System

Abstract

An object of a system according to an embodiment is to enable even a user unfamiliar with cooking to find a menu suitable for his / her skill level and improve his / her skill.SOLUTION: A system includes a skill evaluation part, a menu proposal part, and a guide provision part. The skill evaluation unit evaluates the cooking skill of the user. The menu proposal part proposes a menu corresponding to the skill level evaluated by the skill evaluation part. A guide providing part provides a step-by-step guide for skill improvement on the basis of the menu proposed by the menu proposal part.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 the problem that it is difficult for users who are unfamiliar with cooking to find a menu that suits their skill level, and no specific guide is provided to help them improve their skills.

[0005] The system according to the embodiment aims to enable even users who are unfamiliar with cooking to find a menu that suits their skill level and improve their cooking skills. [Means for solving the problem]

[0006] The system according to the embodiment includes a skill evaluation unit, a menu suggestion unit, and a guide providing unit. The skill evaluation unit evaluates a user's cooking skill. The menu suggestion unit suggests a menu according to the skill level evaluated by the skill evaluation unit. The guide providing unit provides a step-by-step guide for skill improvement based on the menu suggested by the menu suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment allows even users who are unfamiliar with cooking to find a menu that suits their skill level and improve their cooking skills. [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) The cooking master AI system according to an embodiment of the present invention is a system that provides people who are not familiar with cooking with menu recipes that combine main dishes and side dishes, and supports them in improving their cooking skills. In this way, the cooking master AI system can reliably improve the level of the user's cooking skills.

[0029] The cooking master AI system according to the embodiment includes a skill evaluation unit, a menu suggestion unit, and a guide provision unit. The skill evaluation unit evaluates a user's cooking skills. For example, the skill evaluation unit determines the user's skill level based on the user's cooking history and answers to cooking-related questions. The generation AI receives the user's cooking history and answers to skill-related questions as input, analyzes this information, and evaluates the skill level. The menu suggestion unit proposes menus according to the skill level evaluated by the skill evaluation unit. For example, beginners may start with simple dishes, while intermediate cooks may be suggested to choose slightly more difficult dishes. The generation AI receives information about the user's skill level and preferred ingredients and types of dishes as input, and generates an optimal menu based on this information. The guide provision unit provides a step-by-step guide for skill improvement based on the menu proposed by the menu suggestion unit. For example, the guide provision unit suggests specific practice methods for mastering specific cooking techniques (e.g., how to use knives or seasonings). The generation AI receives information about the user's current skill level and target skills as input, and generates a specific guide based on this information. As a result, the master cooking AI system according to the embodiment can improve the cooking skills of the user.

[0030] The skill evaluation unit can analyze the user's cooking video and evaluate their technical actions. For example, the skill evaluation unit uploads a video of the user cooking, and the generation AI analyzes the video. Specifically, it evaluates technical actions such as how to use a knife and how to adjust the heat, and determines the skill level. This allows the skill level to be accurately determined by analyzing the user's cooking video and evaluating their technical actions.

[0031] The skill evaluation unit can analyze feedback from other users and reflect it in the evaluation. For example, the skill evaluation unit collects feedback from other users about a dish made by the user, and the generation AI analyzes that feedback. For example, the skill level is determined based on comments and evaluation scores. In this way, by analyzing feedback from other users and reflecting it in the evaluation, the accuracy of the skill level evaluation is improved.

[0032] The menu suggestion unit can analyze the user's past cooking history and take into account preferences and allergy information. The menu suggestion unit, for example, analyzes the user's past cooking history and suggests a menu that takes into account preferences and allergy information. For example, a menu that reflects the user's preferences is generated based on data on dishes made in the past. In this way, by analyzing the past cooking history and taking into account preferences and allergy information, it is possible to suggest the optimal menu for the user.

[0033] The menu suggestion unit can suggest menus using seasonal ingredients, taking into consideration the season and local ingredients. The menu suggestion unit, for example, suggests menus using seasonal ingredients, taking into consideration the season and local ingredients. For example, it suggests recipes using fresh vegetables in spring and hot pot dishes in winter. In this way, by suggesting menus using seasonal ingredients, taking into consideration the season and local ingredients, it is possible to provide the user with the best dishes.

[0034] The guide providing unit can provide a visually easy-to-understand explanation using specific videos or images. For example, the guide providing unit adds specific videos or images to the step-by-step guide provided by the generation AI to provide a visually easy-to-understand explanation. For example, it can use videos to show how to use knives and seasonings. This makes it easier for users to understand the cooking steps by providing a visually easy-to-understand explanation using specific videos and images.

[0035] The guide providing unit can monitor the user's progress in real time and provide advice as appropriate. For example, the generation AI of the guide providing unit monitors the user's progress in real time and provides advice as appropriate. For example, it indicates the next step depending on the cooking progress. In this way, by monitoring the user's progress in real time and providing advice as appropriate, the user can proceed with cooking smoothly.

[0036] The guide providing unit can automatically generate practice tasks according to skill level. For example, the guide providing unit uses a generation AI to automatically generate practice tasks according to the user's skill level. For example, beginners can practice basic knife usage, while intermediate users can practice complex cooking techniques. By automatically generating practice tasks according to skill level, users can efficiently improve their skills.

[0037] The guide providing unit can provide a guide customized according to the user's preferences and goals. For example, the guide providing unit uses a generation AI to analyze the user's preferences and goals and provide a customized step-by-step guide. For example, a guide is provided according to the goal of cooking a specific dish. This allows the user to improve their skills more effectively by providing a guide customized according to the user's preferences and goals.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The Cooking Master AI System can further include a nutritional balance evaluation unit. The nutritional balance evaluation unit analyzes the nutritional value of the ingredients and menu selected by the user and suggests a balanced meal. For example, it may suggest adding ingredients to compensate for vitamin or mineral deficiencies. It can also provide menus that take into account nutritional balance according to specific health goals (e.g., dieting or muscle building). This allows users to improve their cooking skills while maintaining a healthy diet.

[0040] The Cooking Master AI System can also be equipped with an ingredient management unit. This unit manages the inventory in the user's refrigerator and pantry, and suggests recipes that prioritize ingredients with upcoming expiration dates. For example, it can scan ingredients in the refrigerator, register them in a database, and automatically list ingredients with upcoming expiration dates. It can also suggest recipes using leftover ingredients to reduce food waste. This allows users to use ingredients efficiently and reduce food loss.

[0041] The Cooking Master AI System can also have a cooking community section. The cooking community section provides a platform where users can share recipes and cooking tips with each other. For example, users can post photos and recipes of their own cooking and receive feedback and advice from other users. Cooking contests based on specific themes can also be held to encourage interaction between users. This allows users to connect with other cooking enthusiasts and improve their cooking skills while increasing their motivation.

[0042] The Cooking Master AI System can also be equipped with a voice assistant unit. The voice assistant unit provides the ability to operate the system with voice commands so that users can operate it without using their hands while cooking. For example, it can read out the next step in a recipe or set a timer. It can also answer questions by voice while cooking. This allows users to operate the system without getting their hands dirty, improving cooking efficiency.

[0043] The Cooking Master AI System can further include an ingredient purchasing support unit. This unit provides a function to link with online shopping sites so that users can easily purchase the ingredients they need. For example, it can automatically list the ingredients needed based on the proposed menu and allow users to complete the purchase procedure with one click. It can also provide sale information and coupons to help users purchase ingredients economically. This allows users to gather the ingredients they need without any hassle.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The skill evaluation unit evaluates the user's cooking skills. For example, it determines the user's skill level based on the user's cooking history and answers to cooking-related questions. The generation AI receives the user's cooking history and answers to skill-related questions as input, analyzes this information, and evaluates the skill level. Step 2: The menu suggestion unit proposes a menu according to the skill level assessed by the skill evaluation unit. For example, beginners can start with simple dishes, while intermediate cooks can be suggested slightly more difficult dishes. The generation AI receives information about the user's skill level and preferred ingredients and types of dishes as input, and generates the optimal menu based on this information. Step 3: The guide provider provides a step-by-step guide for skill improvement based on the menu proposed by the menu suggestor. For example, it suggests specific practice methods for mastering a specific cooking technique (e.g., how to use a knife or seasonings). The generation AI receives information about the user's current skill level and the target skill as input, and generates a specific guide based on this information.

[0046] (Example 2) The cooking master AI system according to an embodiment of the present invention is a system that provides people who are not familiar with cooking with menu recipes that combine main dishes and side dishes, and supports them in improving their cooking skills. In this way, the cooking master AI system can reliably improve the level of the user's cooking skills.

[0047] The cooking master AI system according to the embodiment includes a skill evaluation unit, a menu suggestion unit, and a guide provision unit. The skill evaluation unit evaluates a user's cooking skills. For example, the skill evaluation unit determines the user's skill level based on the user's cooking history and answers to cooking-related questions. The generation AI receives the user's cooking history and answers to skill-related questions as input, analyzes this information, and evaluates the skill level. The menu suggestion unit proposes menus according to the skill level evaluated by the skill evaluation unit. For example, beginners may start with simple dishes, while intermediate cooks may be suggested to choose slightly more difficult dishes. The generation AI receives information about the user's skill level and preferred ingredients and types of dishes as input, and generates an optimal menu based on this information. The guide provision unit provides a step-by-step guide for skill improvement based on the menu proposed by the menu suggestion unit. For example, the guide provision unit suggests specific practice methods for mastering specific cooking techniques (e.g., how to use knives or seasonings). The generation AI receives information about the user's current skill level and target skills as input, and generates a specific guide based on this information. As a result, the master cooking AI system according to the embodiment can improve the cooking skills of the user.

[0048] The skill evaluation unit can analyze the user's cooking video and evaluate their technical actions. For example, the skill evaluation unit uploads a video of the user cooking, and the generation AI analyzes the video. Specifically, it evaluates technical actions such as how to use a knife and how to adjust the heat, and determines the skill level. This allows the skill level to be accurately determined by analyzing the user's cooking video and evaluating their technical actions.

[0049] The skill evaluation unit can analyze feedback from other users and reflect it in the evaluation. For example, the skill evaluation unit collects feedback from other users about a dish made by the user, and the generation AI analyzes that feedback. For example, the skill level is determined based on comments and evaluation scores. In this way, by analyzing feedback from other users and reflecting it in the evaluation, the accuracy of the skill level evaluation is improved.

[0050] The skill evaluation unit can use the emotion estimation function to take into account the user's emotions while cooking in the evaluation. The skill evaluation unit, for example, analyzes the user's facial expressions and voice while cooking and evaluates the emotions using the emotion estimation function. For example, it determines whether the user is enjoying cooking or feeling stressed. The emotion estimation function estimates emotions using facial expression recognition, voice analysis, and sensor data. In this way, by using the emotion estimation function to take into account the emotions while cooking in the evaluation, the skill level evaluation becomes more accurate.

[0051] The menu suggestion unit can analyze the user's past cooking history and take into account preferences and allergy information. The menu suggestion unit, for example, analyzes the user's past cooking history and suggests a menu that takes into account preferences and allergy information. For example, a menu that reflects the user's preferences is generated based on data on dishes made in the past. In this way, by analyzing the past cooking history and taking into account preferences and allergy information, it is possible to suggest the optimal menu for the user.

[0052] The menu suggestion unit can suggest menus using seasonal ingredients, taking into consideration the season and local ingredients. The menu suggestion unit, for example, suggests menus using seasonal ingredients, taking into consideration the season and local ingredients. For example, it suggests recipes using fresh vegetables in spring and hot pot dishes in winter. In this way, by suggesting menus using seasonal ingredients, taking into consideration the season and local ingredients, it is possible to provide the user with the best dishes.

[0053] The menu suggestion unit can use the emotion estimation function to suggest a menu that elicits positive emotions from the user. The menu suggestion unit, for example, uses the emotion estimation function to suggest a menu that will make the user feel positive emotions. For example, the menu suggestion unit analyzes the user's emotion data and suggests recipes that will elicit joy and fun. In this way, the emotion estimation function is used to suggest a menu that will elicit positive emotions, thereby improving the user's cooking experience.

[0054] The guide providing unit can provide a visually easy-to-understand explanation using specific videos or images. For example, the guide providing unit adds specific videos or images to the step-by-step guide provided by the generation AI to provide a visually easy-to-understand explanation. For example, it can use videos to show how to use knives and seasonings. This makes it easier for users to understand the cooking steps by providing a visually easy-to-understand explanation using specific videos and images.

[0055] The guide providing unit can monitor the user's progress in real time and provide advice as appropriate. For example, the generation AI of the guide providing unit monitors the user's progress in real time and provides advice as appropriate. For example, it indicates the next step depending on the cooking progress. In this way, by monitoring the user's progress in real time and providing advice as appropriate, the user can proceed with cooking smoothly.

[0056] The guide providing unit can use the emotion estimation function to provide a guide that elicits positive emotions from the user. The guide providing unit, for example, uses the emotion estimation function to provide a guide that makes the user feel positive emotions. For example, the guide providing unit analyzes the user's emotion data and provides advice that elicits joy and fun. In this way, the emotion estimation function is used to provide a guide that elicits positive emotions, thereby improving the user's cooking experience.

[0057] The guide providing unit can automatically generate practice tasks according to skill level. For example, the guide providing unit uses a generation AI to automatically generate practice tasks according to the user's skill level. For example, beginners can practice basic knife usage, while intermediate users can practice complex cooking techniques. By automatically generating practice tasks according to skill level, users can efficiently improve their skills.

[0058] The guide providing unit can provide a guide customized according to the user's preferences and goals. For example, the guide providing unit uses a generation AI to analyze the user's preferences and goals and provide a customized step-by-step guide. For example, a guide is provided according to the goal of cooking a specific dish. This allows the user to improve their skills more effectively by providing a guide customized according to the user's preferences and goals.

[0059] The guide providing unit can use the emotion estimation function to provide a guide that elicits positive emotions from the user in real time. The guide providing unit, for example, uses the emotion estimation function to provide a guide that makes the user feel positive emotions in real time. For example, the guide providing unit analyzes the user's emotion data and provides advice that elicits joy and fun. In this way, the user's cooking experience is improved by using the emotion estimation function to provide a guide that elicits positive emotions in real time.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The Cooking Master AI System can further include a nutritional balance evaluation unit. The nutritional balance evaluation unit analyzes the nutritional value of the ingredients and menu selected by the user and suggests a balanced meal. For example, it may suggest adding ingredients to compensate for vitamin or mineral deficiencies. It can also provide menus that take into account nutritional balance according to specific health goals (e.g., dieting or muscle building). This allows users to improve their cooking skills while maintaining a healthy diet.

[0062] The Cooking Master AI System can also be equipped with an ingredient management unit. This unit manages the inventory in the user's refrigerator and pantry, and suggests recipes that prioritize ingredients with upcoming expiration dates. For example, it can scan ingredients in the refrigerator, register them in a database, and automatically list ingredients with upcoming expiration dates. It can also suggest recipes using leftover ingredients to reduce food waste. This allows users to use ingredients efficiently and reduce food loss.

[0063] The Cooking Master AI System can also have a cooking community section. The cooking community section provides a platform where users can share recipes and cooking tips with each other. For example, users can post photos and recipes of their own cooking and receive feedback and advice from other users. Cooking contests based on specific themes can also be held to encourage interaction between users. This allows users to connect with other cooking enthusiasts and improve their cooking skills while increasing their motivation.

[0064] The Cooking Master AI System can also be equipped with a voice assistant unit. The voice assistant unit provides the ability to operate the system with voice commands so that users can operate it without using their hands while cooking. For example, it can read out the next step in a recipe or set a timer. It can also answer questions by voice while cooking. This allows users to operate the system without getting their hands dirty, improving cooking efficiency.

[0065] The Cooking Master AI System can further include an ingredient purchasing support unit. This unit provides a function to link with online shopping sites so that users can easily purchase the ingredients they need. For example, it can automatically list the ingredients needed based on the proposed menu and allow users to complete the purchase procedure with one click. It can also provide sale information and coupons to help users purchase ingredients economically. This allows users to gather the ingredients they need without any hassle.

[0066] The Cooking Master AI system can also use its emotion estimation function to suggest relaxing meals to reduce the user's stress level. For example, if the user is feeling stressed, it can suggest dishes that use herbal tea or aromas that have a relaxing effect. The emotion estimation function can also be used to play music or environmental sounds that will help the user relax. This allows the user to relax and reduce stress through cooking.

[0067] The Cooking Master AI System can also use its emotion estimation function to provide encouraging messages to boost the user's motivation. For example, if the user feels tired or frustrated while cooking, the emotion estimation function can detect that emotion and display an encouraging message. Sharing successful experiences can also boost the user's confidence. This allows the user to continue cooking with a positive attitude, leading to improved skills.

[0068] The Cooking Master AI System can also use its emotion estimation function to adjust the difficulty of the cooking depending on the user's emotions. For example, if the user is tired, it will suggest an easy dish, and if they are energetic, it will suggest a more challenging dish. The emotion estimation function can also be used to suggest dishes of moderate difficulty so that the user can continue to enjoy cooking. This allows the user to enjoy cooking without straining themselves, leading to skill improvement.

[0069] The Cooking Master AI system can also use its emotion estimation function to provide a personalized cooking experience based on the user's emotions. For example, if the user is feeling a certain emotion, it can suggest dishes and recipes that match that emotion. For example, if the user is sad, it can suggest dishes that will brighten their mood, and if they are happy, it can suggest dishes that will make them even more happy. This allows users to enjoy the optimal cooking experience according to their emotions.

[0070] The Cooking Master AI system can also use its emotion estimation function to suggest a food presentation method based on the user's emotions. For example, if a user is celebrating a special occasion, the emotion estimation function can detect that emotion and suggest a special presentation method. Alternatively, if the user wants to relax, the system can suggest a simple, calm presentation method. This allows users to pay more attention to the appearance of their food and enjoy a more satisfying meal.

[0071] The processing flow of the second embodiment will be briefly explained below.

[0072] Step 1: The skill evaluation unit evaluates the user's cooking skills. For example, it determines the user's skill level based on the user's cooking history and answers to cooking-related questions. The generation AI receives the user's cooking history and answers to skill-related questions as input, analyzes this information, and evaluates the skill level. Step 2: The menu suggestion unit proposes a menu according to the skill level assessed by the skill evaluation unit. For example, beginners can start with simple dishes, while intermediate cooks can be suggested slightly more difficult dishes. The generation AI receives information about the user's skill level and preferred ingredients and types of dishes as input, and generates the optimal menu based on this information. Step 3: The guide provider provides a step-by-step guide for skill improvement based on the menu proposed by the menu suggestor. For example, it suggests specific practice methods for mastering a specific cooking technique (e.g., how to use a knife or seasonings). The generation AI receives information about the user's current skill level and the target skill as input, and generates a specific guide based on this information.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0077] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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).

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0107] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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."

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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]

[0140] 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. a skill evaluation unit that evaluates the cooking skills of a user; a menu suggestion unit that suggests a menu according to the skill level evaluated by the skill evaluation unit; a guide providing unit that provides a step-by-step guide for skill improvement based on the menu proposed by the menu suggestion unit. A system characterized by:

2. The skill evaluation unit Analyzing the user's cooking video and evaluating technical actions 2. The system of claim 1.

3. The menu suggestion unit Analyze the user's past cooking history and take into consideration the user's preferences and allergy information 2. The system of claim 1.

4. The guide providing unit Use specific videos or images to provide easy-to-understand visual explanations 2. The system of claim 1.

5. The skill evaluation unit Using an emotion estimation function, the user's emotions while cooking are taken into account in the evaluation.

2. The system of claim 1.

6. The menu suggestion unit Using an emotion estimation function, the menu is suggested to elicit positive emotions from the user.

2. The system of claim 1.

7. The guide providing unit Using an emotion estimation function, a guide is provided to elicit positive emotions from the user.

2. The system of claim 1.

8. The guide providing unit Using emotion estimation function, provide guidance in real time to elicit positive emotions from the user.

2. The system of claim 1.

Images