

[1207] This process flow allows the home robot to reduce the user's workload, provide efficient household support, and pass on traditional local cooking methods to the next generation.

[1208] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1209] System Overview

[1210] This system makes suggestions to help users efficiently complete housework, while recognizing the user's emotions and optimizing the housework suggestions based on those emotions.The system includes the following functions: user interface, data input and transmission, server data storage and analysis, efficient suggestion generation, suggestion notification, and an emotion engine.

[1211] Providing a user interface

[1212] The device hosts an application and displays a screen where the user can enter data about the chore, such as the start and end times of the chore, the type of chore performed, and the cooking methods and ingredients used.

[1213] Data transmission and storage

[1214] The household chore data entered by the user is sent from the device to a server, which stores the received data in a database for later analysis.

[1215] Analyzing data and generating recommendations

[1216] The server analyzes the stored data. For example, it analyzes how long it took to wash dishes and which cooking method was most efficient. Based on the analysis results, it runs an algorithm that suggests how to proceed with housework more efficiently next time. For example, it might suggest, "If you have a lot of dishes, using the dishwasher next time will reduce the time by 30%."

[1217] Combining Emotion Engines

[1218] The device is equipped with an emotion engine that recognizes emotions from the user's facial expressions, tone of voice, etc. This emotion engine analyzes the user's stress level and satisfaction in real time.

[1219] Sending and analyzing emotional data

[1220] The device also sends the recognized emotion data to the server, which stores the emotion data in a database and analyzes it in conjunction with the household chore data.

[1221] Generating Sentiment-Based Recommendations

[1222] The server uses emotional data to determine the user's stress level and satisfaction level, and adjusts housework suggestions accordingly. For example, if the user is feeling stressed, it might suggest ways to complete the chore quickly or suggest listening to relaxing music while doing the chore.

[1223] Proposal Notification

[1224] The generated housework suggestions are sent from the server to the device and displayed to the user, who can use these suggestions to perform housework more efficiently and stress-free.

[1225] Specific examples

[1226] For example, consider a user washing dishes. The user does the following:

[1227] 1. Launch the application and log in.

[1228] 2. Access the household data entry screen and enter the start time for washing dishes (18:00).

[1229] 3. When you finish washing the dishes, enter the finish time (18:15) and click the send button.

[1230] The terminal transmits this household chore data to the server.

[1231] In parallel, the device's emotion engine analyzes the user's facial expressions and tone of voice and determines, for example, that the user is tired. This emotion data is also sent to the server.

[1232] The server stores the received data in a database and analyzes it. As a result, it generates suggestions such as "Use the automatic dishwasher next time" or "Listen to relaxing music to reduce stress." These suggestions are notified to the device and displayed to the user.

[1233] In this way, the system can make housework more efficient for users and, by making optimal suggestions based on emotions, reduce stress for users and make housework more comfortable.

[1234] The processing flow will be explained below.

[1235] Step 1:

[1236] Terminal: The user launches the application, which then displays the login screen.

[1237] Step 2:

[1238] User: Enters login information and clicks the Login button.

[1239] Step 3:

[1240] Server: Receives login information and performs user authentication. If authentication is successful, sends the user's housework data entry screen to the terminal.

[1241] Step 4:

[1242] Terminal: Displays the housework data entry screen and allows the user to enter housework data.

[1243] Step 5:

[1244] User: Enters household chore data. For example, the start and end times of washing dishes and the cooking method used.

[1245] Step 6:

[1246] Terminal: The entered data is temporarily saved and sent to the server when the send button is pressed.

[1247] Step 7:

[1248] Terminal: When the send button for housework data is pressed, the data is sent to the server. The data includes the user ID and detailed housework information.

[1249] Step 8:

[1250] Server: Stores the received housework data in a database.

[1251] Step 9:

[1252] Server: Analyzes the user's past housework data based on the data stored in the database. For example, calculates the average time for each housework task and the most frequently used cooking method.

[1253] Step 10:

[1254] Server: Based on the analysis results, the server suggests efficient ways to do housework next time. For example, it generates a suggestion such as, "If you use a fully automatic dishwasher next time, you can reduce the time it takes to wash dishes by 30%."

[1255] Step 11:

[1256] Device: Uses an emotion engine to recognize the user's facial expressions and tone of voice in real time to determine stress levels and satisfaction.

[1257] Step 12:

[1258] Device: Sends emotional data to the server, including the user's stress level, satisfaction level, etc.

[1259] Step 13:

[1260] Server: Receives emotion data and stores it in a database.

[1261] Step 14:

[1262] Server: Combines and analyzes emotional data and housework data, and recalculates efficient ways to perform housework that take the user's emotional state into account.

[1263] Step 15:

[1264] Server: For example, if the user is under high stress, the server will suggest ways to complete housework in a short amount of time or ways to relax, such as listening to music.

[1265] Step 16:

[1266] Server: Sends the generated proposal to the device.

[1267] Step 17:

[1268] Device: Receives the suggestion and notifies the user, which is displayed on the device screen.

[1269] Step 18:

[1270] User: Review the suggestions and decide whether to incorporate them into your next chore.

[1271] Step 19:

[1272] User: When entering a traditional local recipe, the user accesses the recipe entry screen and enters the specific recipe and steps.

[1273] Step 20:

[1274] Terminal: Sends the inputted traditional recipe data to the server.

[1275] Step 21:

[1276] Server: Stores the received data in a database and manages the stored data so that it can be provided to other users.

[1277] The above is the specific processing flow of this system.

[1278] Example 2

[1279] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1280] Conventional housework suggestion systems are limited to suggestions based on user input data and are unable to provide optimal suggestions that reflect the user's emotions and stress levels. This makes it difficult to reduce user stress and provide a comfortable housework environment. Furthermore, there is a lack of suggestions that take into account local traditional methods and the characteristics of individual users.

[1281] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1282] In this invention, the server includes a means for providing a user interface, a means for inputting data related to housework from a user, a means for transmitting the input data to the server, a means for storing and analyzing the input data in the server, a means for generating efficient housework methods based on the analysis results, a means for notifying the user of the generated efficient housework methods, a means for recognizing the user's emotions, a means for transmitting the recognized emotion data to the server, a means for storing and analyzing the emotion data, and a means for optimizing efficient suggestions based on the emotion data. This enables optimal housework suggestions that take into account the user's emotional state and stress level in real time, thereby reducing the user's stress and providing a comfortable housework environment. Furthermore, suggestions are effectively made that take into account local traditional methods and the characteristics of each individual user.

[1283] A "user interface" is the means by which a user interacts with a system and inputs data.

[1284] "Housework data" refers to information such as the type of housework the user performed, the start time, the end time, and the tools and materials used.

[1285] "Server" means a computing device that receives, stores, and analyzes data submitted by users and generates and provides recommendations to users.

[1286] "Means for recognizing emotions" refers to a set of emotion engines and related hardware and software for determining emotions from the user's facial expressions, tone of voice, etc.

[1287] "Efficient ways to do housework" are methods and means for doing housework more efficiently that are suggested to users based on data analyzed by the server.

[1288] "Emotional data" refers to data collected by the emotion engine regarding the user's emotional state, such as stress level and satisfaction.

[1289] "Means of notification" refers to the method by which the server-generated proposal is communicated to the user, such as a push notification or a screen display in the application.

[1290] "Means to learn and evolve" refers to a method of continuously improving the content of proposals using machine learning algorithms based on collected data.

[1291] "Traditional local methods" refer to the household and cooking methods that have been historically practiced in a particular region, and include the means of storing them in a database.

[1292] The "optimizing means" is a method for analyzing emotional data and generating optimal suggestions corresponding to the user's emotional state.

[1293] This invention relates to a system that provides suggestions to help users perform housework efficiently and comfortably. The system includes the following functions: user interface, data input and transmission, server data storage and analysis, efficient suggestion generation, suggestion notification, and emotion engine.

[1294] Providing a user interface

[1295] The device, like a smartphone or tablet, provides an application and displays a screen where users can input data about their household chores. The interface includes fields for inputting the start and end times of chores, specific chore tasks, and the tools and materials used.

[1296] Data transmission and storage

[1297] The household chore data entered by the user is sent from the device via the internet to a server, which stores the received data in a database for later analysis.

[1298] Analyzing data and generating recommendations

[1299] The server analyzes the stored housework data. Specifically, it evaluates the time required for housework and the efficiency of the tools used, and then suggests how to proceed with the housework more efficiently next time. For example, it analyzes the time it takes to wash dishes and generates a suggestion that using an automatic dishwasher next time can reduce the time by 30%.

[1300] Combining Emotion Engines

[1301] The device is equipped with an emotion engine that analyzes the user's facial expressions and tone of voice, and in combination with hardware such as a camera and microphone, it analyzes the user's stress level and satisfaction in real time.

[1302] Sending and analyzing emotional data

[1303] The device sends the recognized emotion data to a server via the Internet. The server stores this emotion data in a database and analyzes it in conjunction with housework data. For example, it can analyze the user's stress level after doing housework.

[1304] Generating Sentiment-Based Recommendations

[1305] The server uses emotional data to assess the user's stress level and satisfaction level, and adjusts housework suggestions accordingly: if the user is tired, it suggests shorter chores or chores to be done while listening to relaxing music.

[1306] Proposal Notification

[1307] The system's generated suggestions are sent from the server to the device and displayed to the user, who can use these suggestions to perform housework more efficiently and stress-free.

[1308] Specific examples

[1309] For example, if a user wants to wash dishes, they might do the following:

[1310] 1. The user launches the application and logs in.

[1311] 2. The user accesses the housework data entry screen and enters the start time (18:00) and end time (18:15) for washing dishes.

[1312] 3. The device sends this household data to the server.

[1313] 4. The device's emotion engine analyzes the user's facial expressions and tone of voice and determines that the user is tired. This emotion data is also sent to the server.

[1314] 5. The server stores the received data in a database, analyzes it, and generates recommendations such as "Use an automatic dishwasher next time" or "Listen to relaxing music to reduce stress."

[1315] 6. This suggestion is notified to the device and displayed to the user.

[1316] Prompt Sentence Examples

[1317] Please suggest efficient ways to do housework. Specifically, I would like the following information: which chores to do and in what order, efficient tools and methods, and things to be careful about. Also, please suggest what to do if you are feeling stressed.

[1318] This allows the system to make housework more efficient for users, and by making optimal suggestions based on emotions, it reduces stress for users and makes housework more comfortable.

[1319] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1320] Step 1: Displaying the User Interface

[1321] The device launches the application and displays a screen where the user can enter data about the chore. Input fields include the start and end times of the chore, the specific chore, and the tools and materials used, making it easy for the user to enter the required data.

[1322] Input: Launch application

[1323] Output: Display of data entry screen

[1324] How it works: The application invokes the interface and provides the necessary input fields.

[1325] Step 2: Enter household chore data

[1326] The user enters the start and end times of the chore, the chore performed, and the tools and materials used in the input fields. For example, the user enters the start time (18:00) and end time (18:15) of washing dishes, and the detergent used.

[1327] Input: Manually entered data by the user

[1328] Output: Input housework data

[1329] How it works: The user enters the required data into the application's input fields and presses the confirm button.

[1330] Step 3: Sending data

[1331] The device sends the household data entered by the user to a server via the Internet, with encryption protocols such as SSL used to ensure security during transmission.

[1332] Input: Entered housework data

[1333] Output: Send data to the server

[1334] What it does: After the submit button is pressed, the data is sent as an HTTP request to the server.

[1335] Step 4: Save your data

[1336] The server stores the received housework data in a database, including detailed information such as the date and time, the contents of the housework, the time required, and the tools used.

[1337] Input: Submitted housekeeping data

[1338] Output: Housework data stored in a database

[1339] How it works: The server parses the data and stores it in the appropriate tables in the database.

[1340] Step 5: Analyze the data

[1341] The server analyzes the efficiency of housework based on the stored housework data, for example, evaluating the time it took to wash dishes and the efficiency of the tools used, and generates suggestions for the next time.

[1342] Input: Saved housework data

[1343] Output: Analysis results on housework efficiency

[1344] How it works: The server analyzes the data using statistical methods and machine learning models to generate results.

[1345] Step 6: Generate proposals

[1346] The server then makes suggestions based on the analysis results on how to efficiently carry out housework, such as suggesting that you use an automatic dishwasher next time.

[1347] Input: Analysis results on housework efficiency

[1348] Output: Suggestions for efficient housework

[1349] Operation: The server runs the proposal algorithm and stores the generated proposals in a database.

[1350] Step 7: Collect emotion data

[1351] The device uses an emotion engine to analyze the user's facial expressions and tone of voice to collect emotional data, such as measuring the user's stress level and satisfaction in real time using the camera and microphone.

[1352] Input: User's facial expression, tone of voice

[1353] Output: Collected emotion data

[1354] How it works: The emotion engine analyzes the user's video and audio and quantifies their emotional state.

[1355] Step 8: Sending Emotion Data

[1356] The device sends the collected emotion data to a server via the internet, using encryption protocols such as SSL to ensure security.

[1357] Input: Collected emotion data

[1358] Output: Send emotion data to the server

[1359] How it works: After the send button is pressed, the emotion data is sent as an HTTP request to the server.

[1360] Step 9: Store and analyze sentiment data

[1361] The server stores the received emotion data in a database and analyzes it in conjunction with housework data, for example, to analyze the user's stress level after doing housework.

[1362] Input: Emotion data sent

[1363] Output: Emotion data stored in a database, analysis results including emotion data

[1364] Operation: The server stores the emotion data in the appropriate table in the database, integrates it with the household chore data, and performs analysis.

[1365] Step 10: Generate sentiment-based suggestions

[1366] The server optimizes efficient suggestions based on emotion data. If the user is tired, it suggests ways to complete the chore quickly or to do it while listening to relaxing music.

[1367] Input: Analysis results including sentiment data

[1368] Output: Optimized suggestions based on emotions

[1369] Operation: The server runs the proposal algorithm and stores the generated proposals in a database.

[1370] Step 11: Proposal Notification

[1371] The server notifies the device of new suggestions generated and displays them to the user, for example, by using the push notification function of a smartphone.

[1372] Input: Generated proposals

[1373] Output: Proposal notification to device, suggestion display to user

[1374] How it works: The server uses the Notifications API to send suggestions to the device, which then displays them to the user as push notifications.

[1375] Through these processing steps, the system can streamline the user's housework and provide optimal suggestions based on the user's emotional state.

[1376] (Application example 2)

[1377] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1378] In today's world, the use of food delivery services is rapidly increasing, but the user experience is uniform and does not take into account the emotions and circumstances of individual users. This can lead to dissatisfaction and a decrease in repeat customers. It can also cause users to feel stressed and the service to be inefficient. Therefore, there was a need for a system that could use user emotional data to optimize suggestions and provide feedback tailored to individual needs.

[1379] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1380] In this invention, the server includes means for providing a user interface, means for inputting data related to housework from a user, means for transmitting the input data to the server, means for storing and analyzing the input data in the server, means for generating efficient housework methods based on the analysis results, means for notifying the user of the generated efficient housework methods, means for acquiring user emotion data, means for transmitting the acquired emotion data to the server, and means for analyzing the emotion data and optimizing suggestions for housework methods. This enables optimal suggestions based on the emotions of each individual user, thereby improving satisfaction and reuse rates of food delivery services.

[1381] A "user interface" is the part of a system that provides a screen or operating means for a user to input data or view information.

[1382] "Data" refers to recorded content such as information, numbers, text, etc. obtained from a user.

[1383] A "server" is a computer system for storing, analyzing, and processing data.

[1384] "Emotion data" is information indicating the emotional state of the user obtained from facial expressions, voice, etc.

[1385] "Analysis" is the act of generating useful information and suggestions based on collected data using methods such as statistical processing and machine learning.

[1386] "Suggestions" refer to useful information or guidelines for action that the server provides to the user based on the analysis results.

[1387] "Notification" refers to the act of sending the generated suggestion to the user's terminal and displaying it to the user.

[1388] "Optimization" is the act of adjusting a proposal to achieve the most effective and efficient results under specific conditions.

[1389] A "food delivery service" is a service that allows you to order food and drinks and have them delivered to a specified location.

[1390] To implement this invention, a system using a user interface, data acquisition and transmission, data analysis and suggestion generation by a server, and an emotion recognition engine is required.

[1391] composition

[1392] User Interface

[1393] The device provides an interface for users to input data. This interface includes screens where users can enter chore and meal data. Users enter data such as start and end times for chores, meal details, and ingredients used.

[1394] Data transmission and storage

[1395] The device sends the data entered by the user to a server, where it is stored in a database for later analysis.

[1396] Analyzing data and generating recommendations

[1397] The server generates suggestions for efficient housework and food delivery based on the stored data. For example, suggestions for housework include "using the dishwasher to save time" and for food delivery include "listening to relaxing music while doing the job." The server performs analysis using Python and R.

[1398] Emotion Engine

[1399] The device is equipped with an emotion engine that analyzes the user's facial expressions and tone of voice to obtain emotional data, which is also sent to a server and used to optimize suggestions for housework and food delivery.

[1400] Sending and analyzing emotional data

[1401] The device sends the acquired emotional data to the server, which stores this emotional data in a database and analyzes it in conjunction with the household chore data. By using the emotional data, the suggestions are optimized to match the user's emotional state.

[1402] Proposal Notification

[1403] The server then notifies the user of the generated suggestions via push notifications or in-app notifications.

[1404] Hardware and software used

[1405] Hardware:

[1406] Camera (webcam or smartphone camera): To capture the user's facial expressions.

[1407] Smartphone or PC: To provide the user interface and enter data.

[1408] software:

[1409] OpenCV: To capture and preprocess the user's facial expressions.

[1410] TensorFlow / Keras: To run the emotion recognition model.

[1411] Flask or Django: For parsing and saving data server-side.

[1412] Pandas / Numpy: To perform data analysis.

[1413] Requests: To send data.

[1414] Specific examples

[1415] For example, consider a user ordering food delivery. The user does the following:

[1416] 1. Launch the application and log in.

[1417] 2. Access the meal data entry screen and enter the details of the meal you ordered (e.g., pizza, salad) and the delivery time (e.g., 18:30).

[1418] 3. Use the device's camera to capture the user's facial expression.

[1419] The device sends this data and emotional data to a server, which stores and analyzes the data. As a result, it generates suggestions, such as "Next time, serve a relaxing dinner set." These suggestions are then communicated to the user via push notifications or in-app messages.

[1420] Prompt Sentence Examples

[1421] "I'd like to develop a food delivery app, but I'd like to add a feature that suggests meals based on the user's emotions. Specifically, the application should capture and analyze the user's facial expressions and tone of voice, send that data to a server, and notify the server with generated suggestions."

[1422] This will enable optimal proposals tailored to each user's individual circumstances, which is expected to improve satisfaction with the service.

[1423] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1424] Step 1:

[1425] The user launches the application and logs in.

[1426] Input: User credentials (username, password)

[1427] Output: The user is authenticated and redirected to the main page.

[1428] Operation: The terminal sends the authentication information entered by the user to the server, which then checks it against a database to perform the authentication process.

[1429] Step 2:

[1430] The user accesses the meal data entry screen and enters the details of the ordered meal and the delivery time.

[1431] Input: Meal details (e.g., pizza, salad), delivery time (e.g., 18:30)

[1432] Output: The entered meal data is saved on the device and a submit button is displayed.

[1433] How it works: The terminal's user interface provides the user with fields for data entry and a submit button.

[1434] Step 3:

[1435] It uses the device's camera to capture the user's facial expressions.

[1436] Input: An image of the user's face

[1437] Output: Captured face image data

[1438] What it does: The device activates the camera, captures the user's facial expressions, and saves them as image data.

[1439] Step 4:

[1440] Analyze emotions from facial image data.

[1441] Input: Face image data

[1442] Output: Numerical data of the user's emotions (e.g., emotion scores such as happiness, sadness, anger, etc.)

[1443] How it works: The device preprocesses facial images using OpenCV and inputs them into a TensorFlow / Keras model to analyze emotions.

[1444] Step 5:

[1445] The input food data and emotion data are sent to the server.

[1446] Input: Food data, emotion data

[1447] Output: User's food and emotion data stored on the server

[1448] How it works: The device converts this data into JSON format and sends it to the server, which stores it in a database.

[1449] Step 6:

[1450] The server analyzes the received data and generates efficient meal suggestions.

[1451] Input: Stored food and emotion data

[1452] Output: Optimized meal recommendations (e.g., menu recommendations for your next order and time discount information)

[1453] How it works: The server uses Pandas and Numpy to analyze the data and generate recommendations that take into account the user's past order history and emotional state.

[1454] Step 7:

[1455] The suggestions will be sent to the user's device.

[1456] Input: Optimized meal suggestions

[1457] Output: Proposal notification displayed on the user's device

[1458] How it works: The server sends the generated suggestions to the user's device as push notifications or in-app messages.

[1459] This will provide the optimal food delivery experience according to the user's emotional state, and is expected to improve service satisfaction and increase repeat rates.

[1460] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1461] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1462] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1463] [Fourth embodiment]

[1464] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1465] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1466] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1467] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1468] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1469] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1470] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1471] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1472] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1473] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1474] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1475] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1476] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1477] System Overview

[1478] This system provides users with suggestions for efficiently completing household chores while also collecting and storing traditional local recipes. The system includes the following functions: user interface, data input, data transmission, data storage and analysis on the server, efficient suggestion generation, suggestion notification, and storage of traditional recipes.

[1479] Providing a user interface

[1480] The device hosts an application and displays a screen where the user can enter data about the chore, such as the start and end times of the chore, the type of chore performed, and the cooking methods and ingredients used.

[1481] Data transmission and storage

[1482] The household chore data entered by the user is sent from the device to a server, which stores the received data in a database for later analysis.

[1483] Analyzing data and generating recommendations

[1484] The server analyzes the stored data. For example, it analyzes how long it took to wash dishes and which cooking method was most efficient. Based on the analysis results, it runs an algorithm that suggests how to proceed with housework more efficiently next time. For example, it might suggest, "If you have a lot of dishes, using the dishwasher next time will reduce the time by 30%."

[1485] Proposal Notification

[1486] The generated suggestions for efficient housework are sent from the server to the device and displayed to the user, who can use these suggestions to perform housework more efficiently.

[1487] Collecting and preserving traditional recipes

[1488] Users can also input traditional local recipes into the system. For example, they can enter detailed recipes such as "traditional ozōni made using my grandmother's recipe." The input data is sent from the device to the server and saved in a database. The saved traditional recipes can then be shared with other users.

[1489] Specific examples

[1490] For example, consider a user washing dishes. The user does the following:

[1491] 1. Launch the application and log in.

[1492] 2. Access the household data entry screen and enter the start time for washing dishes (18:00).

[1493] 3. When you finish washing the dishes, enter the finish time (18:15).

[1494] 4. Click the Send button to send the data to the server.

[1495] The server receives the data and stores it in a database. The server then analyzes the data and generates suggestions, such as "Next time, use an automatic dishwasher to wash dishes more efficiently." The generated suggestions are sent to the device and displayed to the user.

[1496] Also, if users want to preserve traditional cooking methods,

[1497] 1. Access the traditional recipe entry screen and enter the specific steps and ingredients along with the title "Traditional Ozoni made using my grandmother's recipe."

[1498] 2. Click the submit button to send the data to the server.

[1499] The server stores the data and makes it accessible to other users.

[1500] In this way, the system helps users streamline their household chores while preserving traditional local cooking methods.

[1501] The processing flow will be explained below.

[1502] Step 1:

[1503] Terminal: The user launches the application, which then displays the login screen.

[1504] Step 2:

[1505] User: Enters login information and clicks the Login button.

[1506] Step 3:

[1507] Server: Receives login information and performs user authentication. If authentication is successful, sends the user's housework data entry screen to the terminal.

[1508] Step 4:

[1509] Terminal: Displays the housework data entry screen and allows the user to enter housework data.

[1510] Step 5:

[1511] User: Enters household chore data. For example, the start and end times of washing dishes and the cooking method used.

[1512] Step 6:

[1513] Terminal: The entered data is temporarily saved and sent to the server when the send button is pressed.

[1514] Step 7:

[1515] Terminal: When the send button for housework data is pressed, the data is sent to the server. The data includes the user ID and detailed housework information.

[1516] Step 8:

[1517] Server: Stores the received housework data in a database.

[1518] Step 9:

[1519] Server: Analyzes the user's past housework data based on the data stored in the database. For example, calculates the average time for each housework task and the most frequently used cooking method.

[1520] Step 10:

[1521] Server: Based on the analysis results, the server suggests efficient ways to do housework next time. For example, it generates a suggestion such as, "If you use a fully automatic dishwasher next time, you can reduce the time it takes to wash dishes by 30%."

[1522] Step 11:

[1523] Server: Sends the generated proposal to the device.

[1524] Step 12:

[1525] Device: Receives the suggestion and notifies the user, which is displayed on the device screen.

[1526] Step 13:

[1527] User: Review the suggestions and decide whether to incorporate them into your next chore.

[1528] Step 14:

[1529] User: When entering a traditional local recipe, the user accesses the recipe entry screen and enters the specific recipe and steps.

[1530] Step 15:

[1531] Terminal: Sends the inputted traditional recipe data to the server.

[1532] Step 16:

[1533] Server: Stores the received data in a database and manages the stored data so that it can be provided to other users.

[1534] The above is the specific processing flow of this system.

[1535] Example 1

[1536] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1537] In today's busy lifestyles, it is difficult to provide specific methods for efficiently performing household chores individually. Also, considering the current situation where traditional local cooking methods are being lost, there is a need to efficiently collect and preserve these methods and provide an environment where other users can access them.

[1538] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1539] In this invention, the server includes means for providing a user interface, means for inputting data related to housework from a user, means for transmitting the input data to the server, means for storing and analyzing the input data in the server, means for generating efficient housework methods based on the analysis results, means for notifying the user of the generated efficient housework methods, means for inputting local traditional recipes from a user, means for transmitting the input traditional recipes to the server and storing them in a database, and means for managing the stored traditional recipes so that other users can access them, thereby enabling users to efficiently complete housework while saving and sharing local traditional recipes.

[1540] A "user interface" is a collection of screens and controls that allow a user to interact with a system.

[1541] "Data related to housework" refers to information such as the start and end times of housework, the type of housework performed, and the cooking methods and ingredients used.

[1542] "Server" means a computer system that receives, stores, analyzes data from users, and generates and notifies results.

[1543] A "database" is a system that allows a server to efficiently store and manage data received.

[1544] "Analysis" is the process by which the server analyzes the data it receives and stores, and extracts useful information and suggestions.

[1545] "Efficient ways to do housework" refers to optimal methods and suggestions for doing housework efficiently.

[1546] "Notification" is an action that informs the user of the generated efficient way to perform housework.

[1547] "Traditional cooking methods" are cooking methods that are historically and culturally important and have been passed down in local areas.

[1548] "Storage" refers to the act of recording data and information from users on a server so that it can be retrieved as needed.

[1549] "Management" is the process of organizing stored data and making it quickly accessible when needed.

[1550] "Learning" is the process by which the system uses data collected from users to improve its algorithms to make better suggestions next time.

[1551] "Evolution" refers to the way the suggestions are improved through learning, enabling more advanced advice.

[1552] "Transmission" is the process of transmitting data or information entered by the user from the terminal to the server.

[1553] This invention is a system that provides users with suggestions for efficiently completing housework and collects and stores traditional local recipes. The system mainly consists of the following functions: user interface, data input, data transmission, data storage and analysis on the server, efficient suggestion generation, suggestion notification, and storage of traditional recipes.

[1554] Providing a user interface

[1555] The terminal provides an application and displays a screen for users to enter data about their household chores. The application runs on devices such as smartphones and tablets and can be downloaded from Android or iOS application stores. Through an intuitive interface, users can enter data such as the start and end times of chores, the chore content, and the cooking methods and ingredients used. The interface is built using web technologies such as HTML, CSS, and JavaScript.

[1556] Data transmission and storage

[1557] When a user enters household chore data and clicks the send button, the device sends the data to the server. The communication is secure using the SSL / TLS protocol, and the data is serialized in JSON format. The server then stores the received data in a database such as MySQL or PostgreSQL.

[1558] Analyzing data and generating recommendations

[1559] The stored data is analyzed on the server. Python libraries such as Pandas and NumPy are used to analyze the data. For example, the difference between the start and end times of housework is calculated to determine how long each chore took. Based on the insights gained from the data, a machine learning algorithm is used to suggest efficient ways to perform the chore. The machine learning model used is trained using libraries such as Scikit-learn and TensorFlow.

[1560] Proposal Notification

[1561] The generated suggestions are sent from the server to the device via WebSocket or push notification. The device receives the notification and displays its contents to the user. An example of a notification might include specific advice such as "Next time, use the dishwasher to reduce dishwashing time by 30%."

[1562] Collecting and preserving traditional recipes

[1563] Users can enter traditional local recipes through a dedicated screen within the application. For example, they can enter a title like "Traditional Ozoni made using my grandmother's recipe," along with specific steps and ingredients. The entered data is sent from the device to a server and stored in a database. This recipe data is managed so that other users can search and refer to it.

[1564] Specific examples and examples of input prompts to the generative AI model

[1565] For example, if a user washes dishes:

[1566] 1. The user launches the application and logs in.

[1567] 2. Access the household data entry screen and enter the start time (18:00) and end time (18:15) for washing dishes.

[1568] 3. Click the submit button to send the data to the server.

[1569] 4. The server receives the data and stores it in a database.

[1570] 5. The server analyzes the data and generates a suggestion such as, "Next time, use the fully automatic dishwasher to wash dishes more efficiently."

[1571] 6. The generated suggestions are sent to the device and displayed to the user.

[1572] Also, if users want to save traditional recipes:

[1573] 1. Access the traditional recipe entry screen and enter the specific steps and ingredients along with the title "Traditional Ozoni made using my grandmother's recipe."

[1574] 2. Click the submit button to send the data to the server.

[1575] 3. The server stores the data and manages it so that other users can access it.

[1576] Example of an input prompt for a generative AI model:

[1577] 1. "Generate suggestions for efficient ways to wash dishes. I have data that shows a user washed dishes from 18:00 to 18:15. What can they do next time to save time?"

[1578] 2. "Please provide detailed instructions for making traditional ozoni using my grandmother's recipe. Include ingredients and specific steps."

[1579] This allows users to efficiently carry out household chores while preserving and sharing traditional local cooking methods.

[1580] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1581] Step 1:

[1582] The device runs the application and provides a user interface, which displays a form for entering household data. This interface is built using HTML, CSS, and JavaScript.

[1583] Step 2:

[1584] The user logs in to the application and accesses the housework data input screen. The user enters the start time, end time, the chore performed, and the cooking method and ingredients used. Examples of input data include start time "18:00," end time "18:15," chore "washing dishes," cooking method "hand washing," and ingredient "detergent A."

[1585] Step 3:

[1586] The terminal receives user input, triggers a click event on the "Submit" button, serializes the input data, converts it to JSON format, and sends the converted data to the server. The transmission is performed as an HTTP POST request.

[1587] Step 4:

[1588] The server receives the HTTP request, deserializes the data in JSON format, and stores it in a MySQL or PostgreSQL database. The stored data includes the user ID, start time, end time, chore details, and ingredients used.

[1589] Step 5:

[1590] The server periodically analyzes the stored data, using Python's Pandas and NumPy. The server calculates the difference between the start and end times of each chore to calculate the time it took to complete the chore and determine how long each chore took. It compares the chore data with past data and prepares to generate useful suggestions.

[1591] Step 6:

[1592] The server runs a machine learning algorithm based on the analysis data. The algorithm is trained using libraries such as Scikit-learn and TensorFlow. Based on the analysis results, the server generates efficient housework methods. For example, it generates a suggestion that "using the dishwasher next time will reduce dishwashing time by 30%."

[1593] Step 7:

[1594] The server serializes the generated suggestions into JSON format and sends them to the device via WebSocket or push notification. The notification message includes the specific instructions for completing the generated chores.

[1595] Step 8:

[1596] The device receives the notification and displays the suggestions to the user. The notification can be a push notification or a pop-up within the app. The user can then view the suggestions and use them to help them with their next household chore.

[1597] Step 9:

[1598] Users access a dedicated screen for entering traditional recipes, enter a title (e.g., "Traditional Ozoni made using my grandmother's recipe"), specific steps, and ingredients used, and then click the "Submit" button.

[1599] Step 10:

[1600] The terminal serializes the input traditional recipe data into JSON format and sends it to the server as an HTTP POST request.

[1601] Step 11:

[1602] The server receives traditional recipe data and deserializes it in JSON format, which is then stored in a database. The stored data includes the title, steps, and ingredients.

[1603] Step 12:

[1604] The server manages the stored traditional recipe data so that other users can access it. When a user searches for a recipe, the server retrieves the appropriate data from the database and sends it to the user.

[1605] Examples of input prompts for generative AI models:

[1606] "Generate suggestions on how to wash dishes more efficiently. I have data that shows a user washed dishes from 18:00 to 18:15. What can I do to save time next time?"

[1607] "Please provide detailed instructions for making your grandmother's traditional ozoni recipe, including ingredients and specific steps."

[1608] (Application example 1)

[1609] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1610] There are currently a wide variety of methods and systems to support efficient household chores, but most rely on manual input from users, which can be time-consuming and laborious to obtain efficient suggestions. There is also a lack of methods for passing on traditional local cooking methods to the next generation. Therefore, there is a need for a system that allows for efficient household chores at home and preserves and practices traditional cooking methods.

[1611] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1612] In this invention, the server includes means for providing a user interface, means for inputting data related to housework from a user, means for transmitting the input data to the server, means for storing and analyzing the input data in the server, means for generating efficient ways to perform the housework based on the analysis results, means for notifying the user of the generated efficient ways to perform the housework, means for the household robot to monitor the progress of the housework, and means for the household robot to practice traditional cooking methods. This makes it possible for the household robot to reduce the user's workload and support efficient housework while passing on traditional local cooking methods to the next generation.

[1613] A "user interface" is the means, including a display screen and input devices, by which a user interacts with a system.

[1614] "Housework data" refers to information such as the progress of various household tasks performed by the user, the tools and materials used, and the time required.

[1615] "Means for transmitting input data to a server" refers to a function for transmitting data input by a user to a server via a network.

[1616] "Means for storing and analyzing" refers to the technology that the server uses to store the transmitted data in a database and perform analysis.

[1617] "Means for generating efficient ways of performing housework" refers to an algorithm that generates suggestions for optimizing ways of performing housework based on the analysis results.

[1618] "Means for notifying the user of the generated efficient ways to perform housework" refers to the message and notification functions that inform the user of the suggestions generated by the server.

[1619] "Means for a domestic robot to monitor the progress of household chores" refers to technologies, including sensors and cameras, that monitor the progress of a user's household chores and collect necessary data.

[1620] "Means for domestic robots to practice traditional cooking methods" refers to means including operations and techniques that enable the robot to actually cook the traditional cooking methods collected from users.

[1621] System configuration

[1622] This invention is a system in which a home robot efficiently assists users with household chores and also practices traditional local cooking methods. The system mainly consists of the following components:

[1623] 1. User Interface: This includes the display screen and input devices that allow the user to interact with the system. This is done through the home robot's screen or a smartphone app.

[1624] 2. Data input means: A function that allows users to input data about housework, such as start and end times, tools and materials used, etc.

[1625] 3. Communication method: Network communication function for sending input data to the server. Uses Wi-Fi or internet connection.

[1626] 4. Storage and analysis means: The server stores the transmitted data in a database and analyzes it using database software and analysis algorithms.

[1627] 5. Means for generating efficiency suggestions: Based on the analysis results on the server, an algorithm is run to generate suggestions for optimizing how housework is performed.

[1628] 6. Notification: A function to notify users of efficient housework suggestions generated by the server. This uses the home robot's display and smartphone notification functions.

[1629] 7. Monitoring: The home robot will monitor the user's housework progress and collect necessary data using sensors and cameras.

[1630] 8. Cooking execution means: The function of the household robot to actually cook the traditional recipes collected from the user. This includes software to control the robot arm and cooking equipment.

[1631] Program processing

[1632] The server program is written in Python and is responsible for collecting, storing, analyzing, generating recommendations, and notifying users. It mainly uses the following libraries and technologies:

[1633] requests library: Used to perform network communication.

[1634] datetime module: Used to record the start and end times of chores.

[1635] Database software: Data is stored using MySQL, PostgreSQL, etc.

[1636] Analytical algorithms: Use AI models and machine learning algorithms to generate efficient housekeeping routines.

[1637] Specific examples

[1638] 1. Suggestions for making dishwashing more efficient:

[1639] When a user inputs the start and end times for washing dishes into a home robot, the data is sent to a server, where it is analyzed and a suggestion is made, such as "Use the dishwasher next time to reduce the time by 30%," and this is notified to the home robot or smartphone.

[1640] 2. Practicing traditional cooking methods:

[1641] The user inputs the cooking instructions for "Ozoni (rice soup) made using Grandma's recipe" into the home robot. Based on this data, the robot prepares the ingredients and carries out the specific cooking steps. When the cooking is complete, the user is notified.

[1642] Prompt Sentence Examples

[1643] An example of input to the generative AI model is also set as follows:

[1644] "Write a program for a home robot that records household chore data and generates efficiency suggestions."

[1645] "Tell us a program that preserves traditional cooking methods and lets the robot actually do the cooking."

[1646] This will enable household robots to reduce the workload of users, provide efficient household support, and pass on traditional local cooking methods to the next generation.

[1647] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1648] Step 1:

[1649] The user inputs data about household chores (e.g., start and finish times for washing dishes, tools and ingredients used) through the user interface of the home robot or smartphone app. This input data is entered manually by the user.

[1650] Input: Housework data (start time, end time, tools used)

[1651] Output: Data entry confirmation

[1652] Step 2:

[1653] The device sends the input data to the server via network communication, using Wi-Fi or an internet connection.

[1654] Input: Household data entered by the user

[1655] Output: Send data to the server

[1656] Step 3:

[1657] The server stores the received data in a database, which is then used for analysis. The database software used is MySQL or PostgreSQL.

[1658] Input: Housework data sent from the device

[1659] Output: Saved database entries

[1660] Step 4:

[1661] The server runs an analytical algorithm to analyze the stored data, using machine learning and AI models to obtain information that suggests efficient ways to perform housework.

[1662] Input: Saved housework data

[1663] Output: Analysis results (efficient housework suggestions)

[1664] Step 5:

[1665] The server generates efficient housework suggestions based on the analysis results and notifies the home robot or smartphone of the suggestions using voice synthesis and display functions.

[1666] Input: Analysis results

[1667] Output: Notification of efficient housework suggestions

[1668] Step 6:

[1669] A home robot monitors the user's progress as they perform household chores. The robot uses sensors and cameras to monitor and collect data on users' activities in real time.

[1670] Input: Real-time monitoring data

[1671] Output: Housework progress data

[1672] Step 7:

[1673] The user inputs traditional cooking instructions into the domestic robot, specifically, the cooking steps and ingredients, through a user interface.

[1674] Input: Traditional recipe data (cooking steps, ingredients)

[1675] Output: Registration of traditional recipes

[1676] Step 8:

[1677] The server stores data on traditional recipes in a database, allowing the home robot to perform cooking based on that data.

[1678] Input: Traditional recipe data

[1679] Output: Saved recipe database

[1680] Step 9:

[1681] The domestic robot will actually cook traditional recipes based on an existing database, preparing ingredients and cooking according to the specified steps.

[1682] Input: Saved recipe data

[1683] Output: Finished dish

[1684] Step 10:

[1685] The server records the progress of the home robot as monitoring data and notifies the user of the progress as needed, allowing the user to check the progress of cooking in real time.

[1686] Input: Robot progress data

[1687] Output: Progress notification

[1688] This process flow allows the home robot to reduce the user's workload, provide efficient household support, and pass on traditional local cooking methods to the next generation.

[1689] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1690] System Overview

[1691] This system makes suggestions to help users efficiently complete housework, while recognizing the user's emotions and optimizing the housework suggestions based on those emotions.The system includes the following functions: user interface, data input and transmission, server data storage and analysis, efficient suggestion generation, suggestion notification, and an emotion engine.

[1692] Providing a user interface

[1693] The device hosts an application and displays a screen where the user can enter data about the chore, such as the start and end times of the chore, the type of chore performed, and the cooking methods and ingredients used.

[1694] Data transmission and storage

[1695] The household chore data entered by the user is sent from the device to a server, which stores the received data in a database for later analysis.

[1696] Analyzing data and generating recommendations

[1697] The server analyzes the stored data. For example, it analyzes how long it took to wash dishes and which cooking method was most efficient. Based on the analysis results, it runs an algorithm that suggests how to proceed with housework more efficiently next time. For example, it might suggest, "If you have a lot of dishes, using the dishwasher next time will reduce the time by 30%."

[1698] Combining Emotion Engines

[1699] The device is equipped with an emotion engine that recognizes emotions from the user's facial expressions, tone of voice, etc. This emotion engine analyzes the user's stress level and satisfaction in real time.

[1700] Sending and analyzing emotional data

[1701] The device also sends the recognized emotion data to the server, which stores the emotion data in a database and analyzes it in conjunction with the household chore data.

[1702] Generating Sentiment-Based Recommendations

[1703] The server uses emotional data to determine the user's stress level and satisfaction level, and adjusts housework suggestions accordingly. For example, if the user is feeling stressed, it might suggest ways to complete the chore quickly or suggest listening to relaxing music while doing the chore.

[1704] Proposal Notification

[1705] The generated housework suggestions are sent from the server to the device and displayed to the user, who can use these suggestions to perform housework more efficiently and stress-free.

[1706] Specific examples

[1707] For example, consider a user washing dishes. The user does the following:

[1708] 1. Launch the application and log in.

[1709] 2. Access the household data entry screen and enter the start time for washing dishes (18:00).

[1710] 3. When you finish washing the dishes, enter the finish time (18:15) and click the send button.

[1711] The terminal transmits this household chore data to the server.

[1712] In parallel, the device's emotion engine analyzes the user's facial expressions and tone of voice and determines, for example, that the user is tired. This emotion data is also sent to the server.

[1713] The server stores the received data in a database and analyzes it. As a result, it generates suggestions such as "Use the automatic dishwasher next time" or "Listen to relaxing music to reduce stress." These suggestions are notified to the device and displayed to the user.

[1714] In this way, the system can make housework more efficient for users and, by making optimal suggestions based on emotions, reduce stress for users and make housework more comfortable.

[1715] The processing flow will be explained below.

[1716] Step 1:

[1717] Terminal: The user launches the application, which then displays the login screen.

[1718] Step 2:

[1719] User: Enters login information and clicks the Login button.

[1720] Step 3:

[1721] Server: Receives login information and performs user authentication. If authentication is successful, sends the user's housework data entry screen to the terminal.

[1722] Step 4:

[1723] Terminal: Displays the housework data entry screen and allows the user to enter housework data.

[1724] Step 5:

[1725] User: Enters household chore data. For example, the start and end times of washing dishes and the cooking method used.

[1726] Step 6:

[1727] Terminal: The entered data is temporarily saved and sent to the server when the send button is pressed.

[1728] Step 7:

[1729] Terminal: When the send button for housework data is pressed, the data is sent to the server. The data includes the user ID and detailed housework information.

[1730] Step 8:

[1731] Server: Stores the received housework data in a database.

[1732] Step 9:

[1733] Server: Analyzes the user's past housework data based on the data stored in the database. For example, calculates the average time for each housework task and the most frequently used cooking method.

[1734] Step 10:

[1735] Server: Based on the analysis results, the server suggests efficient ways to do housework next time. For example, it generates a suggestion such as, "If you use a fully automatic dishwasher next time, you can reduce the time it takes to wash dishes by 30%."

[1736] Step 11:

[1737] Device: Uses an emotion engine to recognize the user's facial expressions and tone of voice in real time to determine stress levels and satisfaction.

[1738] Step 12:

[1739] Device: Sends emotional data to the server, including the user's stress level, satisfaction level, etc.

[1740] Step 13:

[1741] Server: Receives emotion data and stores it in a database.

[1742] Step 14:

[1743] Server: Combines and analyzes emotional data and housework data, and recalculates efficient ways to perform housework that take the user's emotional state into account.

[1744] Step 15:

[1745] Server: For example, if the user is under high stress, the server will suggest ways to complete housework in a short amount of time or ways to relax, such as listening to music.

[1746] Step 16:

[1747] Server: Sends the generated proposal to the device.

[1748] Step 17:

[1749] Device: Receives the suggestion and notifies the user, which is displayed on the device screen.

[1750] Step 18:

[1751] User: Review the suggestions and decide whether to incorporate them into your next chore.

[1752] Step 19:

[1753] User: When entering a traditional local recipe, the user accesses the recipe entry screen and enters the specific recipe and steps.

[1754] Step 20:

[1755] Terminal: Sends the inputted traditional recipe data to the server.

[1756] Step 21:

[1757] Server: Stores the received data in a database and manages the stored data so that it can be provided to other users.

[1758] The above is the specific processing flow of this system.

[1759] Example 2

[1760] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1761] Conventional housework suggestion systems are limited to suggestions based on user input data and are unable to provide optimal suggestions that reflect the user's emotions and stress levels. This makes it difficult to reduce user stress and provide a comfortable housework environment. Furthermore, there is a lack of suggestions that take into account local traditional methods and the characteristics of individual users.

[1762] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1763] In this invention, the server includes a means for providing a user interface, a means for inputting data related to housework from a user, a means for transmitting the input data to the server, a means for storing and analyzing the input data in the server, a means for generating efficient housework methods based on the analysis results, a means for notifying the user of the generated efficient housework methods, a means for recognizing the user's emotions, a means for transmitting the recognized emotion data to the server, a means for storing and analyzing the emotion data, and a means for optimizing efficient suggestions based on the emotion data. This enables optimal housework suggestions that take into account the user's emotional state and stress level in real time, thereby reducing the user's stress and providing a comfortable housework environment. Furthermore, suggestions are effectively made that take into account local traditional methods and the characteristics of each individual user.

[1764] A "user interface" is the means by which a user interacts with a system and inputs data.

[1765] "Housework data" refers to information such as the type of housework the user performed, the start time, the end time, and the tools and materials used.

[1766] "Server" means a computing device that receives, stores, and analyzes data submitted by users and generates and provides recommendations to users.

[1767] "Means for recognizing emotions" refers to a set of emotion engines and related hardware and software for determining emotions from the user's facial expressions, tone of voice, etc.

[1768] "Efficient ways to do housework" are methods and means for doing housework more efficiently that are suggested to users based on data analyzed by the server.

[1769] "Emotional data" refers to data collected by the emotion engine regarding the user's emotional state, such as stress level and satisfaction.

[1770] "Means of notification" refers to the method by which the server-generated proposal is communicated to the user, such as a push notification or a screen display in the application.

[1771] "Means to learn and evolve" refers to a method of continuously improving the content of proposals using machine learning algorithms based on collected data.

[1772] "Traditional local methods" refer to the household and cooking methods that have been historically practiced in a particular region, and include the means of storing them in a database.

[1773] The "optimizing means" is a method for analyzing emotional data and generating optimal suggestions corresponding to the user's emotional state.

[1774] This invention relates to a system that provides suggestions to help users perform housework efficiently and comfortably. The system includes the following functions: user interface, data input and transmission, server data storage and analysis, efficient suggestion generation, suggestion notification, and emotion engine.

[1775] Providing a user interface

[1776] The device, like a smartphone or tablet, provides an application and displays a screen where users can input data about their household chores. The interface includes fields for inputting the start and end times of chores, specific chore tasks, and the tools and materials used.

[1777] Data transmission and storage

[1778] The household chore data entered by the user is sent from the device via the internet to a server, which stores the received data in a database for later analysis.

[1779] Analyzing data and generating recommendations

[1780] The server analyzes the stored housework data. Specifically, it evaluates the time required for housework and the efficiency of the tools used, and then suggests how to proceed with the housework more efficiently next time. For example, it analyzes the time it takes to wash dishes and generates a suggestion that using an automatic dishwasher next time can reduce the time by 30%.

[1781] Combining Emotion Engines

[1782] The device is equipped with an emotion engine that analyzes the user's facial expressions and tone of voice, and in combination with hardware such as a camera and microphone, it analyzes the user's stress level and satisfaction in real time.

[1783] Sending and analyzing emotional data

[1784] The device sends the recognized emotion data to a server via the Internet. The server stores this emotion data in a database and analyzes it in conjunction with housework data. For example, it can analyze the user's stress level after doing housework.

[1785] Generating Sentiment-Based Recommendations

[1786] The server uses emotional data to assess the user's stress level and satisfaction level, and adjusts housework suggestions accordingly: if the user is tired, it suggests shorter chores or chores to be done while listening to relaxing music.

[1787] Proposal Notification

[1788] The system's generated suggestions are sent from the server to the device and displayed to the user, who can use these suggestions to perform housework more efficiently and stress-free.

[1789] Specific examples

[1790] For example, if a user wants to wash dishes, they might do the following:

[1791] 1. The user launches the application and logs in.

[1792] 2. The user accesses the housework data entry screen and enters the start time (18:00) and end time (18:15) for washing dishes.

[1793] 3. The device sends this household data to the server.

[1794] 4. The device's emotion engine analyzes the user's facial expressions and tone of voice and determines that the user is tired. This emotion data is also sent to the server.

[1795] 5. The server stores the received data in a database, analyzes it, and generates recommendations such as "Use an automatic dishwasher next time" or "Listen to relaxing music to reduce stress."

[1796] 6. This suggestion is notified to the device and displayed to the user.

[1797] Prompt Sentence Examples

[1798] Please suggest efficient ways to do housework. Specifically, I would like the following information: which chores to do and in what order, efficient tools and methods, and things to be careful about. Also, please suggest what to do if you are feeling stressed.

[1799] This allows the system to make housework more efficient for users, and by making optimal suggestions based on emotions, it reduces stress for users and makes housework more comfortable.

[1800] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1801] Step 1: Displaying the User Interface

[1802] The device launches the application and displays a screen where the user can enter data about the chore. Input fields include the start and end times of the chore, the specific chore, and the tools and materials used, making it easy for the user to enter the required data.

[1803] Input: Launch application

[1804] Output: Display of data entry screen

[1805] How it works: The application invokes the interface and provides the necessary input fields.

[1806] Step 2: Enter household chore data

[1807] The user enters the start and end times of the chore, the chore performed, and the tools and materials used in the input fields. For example, the user enters the start time (18:00) and end time (18:15) of washing dishes, and the detergent used.

[1808] Input: Manually entered data by the user

[1809] Output: Input housework data

[1810] How it works: The user enters the required data into the application's input fields and presses the confirm button.

[1811] Step 3: Sending data

[1812] The device sends the household data entered by the user to a server via the Internet, with encryption protocols such as SSL used to ensure security during transmission.

[1813] Input: Entered housework data

[1814] Output: Send data to the server

[1815] What it does: After the submit button is pressed, the data is sent as an HTTP request to the server.

[1816] Step 4: Save your data

[1817] The server stores the received housework data in a database, including detailed information such as the date and time, the contents of the housework, the time required, and the tools used.

[1818] Input: Submitted housekeeping data

[1819] Output: Housework data stored in a database

[1820] How it works: The server parses the data and stores it in the appropriate tables in the database.

[1821] Step 5: Analyze the data

[1822] The server analyzes the efficiency of housework based on the stored housework data, for example, evaluating the time it took to wash dishes and the efficiency of the tools used, and generates suggestions for the next time.

[1823] Input: Saved housework data

[1824] Output: Analysis results on housework efficiency

[1825] How it works: The server analyzes the data using statistical methods and machine learning models to generate results.

[1826] Step 6: Generate proposals

[1827] The server then makes suggestions based on the analysis results on how to efficiently carry out housework, such as suggesting that you use an automatic dishwasher next time.

[1828] Input: Analysis results on housework efficiency

[1829] Output: Suggestions for efficient housework

[1830] Operation: The server runs the proposal algorithm and stores the generated proposals in a database.

[1831] Step 7: Collect emotion data

[1832] The device uses an emotion engine to analyze the user's facial expressions and tone of voice to collect emotional data, such as measuring the user's stress level and satisfaction in real time using the camera and microphone.

[1833] Input: User's facial expression, tone of voice

[1834] Output: Collected emotion data

[1835] How it works: The emotion engine analyzes the user's video and audio and quantifies their emotional state.

[1836] Step 8: Sending Emotion Data

[1837] The device sends the collected emotion data to a server via the internet, using encryption protocols such as SSL to ensure security.

[1838] Input: Collected emotion data

[1839] Output: Send emotion data to the server

[1840] How it works: After the send button is pressed, the emotion data is sent as an HTTP request to the server.

[1841] Step 9: Store and analyze sentiment data

[1842] The server stores the received emotion data in a database and analyzes it in conjunction with housework data, for example, to analyze the user's stress level after doing housework.

[1843] Input: Emotion data sent

[1844] Output: Emotion data stored in a database, analysis results including emotion data

[1845] Operation: The server stores the emotion data in the appropriate table in the database, integrates it with the household chore data, and performs analysis.

[1846] Step 10: Generate sentiment-based suggestions

[1847] The server optimizes efficient suggestions based on emotion data. If the user is tired, it suggests ways to complete the chore quickly or to do it while listening to relaxing music.

[1848] Input: Analysis results including sentiment data

[1849] Output: Optimized suggestions based on emotions

[1850] Operation: The server runs the proposal algorithm and stores the generated proposals in a database.

[1851] Step 11: Proposal Notification

[1852] The server notifies the device of new suggestions generated and displays them to the user, for example, by using the push notification function of a smartphone.

[1853] Input: Generated proposals

[1854] Output: Proposal notification to device, suggestion display to user

[1855] How it works: The server uses the Notifications API to send suggestions to the device, which then displays them to the user as push notifications.

[1856] Through these processing steps, the system can streamline the user's housework and provide optimal suggestions based on the user's emotional state.

[1857] (Application example 2)

[1858] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1859] In today's world, the use of food delivery services is rapidly increasing, but the user experience is uniform and does not take into account the emotions and circumstances of individual users. This can lead to dissatisfaction and a decrease in repeat customers. It can also cause users to feel stressed and the service to be inefficient. Therefore, there was a need for a system that could use user emotional data to optimize suggestions and provide feedback tailored to individual needs.

[1860] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1861] In this invention, the server includes means for providing a user interface, means for inputting data related to housework from a user, means for transmitting the input data to the server, means for storing and analyzing the input data in the server, means for generating efficient housework methods based on the analysis results, means for notifying the user of the generated efficient housework methods, means for acquiring user emotion data, means for transmitting the acquired emotion data to the server, and means for analyzing the emotion data and optimizing suggestions for housework methods. This enables optimal suggestions based on the emotions of each individual user, thereby improving satisfaction and reuse rates of food delivery services.

[1862] A "user interface" is the part of a system that provides a screen or operating means for a user to input data or view information.

[1863] "Data" refers to recorded content such as information, numbers, text, etc. obtained from a user.

[1864] A "server" is a computer system for storing, analyzing, and processing data.

[1865] "Emotion data" is information indicating the emotional state of the user obtained from facial expressions, voice, etc.

[1866] "Analysis" is the act of generating useful information and suggestions based on collected data using methods such as statistical processing and machine learning.

[1867] "Suggestions" refer to useful information or guidelines for action that the server provides to the user based on the analysis results.

[1868] "Notification" refers to the act of sending the generated suggestion to the user's terminal and displaying it to the user.

[1869] "Optimization" is the act of adjusting a proposal to achieve the most effective and efficient results under specific conditions.

[1870] A "food delivery service" is a service that allows you to order food and drinks and have them delivered to a specified location.

[1871] To implement this invention, a system using a user interface, data acquisition and transmission, data analysis and suggestion generation by a server, and an emotion recognition engine is required.

[1872] composition

[1873] User Interface

[1874] The device provides an interface for users to input data. This interface includes screens where users can enter chore and meal data. Users enter data such as start and end times for chores, meal details, and ingredients used.

[1875] Data transmission and storage

[1876] The device sends the data entered by the user to a server, where it is stored in a database for later analysis.

[1877] Analyzing data and generating recommendations

[1878] The server generates suggestions for efficient housework and food delivery based on the stored data. For example, suggestions for housework include "using the dishwasher to save time" and for food delivery include "listening to relaxing music while doing the job." The server performs analysis using Python and R.

[1879] Emotion Engine

[1880] The device is equipped with an emotion engine that analyzes the user's facial expressions and tone of voice to obtain emotional data, which is also sent to a server and used to optimize suggestions for housework and food delivery.

[1881] Sending and analyzing emotional data

[1882] The device sends the acquired emotional data to the server, which stores this emotional data in a database and analyzes it in conjunction with the household chore data. By using the emotional data, the suggestions are optimized to match the user's emotional state.

[1883] Proposal Notification

[1884] The server then notifies the user of the generated suggestions via push notifications or in-app notifications.

[1885] Hardware and software used

[1886] Hardware:

[1887] Camera (webcam or smartphone camera): To capture the user's facial expressions.

[1888] Smartphone or PC: To provide the user interface and enter data.

[1889] software:

[1890] OpenCV: To capture and preprocess the user's facial expressions.

[1891] TensorFlow / Keras: To run the emotion recognition model.

[1892] Flask or Django: For parsing and saving data server-side.

[1893] Pandas / Numpy: To perform data analysis.

[1894] Requests: To send data.

[1895] Specific examples

[1896] For example, consider a user ordering food delivery. The user does the following:

[1897] 1. Launch the application and log in.

[1898] 2. Access the meal data entry screen and enter the details of the meal you ordered (e.g., pizza, salad) and the delivery time (e.g., 18:30).

[1899] 3. Use the device's camera to capture the user's facial expression.

[1900] The device sends this data and emotional data to a server, which stores and analyzes the data. As a result, it generates suggestions, such as "Next time, serve a relaxing dinner set." These suggestions are then communicated to the user via push notifications or in-app messages.

[1901] Prompt Sentence Examples

[1902] "I'd like to develop a food delivery app, but I'd like to add a feature that suggests meals based on the user's emotions. Specifically, the application should capture and analyze the user's facial expressions and tone of voice, send that data to a server, and notify the server with generated suggestions."

[1903] This will enable optimal proposals tailored to each user's individual circumstances, which is expected to improve satisfaction with the service.

[1904] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1905] Step 1:

[1906] The user launches the application and logs in.

[1907] Input: User credentials (username, password)

[1908] Output: The user is authenticated and redirected to the main page.

[1909] Operation: The terminal sends the authentication information entered by the user to the server, which then checks it against a database to perform the authentication process.

[1910] Step 2:

[1911] The user accesses the meal data entry screen and enters the details of the ordered meal and the delivery time.

[1912] Input: Meal details (e.g., pizza, salad), delivery time (e.g., 18:30)

[1913] Output: The entered meal data is saved on the device and a submit button is displayed.

[1914] How it works: The terminal's user interface provides the user with fields for data entry and a submit button.

[1915] Step 3:

[1916] It uses the device's camera to capture the user's facial expressions.

[1917] Input: An image of the user's face

[1918] Output: Captured face image data

[1919] What it does: The device activates the camera, captures the user's facial expressions, and saves them as image data.

[1920] Step 4:

[1921] Analyze emotions from facial image data.

[1922] Input: Face image data

[1923] Output: Numerical data of the user's emotions (e.g., emotion scores such as happiness, sadness, anger, etc.)

[1924] How it works: The device preprocesses facial images using OpenCV and inputs them into a TensorFlow / Keras model to analyze emotions.

[1925] Step 5:

[1926] The input food data and emotion data are sent to the server.

[1927] Input: Food data, emotion data

[1928] Output: User's food and emotion data stored on the server

[1929] How it works: The device converts this data into JSON format and sends it to the server, which stores it in a database.

[1930] Step 6:

[1931] The server analyzes the received data and generates efficient meal suggestions.

[1932] Input: Stored food and emotion data

[1933] Output: Optimized meal recommendations (e.g., menu recommendations for your next order and time discount information)

[1934] How it works: The server uses Pandas and Numpy to analyze the data and generate recommendations that take into account the user's past order history and emotional state.

[1935] Step 7:

[1936] The suggestions will be sent to the user's device.

[1937] Input: Optimized meal suggestions

[1938] Output: Proposal notification displayed on the user's device

[1939] How it works: The server sends the generated suggestions to the user's device as push notifications or in-app messages.

[1940] This will provide the optimal food delivery experience according to the user's emotional state, and is expected to improve service satisfaction and increase repeat rates.

[1941] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1942] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1943] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1944] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1945] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1946] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1947] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1948] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1949] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1950] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1951] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1952] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1953] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1954] 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.

[1955] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1956] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1957] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1958] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1959] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1960] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1961] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1962] The following is further disclosed regarding the above embodiment.

[1963] (Claim 1)

[1964] a means for providing a user interface;

[1965] means for inputting household chore data from a user;

[1966] means for transmitting the input data to a server;

[1967] means for storing and analyzing the input data in a server;

[1968] A means for generating an efficient way of performing housework based on the analysis results;

[1969] A means to notify users of the generated efficient housework methods.

[1970] A system including:

[1971] (Claim 2)

[1972] 10. The system of claim 1, further comprising means for learning and evolving effective suggestions based on data collected from the user.

[1973] (Claim 3)

[1974] 10. The system of claim 1, further comprising means for collecting local traditional recipes from users and storing them in a database.

[1975] "Example 1"

[1976] (Claim 1)

[1977] a means for providing a user interface;

[1978] means for inputting household chore data from a user;

[1979] means for transmitting the input data to a server;

[1980] means for storing and analyzing the input data in a server;

[1981] A means for generating an efficient way of performing housework based on the analysis results;

[1982] A means for notifying the user of the generated efficient housework methods;

[1983] means for inputting local traditional recipes from a user;

[1984] a means for transmitting the inputted traditional recipe to a server and storing it in a database;

[1985] A means of managing traditional recipes that are saved for others to access;

[1986] A system including:

[1987] (Claim 2)

[1988] 10. The system of claim 1, further comprising means for learning and evolving effective suggestions based on data collected from the user.

[1989] (Claim 3)

[1990] 10. The system according to claim 1, further comprising means for notifying a terminal of the generated efficient housework procedure and displaying the procedure on the terminal.

[1991] "Application Example 1"

[1992] (Claim 1)

[1993] a means for providing a user interface;

[1994] means for inputting household chore data from a user;

[1995] means for transmitting the input data to a server;

[1996] means for storing and analyzing the input data in a server;

[1997] A means for generating an efficient way of performing housework based on the analysis results;

[1998] A means to notify users of the generated efficient housework methods.

[1999] A means for the home robot to monitor the progress of household chores;

[2000] How can domestic robots practice traditional cooking methods?

[2001] A system including:

[2002] (Claim 2)

[2003] 10. The system of claim 1, further comprising means for learning and evolving effective suggestions based on data collected from the user.

[2004] (Claim 3)

[2005] 10. The system of claim 1, further comprising means for collecting local traditional recipes from users and storing them in a database.

[2006] "Example 2: Combining Emotion Engines"

[2007] (Claim 1)

[2008] a means for providing a user interface;

[2009] means for inputting household chore data from a user;

[2010] means for transmitting the input data to a server;

[2011] means for storing and analyzing the input data in a server;

[2012] A means for generating an efficient way of performing housework based on the analysis results;

[2013] A means for notifying the user of the generated efficient housework methods;

[2014] a means of recognizing a user's emotions;

[2015] means for transmitting the recognized emotion data to a server;

[2016] a means for storing and analyzing the emotion data;

[2017] A means of optimizing efficient proposals based on emotion data;

[2018] A system including:

[2019] (Claim 2)

[2020] 10. The system of claim 1, further comprising means for learning and evolving effective suggestions based on collected data and sentiment data from the user.

[2021] (Claim 3)

[2022] 10. The system of claim 1, further comprising means for collecting local traditional methods from users and storing them in a database.

[2023] "Application example 2 when combining emotion engines"

[2024] (Claim 1)

[2025] a means for providing a user interface;

[2026] means for inputting household chore data from a user;

[2027] means for transmitting the input data to a server;

[2028] means for storing and analyzing the input data in a server;

[2029] A means for generating an efficient way of performing housework based on the analysis results;

[2030] A means for notifying the user of the generated efficient housework methods;

[2031] A means for acquiring user emotion data;

[2032] means for transmitting the acquired emotion data to a server;

[2033] A means of analyzing emotional data and optimizing suggestions for how to proceed with housework,

[2034] A system including:

[2035] (Claim 2)

[2036] 10. The system of claim 1, further comprising means for learning and evolving effective suggestions based on data collected from the user.

[2037] (Claim 3)

[2038] 10. The system of claim 1, further comprising means for collecting local traditional recipes from users and storing them in a database. [Explanation of symbols]

[2039] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for providing a user interface; means for inputting household chore data from a user; means for transmitting the input data to a server; means for storing and analyzing the input data in a server; A means for generating an efficient way of performing housework based on the analysis results; A means to notify users of the generated efficient housework methods. A system including:

2. The system of claim 1 , further comprising means for learning and evolving effective suggestions based on data collected from users.

3. 10. The system of claim 1, further comprising means for collecting local traditional recipes from users and storing them in a database.

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