System

Abstract

An object of a system according to an embodiment is to visually support a cooking process and enhance a user's motivation.SOLUTION: A system according to an embodiment includes a camera, a projector, and a projection mapping unit. The camera is installed on the ceiling of the kitchen and captures an image of a cooking process. The projector displays a cutting method or the like by projection mapping based on the information analyzed by the AI analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has limited means of visually supporting the cooking process, and there has been a lack of ingenuity to increase user motivation.

[0005] The system according to the embodiment aims to provide visual support for the cooking process and increase the motivation of the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a camera, a projector, and a projection mapping unit. The camera is installed on the kitchen ceiling and captures the cooking process. The projector displays cutting instructions and other information using projection mapping based on the information analyzed by the AI ​​analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment visually supports the cooking process and can increase the motivation of the user. [Brief explanation of the drawings]

[0008] [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. DETAILED DESCRIPTION OF THE INVENTION

[0009] 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.

[0010] First, the terms used in the following description will be explained.

[0011] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] 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.

[0013] 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.

[0014] 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), and Bluetooth (registered trademark).

[0015] 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."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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).

[0019] 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.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. 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 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. 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.

[0022] 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.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A cooking support system according to an embodiment of the present invention uses a camera and projector installed on the kitchen ceiling to recognize the cooking process in real time and display cutting instructions and other information using projection mapping. This allows users to improve their cooking skills while enjoying cooking.

[0029] A cooking support system according to an embodiment includes a camera, a projector, an AI analysis unit, and a projection mapping unit. The camera captures images of cooking processes. For example, the camera may be installed on the ceiling and capture the entire work surface. The camera can cover a wide area using a wide-angle lens. The camera can transmit images to the AI ​​analysis unit in real time. The projector includes a projection mapping unit that displays cutting instructions and other information using projection mapping based on information analyzed by the AI ​​analysis unit. For example, the projector can project cutting guidelines onto the work surface. The projector can also display step-by-step cooking procedures. The projector can dynamically change the display content according to the user's progress. The AI ​​analysis unit analyzes the images sent from the camera and recognizes the cooking process being performed by the user. For example, the AI ​​analysis unit can track the user's hand movements using image recognition technology to determine the cutting method. The AI ​​analysis unit can also classify cooking processes using a machine learning algorithm. The AI ​​analysis unit can also analyze the user's actions in real time and generate appropriate instructions. The projection mapping unit controls the projector and determines the display content based on the instructions generated by the AI ​​analysis unit. For example, the projection mapping unit projects cutting guidelines onto the work surface. The projection mapping unit also displays cooking procedures step by step. The projection mapping unit also dynamically changes the display content according to the user's progress. This allows the cooking support system according to the embodiment to enable users to improve their cooking skills while enjoying cooking. For example, users can enjoy cooking while learning the techniques of professional chefs. Furthermore, being praised by the AI ​​increases motivation. Furthermore, incorporating a game element allows users to enjoy cooking.

[0030] The camera is equipped with a depth sensor and can generate 3D models to perform more accurate projection mapping. For example, by adding a depth sensor, the camera can accurately determine the positions of cooking ingredients and utensils as a 3D model. For example, the height and shape of vegetables can be measured in real time and projection mapping can be performed based on that information. The camera can also use the depth sensor to analyze the user's hand movements as a 3D model. For example, it can accurately determine the position and movement of the hand and display cutting guidelines. The camera can also use the depth sensor to adjust the height and angle of the work surface. For example, it can optimize the display position of the projection mapping. This allows for more accurate projection mapping by generating a 3D model.

[0031] By combining AR technology with the projector, the user can wear AR glasses and receive interactive guidance. For example, the projector can link the projector's image to the AR glasses, allowing the user to receive interactive guidance through the AR glasses. For example, cutting guidelines can be displayed on the AR glasses, allowing the user to cut accurately while looking at their hands. The projector can also use AR technology to dynamically change the display content in response to the user's movements. For example, when the user moves their hand, the guidelines displayed on the AR glasses also move. The projector can also use AR technology to track the user's line of sight and display guidelines in front of their line of sight. For example, cutting guidelines can be displayed where the user is looking. In this way, the use of AR technology allows the user to receive more interactive guidance.

[0032] The camera and projector can be modularized and applied to other household tasks. For example, the camera and projector can be modularized and applied to DIY tasks. For example, furniture assembly procedures can be displayed using projection mapping, allowing the user to follow the steps. The camera and projector can also be applied to cleaning tasks. For example, cleaning procedures and important points can be displayed using projection mapping, allowing the user to clean efficiently. The camera and projector can also be applied to laundry tasks. For example, how to separate laundry and the amount of detergent to use can be displayed using projection mapping, allowing the user to do the laundry accurately. In this way, the modularization of the camera and projector can be applied to other household tasks.

[0033] The image from the projector can be mirrored on a smartphone or tablet, allowing users to receive guidance simultaneously on multiple devices. For example, the image from the projector can be mirrored on a smartphone, allowing users to view the guidance at their fingertips. For example, cutting guidelines can be displayed on the smartphone, allowing users to work while looking at their hands. The image from the projector can also be mirrored on a tablet, allowing users to receive guidance simultaneously on multiple devices. For example, users can check a recipe on another device while looking at the guidelines displayed on the tablet. The image from the projector can also be mirrored using a Wi-Fi or Bluetooth connection. For example, high-resolution images can be mirrored in real time using a Wi-Fi connection. This allows users to receive guidance simultaneously on multiple devices, improving convenience.

[0034] The AI ​​analysis unit can also use voice recognition to determine the cooking stage from the user's voice. For example, the AI ​​analysis unit uses camera footage and voice recognition together to determine the cooking stage from the user's voice. For example, if the user says, "Next, I'll cut the onions," the AI ​​recognizes the instruction and displays appropriate guidance. The AI ​​analysis unit can also use voice recognition technology to analyze the user's voice and determine the cooking stage. For example, it can analyze voice commands and display the next step. The AI ​​analysis unit can also use voice recognition technology to analyze the tone and speed of the user's voice to determine the progress of cooking. For example, if the voice tone is high, it will display the next step quickly. In this way, by using voice recognition in combination, the cooking stage can also be determined from the user's voice.

[0035] The AI ​​analysis unit can learn from past cooking data and adapt to the user's individual cooking style. For example, the AI ​​analysis unit can learn from past cooking data and adapt to the user's individual cooking style. For example, it can learn the user's frequently used cooking methods and procedures and provide guidance based on them. The AI ​​analysis unit can also analyze past cooking data to understand the user's preferences and habits. For example, it can learn the user's preferred seasonings and cooking times and suggest recipes based on them. The AI ​​analysis unit can also use past cooking data to evaluate the user's skill level and provide guidance of an appropriate level of difficulty. For example, it can teach basic procedures to beginners, advanced procedures to intermediate cooks, and professional techniques to advanced cooks. In this way, by learning from past cooking data, it can adapt to the user's individual cooking style.

[0036] The AI ​​analysis unit can also recognize the steps in household chores other than cooking and issue instructions in the same way. For example, the AI ​​analysis unit can be expanded into a system where AI recognizes the steps in laundry and issues appropriate instructions. For example, it can show how to separate laundry and how much detergent to use using projection mapping. The AI ​​analysis unit can also recognize the steps in cleaning and issue appropriate instructions. For example, it can display cleaning procedures and important points using projection mapping. The AI ​​analysis unit can also recognize the steps in tidying up and issue appropriate instructions. For example, it can show how to arrange and store things using projection mapping. This can be applied to household chores other than cooking, making it possible to support general housework.

[0037] The AI ​​analysis unit can share the recognized cooking steps with other users and receive feedback within the community. For example, the AI ​​analysis unit can share the cooking steps recognized by the AI ​​on an online platform and receive feedback from other users. For example, cooking procedures and tips can be shared and opinions exchanged within the community. The AI ​​analysis unit can also share the recognized cooking steps on social media and receive comments and ratings from other users. For example, photos and videos of cooking can be posted and feedback can be received from other users. The AI ​​analysis unit can also save the recognized cooking steps on the cloud and share them with other users. For example, a recipe can be shared on the cloud and advice can be received from other users. In this way, by sharing the recognized cooking steps, feedback can be received from other users.

[0038] The AI ​​analysis unit adjusts the difficulty level according to the user's cooking skill and gradually increases the difficulty level to encourage continuous learning. For example, the AI ​​analysis unit evaluates the user's cooking skill and provides guides divided into beginner, intermediate, and advanced difficulty levels. For example, it can teach basic cutting techniques to beginners, advanced cutting techniques to intermediate users, and professional techniques to advanced users. The AI ​​analysis unit can also analyze the user's progress and adjust the difficulty level at the appropriate time. For example, once the user has mastered the basic steps, it will instruct them to move on to the next step. The AI ​​analysis unit can also adjust the difficulty level based on user feedback. For example, if the user finds the task difficult, it will suggest lowering the difficulty level. This allows the difficulty level to be adjusted according to the user's cooking skill, encouraging continuous learning.

[0039] The AI ​​analysis unit can add to the fun by inserting quizzes and mini-games in real time depending on the cooking progress. For example, the AI ​​analysis unit can insert quizzes depending on the cooking progress, allowing the user to learn while having fun. For example, it can ask a quiz about how to cut vegetables, and if the answer is correct, the user can earn points. The AI ​​analysis unit can also insert mini-games depending on the cooking progress. For example, it can hold a time attack or score challenge, allowing the user to enjoy cooking. The AI ​​analysis unit can also display messages praising the user depending on the cooking progress. For example, if the user answers a quiz correctly, it can display "Great!". In this way, inserting quizzes and mini-games increases the fun for the user.

[0040] The AI ​​analysis unit can introduce game elements into household chores other than cooking, making all housework more enjoyable. The AI ​​analysis unit can, for example, introduce game elements into household chores other than cooking, expanding the system to make all housework more enjoyable. For example, cleaning and laundry procedures can be gamified, allowing points to be earned. The AI ​​analysis unit can also insert quizzes and mini-games depending on the progress of the household chore. For example, it can pose a quiz about cleaning procedures, and points can be earned for correct answers. The AI ​​analysis unit can also display messages praising the user depending on the progress of the household chore. For example, if the user has completed cleaning, it can display "Great!". In this way, by introducing game elements into household chores other than cooking, all housework can be made more enjoyable.

[0041] The AI ​​analysis unit can add a ranking function that allows users to compete against each other, encouraging competition within the community. The AI ​​analysis unit can add a ranking function that allows users to compete against each other, encouraging competition within the community. For example, points can be awarded based on cooking skills and progress, and a ranking can be displayed. The AI ​​analysis unit can also analyze the user's progress and update the ranking in real time. For example, if a user acquires a new skill, the ranking will rise. The AI ​​analysis unit can also display a message praising the user based on the ranking. For example, if the ranking rises, the AI ​​analysis unit can display a message saying "Amazing!". In this way, adding the ranking function encourages competition between users.

[0042] The AI ​​analysis unit can provide a sense of accomplishment by analyzing the user's past cooking data and visualizing their progress. For example, the AI ​​analysis unit can analyze the user's past cooking data and build a system that visualizes their progress. For example, it can display the completion rate and speed of past cooking in a graph, providing a sense of accomplishment. The AI ​​analysis unit can also analyze the user's past cooking data and evaluate their skill improvement. For example, it can compare past dishes with current dishes to show skill improvement. The AI ​​analysis unit can also display goals that have been achieved based on the user's past cooking data. For example, if a specific dish is perfectly reproduced, the title of "Master Chef" can be awarded. In this way, the analysis of past cooking data and visualization of progress provide the user with a sense of accomplishment.

[0043] The AI ​​analysis unit can provide personalized feedback according to the user's preferences and goals. For example, the AI ​​analysis unit can provide personalized feedback according to the user's preferences and goals. For example, the AI ​​analysis unit can provide appropriate advice based on the user's favorite dishes or the skills they are aiming for. The AI ​​analysis unit can also analyze the user's progress and provide personalized feedback. For example, if the user has mastered a specific skill, it can provide advice based on that skill. The AI ​​analysis unit can also provide personalized feedback based on the user's feedback. For example, if the user finds something difficult, it can provide advice based on that feedback. In this way, user satisfaction can be improved by providing feedback according to the user's preferences and goals.

[0044] The AI ​​analysis unit can introduce a motivation-boosting function to household tasks other than cooking, making all housework more enjoyable. The AI ​​analysis unit can, for example, introduce a motivation-boosting function to household tasks other than cooking, expanding the system to make all housework more enjoyable. For example, it can provide praise and encouraging messages depending on the progress of cleaning or laundry. The AI ​​analysis unit can also analyze the progress of household tasks and display motivation-boosting messages at appropriate times. For example, when a user completes cleaning, it displays "Great!" The AI ​​analysis unit can also provide rewards to users depending on the progress of household tasks. For example, it can award points when cleaning is completed. In this way, by introducing a motivation-boosting function to household tasks other than cooking, all housework can be made more enjoyable.

[0045] The AI ​​analysis unit can provide a sense of accomplishment by awarding badges and titles according to the goals the user achieves. For example, the AI ​​analysis unit can build a system that awards badges and titles according to the goals the user achieves. For example, if a specific dish is perfectly reproduced, the title of "Master Chef" is awarded. The AI ​​analysis unit can also analyze the user's progress and award badges according to the goals achieved. For example, a badge is awarded when a specific skill is mastered. The AI ​​analysis unit can also display messages to provide a sense of accomplishment based on user feedback. For example, if the user achieves a goal, it displays "Amazing!". In this way, a sense of accomplishment is provided by awarding badges and titles according to the goals the user achieves.

[0046] The AI ​​analysis unit can analyze cooking videos of professional chefs and show the same steps to users in real time. For example, the AI ​​analysis unit can build a system in which AI analyzes cooking videos of professional chefs and shows the same steps to users in real time. For example, it can analyze the chef's hand movements and cooking steps and provide guidance to the user. The AI ​​analysis unit can also analyze cooking videos of professional chefs and show users step-by-step steps. For example, it can break down the chef's movements and display each step. The AI ​​analysis unit can also analyze cooking videos of professional chefs and give instructions to users at the appropriate time. For example, it can give instructions to users when the chef is about to proceed to the next step. In this way, by analyzing cooking videos of professional chefs and showing users the same steps in real time, users can learn professional techniques.

[0047] The AI ​​analysis unit can evaluate whether the user was able to perfectly replicate the dish and provide feedback. The AI ​​analysis unit, for example, builds a system in which AI evaluates the user's dish and determines whether they were able to perfectly replicate it. For example, it analyzes the appearance and steps of the dish and evaluates it by comparing it with dishes made by professional chefs. The AI ​​analysis unit can also evaluate the user's dish and provide feedback. For example, it can evaluate the completeness and taste of the dish and point out areas for improvement. The AI ​​analysis unit can also evaluate the user's dish and display a message praising it. For example, if the dish is perfectly replicated, it will display "Amazing!". This allows the system to evaluate whether the user was able to perfectly replicate the dish and provide feedback, thereby supporting the user in improving their skills.

[0048] The AI ​​analysis unit can be expanded to a system that provides a perfect copy experience of skills other than cooking. The AI ​​analysis unit can be expanded to a system that provides a perfect copy experience of skills other than cooking (e.g., DIY or crafts). For example, the steps for assembling furniture can be displayed using projection mapping, allowing the user to reproduce the same steps. The AI ​​analysis unit can also display the steps for craft work using projection mapping, allowing the user to reproduce the same steps. For example, the steps for handicrafts and crafts can be displayed so that the user can accurately reproduce them. The AI ​​analysis unit can also display the steps for DIY work using projection mapping, allowing the user to reproduce the same steps. For example, the steps for using tools and assembly can be displayed so that the user can accurately reproduce them. This allows the user to learn a variety of skills by providing a perfect copy experience of skills other than cooking.

[0049] The AI ​​analysis unit allows users to share their perfect copy results with each other and receive feedback within the community. The AI ​​analysis unit, for example, builds a system that allows users to share their perfect copy results with each other and receive feedback within the community. For example, users can post photos and videos of their dishes and receive comments and ratings from other users. The AI ​​analysis unit can also share the perfect copy results on social media and receive feedback from other users. For example, users can share cooking procedures and tips and exchange opinions within the community. The AI ​​analysis unit can also save the perfect copy results in the cloud and share them with other users. For example, users can share recipes on the cloud and receive advice from other users. This allows users to share their perfect copy results with each other and receive feedback, thereby supporting the improvement of users' skills.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The cooking support system can further include an audio guide unit. The audio guide unit allows the user to receive instructions while cooking without using their hands. For example, the audio guide unit can provide audio guidance on cutting methods and cooking procedures. The audio guide unit can also change instructions in real time according to the user's progress. For example, when the user is ready to proceed to the next step, it will issue the next instruction by voice. The audio guide unit can also answer the user's questions by voice. For example, in response to the question, "What should I do next?", specific instructions will be provided by voice. This allows the user to receive cooking instructions without using their hands, improving convenience.

[0052] The camera may further include a temperature sensor. The temperature sensor measures the temperature of food in real time and provides guidance for maintaining the appropriate cooking temperature. For example, the temperature sensor can measure the temperature of a frying pan and notify the user when the appropriate temperature has been reached. The temperature sensor can also measure the internal temperature of an oven and provide guidance for maintaining the appropriate doneness. For example, a warning will be displayed if the oven temperature is too high. The temperature sensor can also measure the internal temperature of food and provide guidance for the appropriate cooking time. For example, a warning will be displayed when the internal temperature of meat has reached the appropriate temperature. Thus, the use of temperature sensors allows for more accurate cooking.

[0053] The projector can further include a color recognition function. The color recognition function is used to recognize the color of ingredients and evaluate their freshness and cooking state. For example, the color recognition function can analyze the color of vegetables and display a warning if they are no longer fresh. The color recognition function can also analyze the color of meat and determine the appropriate level of doneness. For example, the color can determine whether the meat is properly cooked and notify the user. The color recognition function can also analyze the color of sauces and soups and evaluate the appropriate cooking state. For example, a warning can be displayed if the sauce is burnt. As a result, the color recognition function can be used to more accurately evaluate the freshness and cooking state of ingredients.

[0054] The camera and projector may further include an educational mode. The educational mode provides a guide for learning basic cooking techniques and knowledge. For example, the educational mode may provide step-by-step instruction on how to use a knife and basic cutting techniques. The educational mode may also provide information on how to select and store ingredients. For example, the educational mode may teach how to identify fresh vegetables and how to store them properly. The educational mode may also provide advice on nutritional balance and healthy eating. For example, the educational mode may teach how to combine balanced meals and how to calculate calories. Thus, by using the educational mode, a user can improve their skills while learning basic cooking techniques and knowledge.

[0055] The projector's images can further utilize augmented reality (AR) technology to allow users to visually check the progress of cooking. For example, the projector can use AR technology to display 3D models of how to cut ingredients and cooking procedures. The projector can also use AR technology to display virtual guidelines at the user's fingertips. For example, cutting guidelines can be displayed at the user's fingertips, allowing the user to cut accurately. The projector can also use AR technology to display the progress of cooking in real time. For example, the state and temperature of ingredients can be visually confirmed during cooking. Thus, by using AR technology, the user can visually check the progress of cooking as they work.

[0056] The AI ​​analysis unit can further include a recipe suggestion unit. The recipe suggestion unit suggests appropriate recipes based on the user's preferences and ingredient stock. For example, the recipe suggestion unit can analyze the user's past cooking data and suggest recipes that suit the user's preferences. The recipe suggestion unit can also use a camera to recognize ingredients in the refrigerator and suggest recipes using those ingredients. For example, it can suggest recipes using vegetables or meat in the refrigerator. The recipe suggestion unit can also suggest recipes that take the user's nutritional balance into consideration. For example, it can suggest balanced meals and support a healthy diet. As a result, by using the recipe suggestion unit, users can easily find recipes based on their preferences and ingredient stock.

[0057] The AI ​​analysis unit can further include an ingredient management unit. The ingredient management unit manages ingredients in the user's refrigerator or pantry and keeps track of expiration dates and inventory status. For example, the ingredient management unit can use a camera to recognize ingredients in the refrigerator and record expiration dates. The ingredient management unit can also scan the barcodes of ingredients purchased by the user and manage inventory status. For example, it can scan barcodes to update ingredient inventory. The ingredient management unit can also notify the user of ingredients that are approaching their expiration date and suggest using them sooner. For example, it can suggest recipes using ingredients that are close to their expiration date. As a result, using the ingredient management unit allows the user to manage ingredients efficiently and reduce waste.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The camera captures the cooking process. For example, the camera is installed on the ceiling and can capture the entire work surface. The camera also uses a wide-angle lens to cover a wide area and transmits the footage to the AI ​​analysis unit in real time. Step 2: The AI ​​analysis unit analyzes the video sent from the camera and recognizes the cooking steps the user is performing. For example, the AI ​​analysis unit uses image recognition technology to track the user's hand movements and determine the cutting method. It also uses machine learning algorithms to classify the cooking steps, analyze the user's actions in real time, and generate appropriate instructions. Step 3: The projector uses projection mapping to display cutting instructions and other information based on the information analyzed by the AI ​​analysis unit. For example, the projector could project cutting guidelines onto a work surface and display step-by-step cooking instructions. The display content can also be dynamically changed according to the user's progress. Step 4: The projection mapping unit controls the projector and determines the display content based on the instructions generated by the AI ​​analysis unit. For example, the projection mapping unit projects cutting guidelines onto the work surface and displays step-by-step cooking instructions. It also dynamically changes the display content according to the user's progress.

[0060] (Example 2) A cooking support system according to an embodiment of the present invention uses a camera and projector installed on the kitchen ceiling to recognize the cooking process in real time and display cutting instructions and other information using projection mapping. This allows users to improve their cooking skills while enjoying cooking.

[0061] A cooking support system according to an embodiment includes a camera, a projector, an AI analysis unit, and a projection mapping unit. The camera captures images of cooking processes. For example, the camera may be installed on the ceiling and capture the entire work surface. The camera can cover a wide area using a wide-angle lens. The camera can transmit images to the AI ​​analysis unit in real time. The projector includes a projection mapping unit that displays cutting instructions and other information using projection mapping based on information analyzed by the AI ​​analysis unit. For example, the projector can project cutting guidelines onto the work surface. The projector can also display step-by-step cooking procedures. The projector can dynamically change the display content according to the user's progress. The AI ​​analysis unit analyzes the images sent from the camera and recognizes the cooking process being performed by the user. For example, the AI ​​analysis unit can track the user's hand movements using image recognition technology to determine the cutting method. The AI ​​analysis unit can also classify cooking processes using a machine learning algorithm. The AI ​​analysis unit can also analyze the user's actions in real time and generate appropriate instructions. The projection mapping unit controls the projector and determines the display content based on the instructions generated by the AI ​​analysis unit. For example, the projection mapping unit projects cutting guidelines onto the work surface. The projection mapping unit also displays cooking procedures step by step. The projection mapping unit also dynamically changes the display content according to the user's progress. This allows the cooking support system according to the embodiment to enable users to improve their cooking skills while enjoying cooking. For example, users can enjoy cooking while learning the techniques of professional chefs. Furthermore, being praised by the AI ​​increases motivation. Furthermore, incorporating a game element allows users to enjoy cooking.

[0062] The camera is equipped with a depth sensor and can generate 3D models to perform more accurate projection mapping. For example, by adding a depth sensor, the camera can accurately determine the positions of cooking ingredients and utensils as a 3D model. For example, the height and shape of vegetables can be measured in real time and projection mapping can be performed based on that information. The camera can also use the depth sensor to analyze the user's hand movements as a 3D model. For example, it can accurately determine the position and movement of the hand and display cutting guidelines. The camera can also use the depth sensor to adjust the height and angle of the work surface. For example, it can optimize the display position of the projection mapping. This allows for more accurate projection mapping by generating a 3D model.

[0063] By combining AR technology with the projector, the user can wear AR glasses and receive interactive guidance. For example, the projector can link the projector's image to the AR glasses, allowing the user to receive interactive guidance through the AR glasses. For example, cutting guidelines can be displayed on the AR glasses, allowing the user to cut accurately while looking at their hands. The projector can also use AR technology to dynamically change the display content in response to the user's movements. For example, when the user moves their hand, the guidelines displayed on the AR glasses also move. The projector can also use AR technology to track the user's line of sight and display guidelines in front of their line of sight. For example, cutting guidelines can be displayed where the user is looking. In this way, the use of AR technology allows the user to receive more interactive guidance.

[0064] The camera is equipped with an emotion estimation function and can estimate the stress level from the user's facial expressions and movements and encourage the user to take a break at an appropriate time. The camera, for example, analyzes the user's facial expressions to estimate the stress level. For example, it detects wrinkles between the eyebrows or the degree of downward movement of the corners of the mouth, and displays a message encouraging the user to take a break if it determines that stress is increasing. The camera can also analyze the user's movements to estimate the stress level. For example, it detects hand tremors or slow movements, and displays a message encouraging the user to take a break if it determines that stress is increasing. The camera can also collect the user's heart rate and electrodermal activity using a sensor to estimate the stress level. For example, it displays a message encouraging the user to take a break if it determines that stress is increasing based on fluctuations in heart rate. In this way, the user's stress level can be estimated and a message encouraging the user to take a break at an appropriate time can be displayed.

[0065] The camera and projector can be modularized and applied to other household tasks. For example, the camera and projector can be modularized and applied to DIY tasks. For example, furniture assembly procedures can be displayed using projection mapping, allowing the user to follow the steps. The camera and projector can also be applied to cleaning tasks. For example, cleaning procedures and important points can be displayed using projection mapping, allowing the user to clean efficiently. The camera and projector can also be applied to laundry tasks. For example, how to separate laundry and the amount of detergent to use can be displayed using projection mapping, allowing the user to do the laundry accurately. In this way, the modularization of the camera and projector can be applied to other household tasks.

[0066] The image from the projector can be mirrored on a smartphone or tablet, allowing users to receive guidance simultaneously on multiple devices. For example, the image from the projector can be mirrored on a smartphone, allowing users to view the guidance at their fingertips. For example, cutting guidelines can be displayed on the smartphone, allowing users to work while looking at their hands. The image from the projector can also be mirrored on a tablet, allowing users to receive guidance simultaneously on multiple devices. For example, users can check a recipe on another device while looking at the guidelines displayed on the tablet. The image from the projector can also be mirrored using a Wi-Fi or Bluetooth connection. For example, high-resolution images can be mirrored in real time using a Wi-Fi connection. This allows users to receive guidance simultaneously on multiple devices, improving convenience.

[0067] The camera is equipped with an emotion estimation function and can evaluate in real time whether the user is enjoying cooking and make suggestions to increase the enjoyment. For example, the camera can analyze the user's facial expressions to evaluate whether the user is enjoying cooking. For example, it can detect a smile or a sparkle in the eyes and make suggestions to further increase the enjoyment if it determines that the user is enjoying cooking. The camera can also analyze the user's movements to evaluate whether the user is enjoying cooking. For example, it can detect hand movements and speed and make suggestions to further increase the enjoyment if it determines that the user is enjoying cooking. The camera can also analyze the user's voice to evaluate whether the user is enjoying cooking. For example, it can analyze the tone and speed of the voice and make suggestions to further increase the enjoyment if it determines that the user is enjoying cooking. In this way, the user's satisfaction can be improved by evaluating whether the user is enjoying cooking and making suggestions to increase the enjoyment.

[0068] The AI ​​analysis unit can also use voice recognition to determine the cooking stage from the user's voice. For example, the AI ​​analysis unit uses camera footage and voice recognition together to determine the cooking stage from the user's voice. For example, if the user says, "Next, I'll cut the onions," the AI ​​recognizes the instruction and displays appropriate guidance. The AI ​​analysis unit can also use voice recognition technology to analyze the user's voice and determine the cooking stage. For example, it can analyze voice commands and display the next step. The AI ​​analysis unit can also use voice recognition technology to analyze the tone and speed of the user's voice to determine the progress of cooking. For example, if the voice tone is high, it will display the next step quickly. In this way, by using voice recognition in combination, the cooking stage can also be determined from the user's voice.

[0069] The AI ​​analysis unit can learn from past cooking data and adapt to the user's individual cooking style. For example, the AI ​​analysis unit can learn from past cooking data and adapt to the user's individual cooking style. For example, it can learn the user's frequently used cooking methods and procedures and provide guidance based on them. The AI ​​analysis unit can also analyze past cooking data to understand the user's preferences and habits. For example, it can learn the user's preferred seasonings and cooking times and suggest recipes based on them. The AI ​​analysis unit can also use past cooking data to evaluate the user's skill level and provide guidance of an appropriate level of difficulty. For example, it can teach basic procedures to beginners, advanced procedures to intermediate cooks, and professional techniques to advanced cooks. In this way, by learning from past cooking data, it can adapt to the user's individual cooking style.

[0070] The AI ​​analysis unit can use the emotion estimation function to adjust the tone and content of instructions according to the user's emotions, thereby reducing stress. For example, the AI ​​analysis unit can use the emotion estimation function to adjust the tone of instructions according to the user's emotions. For example, if the user is feeling stressed, it can give instructions in a gentle tone. The AI ​​analysis unit can also use the emotion estimation function to adjust the content of instructions according to the user's emotions. For example, if the user is tired, it can suggest a simple procedure. The AI ​​analysis unit can also use the emotion estimation function to provide feedback according to the user's emotions. For example, it can display a message praising the user if they succeed. In this way, stress can be reduced by providing instructions according to the user's emotions.

[0071] The AI ​​analysis unit can also recognize the steps in household chores other than cooking and issue instructions in the same way. For example, the AI ​​analysis unit can be expanded into a system where AI recognizes the steps in laundry and issues appropriate instructions. For example, it can show how to separate laundry and how much detergent to use using projection mapping. The AI ​​analysis unit can also recognize the steps in cleaning and issue appropriate instructions. For example, it can display cleaning procedures and important points using projection mapping. The AI ​​analysis unit can also recognize the steps in tidying up and issue appropriate instructions. For example, it can show how to arrange and store things using projection mapping. This can be applied to household chores other than cooking, making it possible to support general housework.

[0072] The AI ​​analysis unit can share the recognized cooking steps with other users and receive feedback within the community. For example, the AI ​​analysis unit can share the cooking steps recognized by the AI ​​on an online platform and receive feedback from other users. For example, cooking procedures and tips can be shared and opinions exchanged within the community. The AI ​​analysis unit can also share the recognized cooking steps on social media and receive comments and ratings from other users. For example, photos and videos of cooking can be posted and feedback can be received from other users. The AI ​​analysis unit can also save the recognized cooking steps on the cloud and share them with other users. For example, a recipe can be shared on the cloud and advice can be received from other users. In this way, by sharing the recognized cooking steps, feedback can be received from other users.

[0073] The AI ​​analysis unit can use the emotion estimation function to make suggestions to simplify a particular process if the user is having difficulty with that process. For example, the AI ​​analysis unit can use the emotion estimation function to make suggestions to simplify a particular process if the user is having difficulty with that process. For example, changing a difficult cutting method to an easier one. The AI ​​analysis unit can also analyze the user's facial expressions and movements to identify the process that is causing difficulty. For example, if the user's hand movements become slower, a suggestion to simplify that process can be made. The AI ​​analysis unit can also analyze the user's voice to identify the process that is causing difficulty. For example, if the user's voice tone becomes lower, a suggestion to simplify that process can be made. In this way, simplifying the process that the user is having difficulty can reduce stress.

[0074] The AI ​​analysis unit adjusts the difficulty level according to the user's cooking skill and gradually increases the difficulty level to encourage continuous learning. For example, the AI ​​analysis unit evaluates the user's cooking skill and provides guides divided into beginner, intermediate, and advanced difficulty levels. For example, it can teach basic cutting techniques to beginners, advanced cutting techniques to intermediate users, and professional techniques to advanced users. The AI ​​analysis unit can also analyze the user's progress and adjust the difficulty level at the appropriate time. For example, once the user has mastered the basic steps, it will instruct them to move on to the next step. The AI ​​analysis unit can also adjust the difficulty level based on user feedback. For example, if the user finds the task difficult, it will suggest lowering the difficulty level. This allows the difficulty level to be adjusted according to the user's cooking skill, encouraging continuous learning.

[0075] The AI ​​analysis unit can add to the fun by inserting quizzes and mini-games in real time depending on the cooking progress. For example, the AI ​​analysis unit can insert quizzes depending on the cooking progress, allowing the user to learn while having fun. For example, it can ask a quiz about how to cut vegetables, and if the answer is correct, the user can earn points. The AI ​​analysis unit can also insert mini-games depending on the cooking progress. For example, it can hold a time attack or score challenge, allowing the user to enjoy cooking. The AI ​​analysis unit can also display messages praising the user depending on the cooking progress. For example, if the user answers a quiz correctly, it can display "Great!". In this way, inserting quizzes and mini-games increases the fun for the user.

[0076] The AI ​​analysis unit can use the emotion estimation function to evaluate whether the user is enjoying themselves and provide feedback to increase their enjoyment. For example, the AI ​​analysis unit can use the emotion estimation function to evaluate whether the user is enjoying themselves and provide feedback to increase their enjoyment. For example, if the user smiles a lot, it can display "You're having fun!" The AI ​​analysis unit can also analyze the user's facial expressions and movements to evaluate whether they are enjoying themselves. For example, if the user's hand movements are fast, it can determine that the user is enjoying themselves and make suggestions to further increase their enjoyment. The AI ​​analysis unit can also analyze the user's voice to evaluate whether they are enjoying themselves. For example, if the voice tone is high, it can determine that the user is enjoying themselves and make suggestions to further increase their enjoyment. In this way, by evaluating whether the user is enjoying themselves and providing feedback to increase their enjoyment, user satisfaction can be improved.

[0077] The AI ​​analysis unit can introduce game elements into household chores other than cooking, making all housework more enjoyable. The AI ​​analysis unit can, for example, introduce game elements into household chores other than cooking, expanding the system to make all housework more enjoyable. For example, cleaning and laundry procedures can be gamified, allowing points to be earned. The AI ​​analysis unit can also insert quizzes and mini-games depending on the progress of the household chore. For example, it can pose a quiz about cleaning procedures, and points can be earned for correct answers. The AI ​​analysis unit can also display messages praising the user depending on the progress of the household chore. For example, if the user has completed cleaning, it can display "Great!". In this way, by introducing game elements into household chores other than cooking, all housework can be made more enjoyable.

[0078] The AI ​​analysis unit can add a ranking function that allows users to compete against each other, encouraging competition within the community. The AI ​​analysis unit can add a ranking function that allows users to compete against each other, encouraging competition within the community. For example, points can be awarded based on cooking skills and progress, and a ranking can be displayed. The AI ​​analysis unit can also analyze the user's progress and update the ranking in real time. For example, if a user acquires a new skill, the ranking will rise. The AI ​​analysis unit can also display a message praising the user based on the ranking. For example, if the ranking rises, the AI ​​analysis unit can display a message saying "Amazing!". In this way, adding the ranking function encourages competition between users.

[0079] The AI ​​analysis unit can use the emotion estimation function to identify the game elements that the user enjoys most and enhance those elements. For example, the AI ​​analysis unit can use the emotion estimation function to identify the game elements that the user enjoys most and enhance those elements. For example, if a user smiles a lot during a particular mini-game, that mini-game will be provided more frequently. The AI ​​analysis unit can also analyze the user's facial expressions and movements to identify the game elements that the user enjoys. For example, if the user's hand movements are fast, that game element will be enhanced. The AI ​​analysis unit can also analyze the user's voice to identify the game elements that the user enjoys. For example, if the user's voice tone is high, that game element will be enhanced. In this way, the AI ​​analysis unit can identify the game elements that the user enjoys most and enhance those elements, thereby improving user satisfaction.

[0080] The AI ​​analysis unit can provide a sense of accomplishment by analyzing the user's past cooking data and visualizing their progress. For example, the AI ​​analysis unit can analyze the user's past cooking data and build a system that visualizes their progress. For example, it can display the completion rate and speed of past cooking in a graph, providing a sense of accomplishment. The AI ​​analysis unit can also analyze the user's past cooking data and evaluate their skill improvement. For example, it can compare past dishes with current dishes to show skill improvement. The AI ​​analysis unit can also display goals that have been achieved based on the user's past cooking data. For example, if a specific dish is perfectly reproduced, the title of "Master Chef" can be awarded. In this way, the analysis of past cooking data and visualization of progress provide the user with a sense of accomplishment.

[0081] The AI ​​analysis unit can provide personalized feedback according to the user's preferences and goals. For example, the AI ​​analysis unit can provide personalized feedback according to the user's preferences and goals. For example, the AI ​​analysis unit can provide appropriate advice based on the user's favorite dishes or the skills they are aiming for. The AI ​​analysis unit can also analyze the user's progress and provide personalized feedback. For example, if the user has mastered a specific skill, it can provide advice based on that skill. The AI ​​analysis unit can also provide personalized feedback based on the user's feedback. For example, if the user finds something difficult, it can provide advice based on that feedback. In this way, user satisfaction can be improved by providing feedback according to the user's preferences and goals.

[0082] The AI ​​analysis unit can use the emotion estimation function to provide compliments and encouraging messages that correspond to the user's emotions. For example, the AI ​​analysis unit can use the emotion estimation function to provide compliments and encouraging messages that correspond to the user's emotions. For example, when the user is smiling, it can display "Great!". The AI ​​analysis unit can also analyze the user's facial expressions and movements to provide messages that correspond to their emotions. For example, if the user's hand movements are fast, it can display an encouraging message. The AI ​​analysis unit can also analyze the user's voice to provide messages that correspond to their emotions. For example, if the voice tone is high, it can display compliments. In this way, by providing compliments and encouraging messages that correspond to the user's emotions, the user's motivation can be improved.

[0083] The AI ​​analysis unit can introduce a motivation-boosting function to household tasks other than cooking, making all housework more enjoyable. The AI ​​analysis unit can, for example, introduce a motivation-boosting function to household tasks other than cooking, expanding the system to make all housework more enjoyable. For example, it can provide praise and encouraging messages depending on the progress of cleaning or laundry. The AI ​​analysis unit can also analyze the progress of household tasks and display motivation-boosting messages at appropriate times. For example, when a user completes cleaning, it displays "Great!" The AI ​​analysis unit can also provide rewards to users depending on the progress of household tasks. For example, it can award points when cleaning is completed. In this way, by introducing a motivation-boosting function to household tasks other than cooking, all housework can be made more enjoyable.

[0084] The AI ​​analysis unit can provide a sense of accomplishment by awarding badges and titles according to the goals the user achieves. For example, the AI ​​analysis unit can build a system that awards badges and titles according to the goals the user achieves. For example, if a specific dish is perfectly reproduced, the title of "Master Chef" is awarded. The AI ​​analysis unit can also analyze the user's progress and award badges according to the goals achieved. For example, a badge is awarded when a specific skill is mastered. The AI ​​analysis unit can also display messages to provide a sense of accomplishment based on user feedback. For example, if the user achieves a goal, it displays "Amazing!". In this way, a sense of accomplishment is provided by awarding badges and titles according to the goals the user achieves.

[0085] The AI ​​analysis unit can use the emotion estimation function to identify the elements that motivate the user most and strengthen those elements. For example, the AI ​​analysis unit can use the emotion estimation function to identify the elements that motivate the user most and strengthen those elements. For example, if a particular compliment motivates the user, the user will use that word frequently. The AI ​​analysis unit can also analyze the user's facial expressions and movements to identify the elements that motivate the user. For example, if the user's hand movements are fast, that element will be strengthened. The AI ​​analysis unit can also analyze the user's voice to identify the elements that motivate the user. For example, if the voice tone is high, that element will be strengthened. In this way, the elements that motivate the user most can be identified and strengthened, thereby improving the user's motivation.

[0086] The AI ​​analysis unit can analyze cooking videos of professional chefs and show the same steps to users in real time. For example, the AI ​​analysis unit can build a system in which AI analyzes cooking videos of professional chefs and shows the same steps to users in real time. For example, it can analyze the chef's hand movements and cooking steps and provide guidance to the user. The AI ​​analysis unit can also analyze cooking videos of professional chefs and show users step-by-step steps. For example, it can break down the chef's movements and display each step. The AI ​​analysis unit can also analyze cooking videos of professional chefs and give instructions to users at the appropriate time. For example, it can give instructions to users when the chef is about to proceed to the next step. In this way, by analyzing cooking videos of professional chefs and showing users the same steps in real time, users can learn professional techniques.

[0087] The AI ​​analysis unit can evaluate whether the user was able to perfectly replicate the dish and provide feedback. The AI ​​analysis unit, for example, builds a system in which AI evaluates the user's dish and determines whether they were able to perfectly replicate it. For example, it analyzes the appearance and steps of the dish and evaluates it by comparing it with dishes made by professional chefs. The AI ​​analysis unit can also evaluate the user's dish and provide feedback. For example, it can evaluate the completeness and taste of the dish and point out areas for improvement. The AI ​​analysis unit can also evaluate the user's dish and display a message praising it. For example, if the dish is perfectly replicated, it will display "Amazing!". This allows the system to evaluate whether the user was able to perfectly replicate the dish and provide feedback, thereby supporting the user in improving their skills.

[0088] The AI ​​analysis unit can use the emotion estimation function to evaluate whether the user is enjoying the perfect copy experience and provide feedback to increase the enjoyment. For example, the AI ​​analysis unit can use the emotion estimation function to evaluate whether the user is enjoying the perfect copy experience and provide feedback to increase the enjoyment. For example, if the user smiles a lot, it can display "You're having fun!" The AI ​​analysis unit can also analyze the user's facial expressions and movements to evaluate whether the user is enjoying themselves. For example, if the user's hand movements are fast, it can determine that the user is enjoying themselves and make suggestions to further increase the enjoyment. The AI ​​analysis unit can also analyze the user's voice to evaluate whether the user is enjoying themselves. For example, if the user's voice tone is high, it can determine that the user is enjoying themselves and make suggestions to further increase the enjoyment. In this way, by evaluating whether the user is enjoying the perfect copy experience and providing feedback to increase the enjoyment, user satisfaction can be improved.

[0089] The AI ​​analysis unit can be expanded to a system that provides a perfect copy experience of skills other than cooking. The AI ​​analysis unit can be expanded to a system that provides a perfect copy experience of skills other than cooking (e.g., DIY or crafts). For example, the steps for assembling furniture can be displayed using projection mapping, allowing the user to reproduce the same steps. The AI ​​analysis unit can also display the steps for craft work using projection mapping, allowing the user to reproduce the same steps. For example, the steps for handicrafts and crafts can be displayed so that the user can accurately reproduce them. The AI ​​analysis unit can also display the steps for DIY work using projection mapping, allowing the user to reproduce the same steps. For example, the steps for using tools and assembly can be displayed so that the user can accurately reproduce them. This allows the user to learn a variety of skills by providing a perfect copy experience of skills other than cooking.

[0090] The AI ​​analysis unit allows users to share their perfect copy results with each other and receive feedback within the community. The AI ​​analysis unit, for example, builds a system that allows users to share their perfect copy results with each other and receive feedback within the community. For example, users can post photos and videos of their dishes and receive comments and ratings from other users. The AI ​​analysis unit can also share the perfect copy results on social media and receive feedback from other users. For example, users can share cooking procedures and tips and exchange opinions within the community. The AI ​​analysis unit can also save the perfect copy results in the cloud and share them with other users. For example, users can share recipes on the cloud and receive advice from other users. This allows users to share their perfect copy results with each other and receive feedback, thereby supporting the improvement of users' skills.

[0091] The AI ​​analysis unit can use the emotion estimation function to identify the perfect copy experience that the user enjoys most and enhance its elements. For example, the AI ​​analysis unit can use the emotion estimation function to identify the perfect copy experience that the user enjoys most and enhance its elements. For example, if a particular dish shows a lot of smiles, that dish can be served more frequently. The AI ​​analysis unit can also analyze the user's facial expressions and movements to identify the perfect copy experience that the user enjoys. For example, if the user's hand movements are fast, that experience can be enhanced. The AI ​​analysis unit can also analyze the user's voice to identify the perfect copy experience that the user enjoys. For example, if the voice tone is high, that experience can be enhanced. In this way, the perfect copy experience that the user enjoys most can be identified and its elements enhanced, thereby improving user satisfaction.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The cooking support system can further include an audio guide unit. The audio guide unit allows the user to receive instructions while cooking without using their hands. For example, the audio guide unit can provide audio guidance on cutting methods and cooking procedures. The audio guide unit can also change instructions in real time according to the user's progress. For example, when the user is ready to proceed to the next step, it will issue the next instruction by voice. The audio guide unit can also answer the user's questions by voice. For example, in response to the question, "What should I do next?", specific instructions will be provided by voice. This allows the user to receive cooking instructions without using their hands, improving convenience.

[0094] The camera may further include a temperature sensor. The temperature sensor measures the temperature of food in real time and provides guidance for maintaining the appropriate cooking temperature. For example, the temperature sensor can measure the temperature of a frying pan and notify the user when the appropriate temperature has been reached. The temperature sensor can also measure the internal temperature of an oven and provide guidance for maintaining the appropriate doneness. For example, a warning will be displayed if the oven temperature is too high. The temperature sensor can also measure the internal temperature of food and provide guidance for the appropriate cooking time. For example, a warning will be displayed when the internal temperature of meat has reached the appropriate temperature. Thus, the use of temperature sensors allows for more accurate cooking.

[0095] The projector can further include a color recognition function. The color recognition function is used to recognize the color of ingredients and evaluate their freshness and cooking state. For example, the color recognition function can analyze the color of vegetables and display a warning if they are no longer fresh. The color recognition function can also analyze the color of meat and determine the appropriate level of doneness. For example, the color can determine whether the meat is properly cooked and notify the user. The color recognition function can also analyze the color of sauces and soups and evaluate the appropriate cooking state. For example, a warning can be displayed if the sauce is burnt. As a result, the color recognition function can be used to more accurately evaluate the freshness and cooking state of ingredients.

[0096] The camera has an emotion estimation function and can estimate the level of concentration from the user's facial expressions and movements and make suggestions to improve concentration at appropriate times. The camera, for example, analyzes the user's facial expressions to estimate the level of concentration. For example, it detects eye movements and blinking frequency, and makes suggestions to improve concentration if it determines that the level of concentration is decreasing. The camera can also analyze the user's movements to estimate the level of concentration. For example, it makes suggestions to improve concentration if the user's hand movements become sluggish. The camera can also analyze the user's posture to estimate the level of concentration. For example, it makes suggestions to improve concentration if the user's posture is poor. In this way, the efficiency of cooking can be improved by estimating the user's level of concentration and making suggestions to improve concentration at appropriate times.

[0097] The camera and projector may further include an educational mode. The educational mode provides a guide for learning basic cooking techniques and knowledge. For example, the educational mode may provide step-by-step instruction on how to use a knife and basic cutting techniques. The educational mode may also provide information on how to select and store ingredients. For example, the educational mode may teach how to identify fresh vegetables and how to store them properly. The educational mode may also provide advice on nutritional balance and healthy eating. For example, the educational mode may teach how to combine balanced meals and how to calculate calories. Thus, by using the educational mode, a user can improve their skills while learning basic cooking techniques and knowledge.

[0098] The projector's images can further utilize augmented reality (AR) technology to allow users to visually check the progress of cooking. For example, the projector can use AR technology to display 3D models of how to cut ingredients and cooking procedures. The projector can also use AR technology to display virtual guidelines at the user's fingertips. For example, cutting guidelines can be displayed at the user's fingertips, allowing the user to cut accurately. The projector can also use AR technology to display the progress of cooking in real time. For example, the state and temperature of ingredients can be visually confirmed during cooking. Thus, by using AR technology, the user can visually check the progress of cooking as they work.

[0099] The camera has an emotion estimation function and can estimate the user's motivation from their facial expressions and movements and display encouraging messages at appropriate times. The camera, for example, analyzes the user's facial expressions to estimate their motivation. For example, it detects a smile or a sparkle in the eyes and displays an encouraging message if it determines that the user is highly motivated. The camera can also analyze the user's movements to estimate their motivation. For example, if the user's hand movements are fast, it determines that the user is highly motivated and displays an encouraging message. The camera can also analyze the user's voice to estimate their motivation. For example, if the user's voice tone is high, it determines that the user is highly motivated and displays an encouraging message. In this way, the user's motivation can be estimated and encouraging messages can be displayed at appropriate times, thereby maintaining the user's motivation.

[0100] The AI ​​analysis unit can further include a recipe suggestion unit. The recipe suggestion unit suggests appropriate recipes based on the user's preferences and ingredient stock. For example, the recipe suggestion unit can analyze the user's past cooking data and suggest recipes that suit the user's preferences. The recipe suggestion unit can also use a camera to recognize ingredients in the refrigerator and suggest recipes using those ingredients. For example, it can suggest recipes using vegetables or meat in the refrigerator. The recipe suggestion unit can also suggest recipes that take the user's nutritional balance into consideration. For example, it can suggest balanced meals and support a healthy diet. As a result, by using the recipe suggestion unit, users can easily find recipes based on their preferences and ingredient stock.

[0101] The AI ​​analysis unit can further include an ingredient management unit. The ingredient management unit manages ingredients in the user's refrigerator or pantry and keeps track of expiration dates and inventory status. For example, the ingredient management unit can use a camera to recognize ingredients in the refrigerator and record expiration dates. The ingredient management unit can also scan the barcodes of ingredients purchased by the user and manage inventory status. For example, it can scan barcodes to update ingredient inventory. The ingredient management unit can also notify the user of ingredients that are approaching their expiration date and suggest using them sooner. For example, it can suggest recipes using ingredients that are close to their expiration date. As a result, using the ingredient management unit allows the user to manage ingredients efficiently and reduce waste.

[0102] The AI ​​analysis unit can use the emotion estimation function to play music that corresponds to the user's emotions, improving the atmosphere of the cooking. For example, the AI ​​analysis unit uses the emotion estimation function to select music that corresponds to the user's emotions. For example, if the user is relaxed, it will play relaxing music. The AI ​​analysis unit can also analyze the user's facial expressions and movements to play music that corresponds to the emotions. For example, if the user smiles a lot, it will play fun music. The AI ​​analysis unit can also analyze the user's voice to play music that corresponds to the emotions. For example, if the voice tone is high, it will play up-tempo music. In this way, by playing music that corresponds to the user's emotions, the atmosphere of the cooking can be improved and user satisfaction can be increased.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The camera captures the cooking process. For example, the camera is installed on the ceiling and can capture the entire work surface. The camera also uses a wide-angle lens to cover a wide area and transmits the footage to the AI ​​analysis unit in real time. Step 2: The AI ​​analysis unit analyzes the video sent from the camera and recognizes the cooking steps the user is performing. For example, the AI ​​analysis unit uses image recognition technology to track the user's hand movements and determine the cutting method. It also uses machine learning algorithms to classify the cooking steps, analyze the user's actions in real time, and generate appropriate instructions. Step 3: The projector uses projection mapping to display cutting instructions and other information based on the information analyzed by the AI ​​analysis unit. For example, the projector could project cutting guidelines onto a work surface and display step-by-step cooking instructions. The display content can also be dynamically changed according to the user's progress. Step 4: The projection mapping unit controls the projector and determines the display content based on the instructions generated by the AI ​​analysis unit. For example, the projection mapping unit projects cutting guidelines onto the work surface and displays step-by-step cooking instructions. It also dynamically changes the display content according to the user's progress.

[0105] 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.

[0106] 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> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0111] 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.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] 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.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0126] 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.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0141] 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.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] 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 image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] 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.

[0145] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[0146] 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.

[0147] 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.

[0148] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0149] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] 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.

[0155] FIG. 9 illustrates 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 behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions 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.

[0156] 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.

[0157] 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).

[0158] 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 expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, 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 expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0159] 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."

[0160] 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.

[0161] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, 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. A processor also includes 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.

[0166] The hardware resource that executes the specific process 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 process may be a single processor.

[0167] 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.

[0168] 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.

[0169] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0170] 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.

[0171] 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. [Explanation of symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. Equipped with a camera and projector installed on the kitchen ceiling, The camera captures the cooking process, The projector includes a projection mapping unit that displays cutting methods and the like using projection mapping based on the information analyzed by the AI ​​analysis unit. A system characterized by:

2. The camera is Equipped with a depth sensor, it generates 3D models for more accurate projection mapping.

2. The system of claim 1.

3. The camera and the projector Modularized and applicable to other household tasks 2. The system of claim 1.

4. The AI ​​analysis unit Using voice recognition, the cooking process can be determined based on the user's voice.

2. The system of claim 1.

5. The AI ​​analysis unit The difficulty level is adjusted according to the user's cooking skill level, and the difficulty level increases gradually to encourage continuous learning.

2. The system of claim 1.

6. The AI ​​analysis unit Analyzes cooking videos of professional chefs and shows users the same steps in real time 2. The system of claim 1.

7. The camera is Equipped with an emotion estimation function, it estimates the user's stress level from their facial expressions and movements, and encourages them to take a break at the appropriate time.

2. The system of claim 1.

8. The AI ​​analysis unit Adjust the tone and content of instructions according to the user's emotions to reduce stress 2. The system of claim 1.

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