system

Abstract

The system according to this embodiment aims to accurately reproduce traditional cooking recipes and provide information about their origins and background. [Solution] The system according to the embodiment comprises an analysis unit, a generation unit, a reproduction unit, and a serving unit. The analysis unit analyzes a recipe. The generation unit generates specific procedures based on the data analyzed by the analysis unit. The reproduction unit reproduces the dish based on the procedures generated by the generation unit. The serving unit analyzes research or historical documents and provides information about the origin and background of traditional dishes.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to accurately reproduce the recipe of traditional cuisine and provide information about its origin and background.

[0005] The system according to the embodiment aims to accurately reproduce the recipe of traditional cuisine and provide information about its origin and background.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a generation unit, a reproduction unit, and a serving unit. The analysis unit analyzes a recipe. The generation unit generates specific procedures based on the data analyzed by the analysis unit. The reproduction unit reproduces the dish based on the procedures generated by the generation unit. The serving unit analyzes research or historical documents and provides information about the origin and background of traditional dishes. [Effects of the Invention]

[0007] The system according to this embodiment can accurately reproduce traditional cooking recipes and provide information about their origins and background. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[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 2 eight, a RAM 30, and a storage 32. The processor 2 eight, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The cooking reproduction system according to an embodiment of the present invention is a system that uses AI to analyze traditional cooking recipes and reproduce their taste and techniques more accurately. This cooking reproduction system analyzes traditional cooking recipes, extracting data by analyzing ingredients and cooking methods in detail. Next, the AI ​​generates specific procedures for reproducing the taste and techniques of the dish based on the extracted data. Furthermore, the AI ​​conducts research and analyzes historical documents to provide information about the origin and background of traditional dishes. For example, the AI ​​analyzes historical documents to identify the historical background of a particular traditional dish, and based on this information, users can understand not only the taste and techniques of the dish but also its origin and background. This system promotes the preservation and dissemination of traditional dishes and deepens users' understanding of the cuisine. As a result, the cooking reproduction system can accurately reproduce the taste and techniques of traditional dishes and provide information about their origin and background.

[0029] The cooking reproduction system according to this embodiment comprises an analysis unit, a generation unit, a reproduction unit, and a serving unit. The analysis unit analyzes a recipe for a traditional dish. The analysis unit analyzes the recipe using, for example, natural language processing technology to extract ingredients and cooking methods. For example, the analysis unit uses morphological analysis to segment the text of the recipe and identify each ingredient and cooking method. The analysis unit can also use grammatical analysis to analyze the sentence structure of the recipe and extract cooking steps. Furthermore, the analysis unit can use semantic analysis to understand the meaning of the recipe and identify the relationships between ingredients and cooking methods. The generation unit generates specific steps based on the data analyzed by the analysis unit. For example, the generation unit generates cooking steps step by step based on the extracted data. For example, the generation unit adjusts the level of detail of each step to generate steps that are easy for the user to understand. The generation unit can also select an algorithm to use and generate the optimal steps. The reproduction unit reproduces the dish based on the steps generated by the generation unit. The reproduction unit reproduces the dish considering, for example, the use of cooking utensils and cooking time. For example, the reproduction unit provides procedures for reproducing a dish using specific cooking utensils. The reproduction unit can also provide procedures for optimizing cooking time and efficiently reproducing the dish. The provision unit analyzes research and historical documents to provide information about the origins and background of traditional dishes. For example, the provision unit uses image recognition technology to analyze historical documents and identify the historical background of traditional dishes. For example, the provision unit uses object detection technology to identify important information within historical documents and reflect it in the analysis results. The provision unit can also use character recognition technology to analyze the text of historical documents and identify the historical background. As a result, the cooking reproduction system according to this embodiment can accurately reproduce the taste and techniques of traditional dishes and provide information about their origins and background.

[0030] The analysis unit can analyze recipes using natural language processing techniques and extract ingredients and cooking methods. Natural language processing techniques include, for example, morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit can use morphological analysis to segment the text of a recipe and identify each ingredient and cooking method. For example, morphological analysis is a technique that divides text into words and identifies the part of speech of each word. The analysis unit can also use grammatical analysis to analyze the sentence structure of a recipe and extract cooking steps. For example, grammatical analysis is a technique that analyzes the structure of a sentence and identifies sentence elements such as subject, predicate, and object. Furthermore, the analysis unit can use semantic analysis to understand the meaning of a recipe and identify the relationships between ingredients and cooking methods. For example, semantic analysis is a technique that analyzes the meaning of text and identifies the meaning of words and phrases. In this way, by using natural language processing techniques, the ingredients and cooking methods of a recipe can be accurately extracted. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input recipe text data into a generating AI, which can then extract ingredients and cooking methods.

[0031] The generation unit can generate cooking instructions step by step based on the extracted data. The generation unit can, for example, adjust the level of detail of each step to generate instructions that are easy for the user to understand. The generation unit can, for example, select an algorithm to use and generate the optimal procedure. For example, the generation unit can use a machine learning algorithm to generate the optimal procedure. The generation unit can, for example, use a machine learning algorithm to generate the optimal procedure. This makes it easier to reproduce dishes by generating specific cooking instructions based on the extracted data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the extracted data into a generation AI and have the generation AI generate the cooking instructions.

[0032] The reproduction unit can reproduce a dish based on the generated procedure. The reproduction unit reproduces a dish considering, for example, the use of cooking utensils and cooking time. For example, the reproduction unit provides a procedure for reproducing a dish using specific cooking utensils. The reproduction unit can also provide a procedure for reproducing a dish efficiently by optimizing the cooking time. This allows for the accurate reproduction of the taste and techniques of traditional dishes by reproducing them based on the generated procedure. Some or all of the above-described processes in the reproduction unit may be performed using, for example, AI, or not using AI. For example, the reproduction unit can input the generated procedure into a generating AI and have the generating AI perform the dish reproduction.

[0033] The service provider can analyze ancient documents using image recognition technology to identify the historical background of traditional cuisine. Image recognition technology includes, for example, object detection and character recognition. The service provider can, for example, use object detection technology to identify important information within ancient documents and reflect it in the analysis results. For example, object detection technology is a technology that detects specific objects in an image and identifies their position and shape. The service provider can also analyze the text of ancient documents using character recognition technology to identify the historical background. For example, character recognition technology is a technology that recognizes characters in an image and converts them into text data. In this way, by using image recognition technology, the historical background of traditional cuisine can be accurately identified from ancient documents. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input image data of ancient documents into a generating AI and have the generating AI perform the identification of the historical background.

[0034] The information provider can provide information to the user based on the identified historical context. For example, the information provider can provide text information, image information, audio information, etc., based on the identified historical context. For example, the information provider can provide text information based on the identified historical context. For example, the information provider can provide text information based on the identified historical context. The information provider can also provide image information based on the identified historical context. For example, the information provider can also provide image information based on the identified historical context. The information provider can also provide audio information based on the identified historical context. For example, the information provider can also provide audio information based on the identified historical context. By providing information based on the identified historical context, users can deepen their understanding of the origins and background of traditional cuisine. Some or all of the processing described above in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input data on the identified historical context into a generating AI and have the generating AI perform the information provision.

[0035] The analysis unit can improve the accuracy of its analysis of recipes based on regional food culture or customs. For example, the analysis unit can classify the ingredients and cooking methods of recipes based on regional food culture and reflect this in the analysis results. For example, the analysis unit can classify specific ingredients and cooking methods based on regional food culture and reflect this in the analysis results. For example, the analysis unit can identify dishes related to regional customs and festivals and include their background information in the analysis results. For example, the analysis unit can identify dishes related to regional customs and festivals and include their background information in the analysis results. For example, the analysis unit can customize the analysis results by considering regionally specific ingredients and cooking techniques. For example, the analysis unit can customize the analysis results by considering regionally specific ingredients and cooking techniques. This improves the accuracy of the analysis by considering regional food culture and customs. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on regional food culture and customs into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0036] The analysis unit can perform analysis based on seasonal variations in ingredients when analyzing recipes. For example, the analysis unit analyzes the ingredients of a recipe, taking into account the availability of seasonal ingredients. For example, the analysis unit analyzes the ingredients of a recipe, taking into account the availability of seasonal ingredients. For example, the analysis unit considers seasonal variations in flavor and nutritional value of ingredients and reflects these in the analysis results. For example, the analysis unit considers seasonal variations in flavor and nutritional value and reflects these in the analysis results. For example, the analysis unit considers seasonal price fluctuations of ingredients and proposes cost-effective recipes. For example, the analysis unit considers seasonal price fluctuations of ingredients and proposes cost-effective recipes. By considering seasonal variations in ingredients, more realistic analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input seasonal ingredient data into a generating AI and have the generating AI perform the analysis.

[0037] The analysis unit can prioritize analyzing recipes that are highly relevant by considering the user's eating history. For example, the analysis unit prioritizes analyzing recipes that are highly relevant based on the user's cooking history. For example, the analysis unit prioritizes analyzing recipes that are highly relevant based on the user's cooking history. For example, the analysis unit identifies preferred ingredients and cooking methods from the user's eating history and analyzes recipes based on that. For example, the analysis unit identifies preferred ingredients and cooking methods from the user's eating history and analyzes recipes based on that. For example, the analysis unit analyzes the user's eating history and prioritizes analyzing recipes that take nutritional balance into consideration. For example, the analysis unit analyzes the user's eating history and prioritizes analyzing recipes that take nutritional balance into consideration. In this way, by considering the user's eating history, highly relevant recipes can be prioritized. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's meal history data into the generating AI and have the generating AI perform an analysis to identify highly relevant recipes.

[0038] The analysis unit can perform recipe analysis while taking the user's allergy information into consideration. For example, the analysis unit can prioritize analyzing recipes that do not contain allergens based on the user's allergy information. For example, the analysis unit can prioritize analyzing recipes that do not contain allergens based on the user's allergy information. For example, the analysis unit can suggest alternative ingredients while taking the user's allergy information into consideration. For example, the analysis unit can suggest alternative ingredients while taking the user's allergy information into consideration. For example, the analysis unit can analyze cooking methods that minimize the risk of allergens based on the user's allergy information. For example, the analysis unit can analyze cooking methods that minimize the risk of allergens based on the user's allergy information. In this way, safe recipes can be provided by taking the user's allergy information into consideration. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's allergy information data into a generating AI and have the generating AI perform an analysis that takes the allergy information into consideration.

[0039] The generation unit can adjust the level of detail in the cooking instructions based on the difficulty of the dish. For example, for simple dishes, the generation unit provides only basic instructions. For example, for difficult dishes, the generation unit provides detailed instructions and points to note. For example, for intermediate level dishes, the generation unit provides instructions with a moderate level of detail. By adjusting the level of detail in the instructions based on the difficulty of the dish, cooking instructions tailored to the user's skill level can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input cooking difficulty data into a generation AI and have the generation AI adjust the level of detail in the instructions.

[0040] The generation unit can apply different generation algorithms depending on the category of cuisine when generating cooking procedures. For example, in the case of Japanese cuisine, the generation unit generates procedures based on traditional techniques and ingredients. For example, in the case of Western cuisine, the generation unit generates procedures based on modern cooking techniques and ingredients. For example, in the case of Chinese cuisine, the generation unit generates procedures based on specific cooking techniques and ingredients. By applying different generation algorithms depending on the category of cuisine, more appropriate cooking procedures can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the category data of the cuisine into a generation AI and have the generation AI execute the application of the generation algorithm.

[0041] The generation unit can determine the priority of cooking procedures based on the user's cooking experience when generating cooking procedures. For example, if the user is a beginner, the generation unit will prioritize providing basic procedures. For example, if the user is an intermediate cook, the generation unit will prioritize providing advanced procedures. For example, if the user is an advanced cook, the generation unit will prioritize providing specialized procedures. By determining the priority of procedures based on the user's cooking experience, the generation unit can provide more appropriate cooking procedures. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's cooking experience data into a generation AI and have the generation AI determine the priority of procedures.

[0042] The generation unit can customize cooking procedures by taking into account the user's kitchen equipment. For example, the generation unit customizes the procedures based on the cooking utensils the user owns. For example, the generation unit customizes the procedures based on the size and layout of the user's kitchen. For example, the generation unit provides the optimal procedures based on the user's cooking environment. For example, the generation unit provides the optimal procedures based on the user's cooking environment. This allows for the provision of more realistic cooking procedures by taking into account the user's kitchen equipment. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's kitchen equipment data into a generation AI and have the generation AI perform the procedure customization.

[0043] The reproduction unit can analyze the user's past cooking history to select the optimal reproduction method when reproducing a dish. For example, the reproduction unit can select the optimal reproduction method based on the user's past cooking history. For example, the reproduction unit can select the optimal reproduction method based on the user's past cooking history. For example, the reproduction unit can identify the user's preferred cooking method from the user's past cooking history and select a reproduction method based on that. For example, the reproduction unit can identify the user's preferred cooking method from the user's past cooking history and select a reproduction method based on that. For example, the reproduction unit can analyze the user's past cooking history and select the most efficient reproduction method. For example, the reproduction unit can analyze the user's past cooking history and select the most efficient reproduction method. In this way, by analyzing the user's past cooking history, the optimal reproduction method can be provided. Some or all of the above processing in the reproduction unit may be performed using AI, for example, or without AI. For example, the reproduction unit can input the user's past cooking history data into a generating AI and have the generating AI select the optimal reproduction method.

[0044] The reproduction unit can customize the reproduction procedure when reproducing a dish, taking into account the user's current ingredient inventory. For example, the reproduction unit customizes the reproduction procedure based on the user's current ingredient inventory. For example, the reproduction unit customizes the reproduction procedure based on the user's current ingredient inventory. For example, the reproduction unit suggests alternative ingredients based on the user's ingredient inventory. For example, the reproduction unit suggests alternative ingredients based on the user's ingredient inventory. For example, the reproduction unit provides the optimal reproduction procedure, taking into account the user's ingredient inventory. For example, the reproduction unit provides the optimal reproduction procedure, taking into account the user's ingredient inventory. This allows for the provision of a more realistic reproduction procedure by considering the user's current ingredient inventory. Some or all of the above processing in the reproduction unit may be performed using AI, for example, or without AI. For example, the reproduction unit can input the user's ingredient inventory data into a generating AI and have the generating AI perform the customization of the reproduction procedure.

[0045] The reproduction unit can select the optimal reproduction method when reproducing a dish, taking into account the user's geographical location information. For example, the reproduction unit selects a reproduction method based on the user's geographical location information, taking into account ingredients and cooking methods specific to the region. For example, the reproduction unit selects a reproduction method based on the user's geographical location information, taking into account ingredients and cooking methods specific to the region. For example, the reproduction unit proposes the optimal way to obtain ingredients, taking into account the user's geographical location information. For example, the reproduction unit proposes the optimal way to obtain ingredients, taking into account the user's geographical location information. For example, the reproduction unit selects a reproduction method suitable for the local climate and environment, taking into account the user's geographical location information. For example, the reproduction unit selects a reproduction method suitable for the local climate and environment, taking into account the user's geographical location information. This allows for the provision of more realistic reproduction methods by considering the user's geographical location information. Some or all of the above processing in the reproduction unit may be performed using AI, for example, or without AI. For example, the reproduction unit can input the user's geographical location information data into a generating AI and have the generating AI select the optimal reproduction method.

[0046] The reproduction unit can adjust the reproduction procedure when reproducing a dish, taking into account the user's dietary restrictions. For example, the reproduction unit can provide an allergen-free reproduction procedure based on the user's dietary restrictions. For example, the reproduction unit can provide an allergen-free reproduction procedure based on the user's dietary restrictions. For example, the reproduction unit can suggest alternative ingredients, taking into account the user's dietary restrictions. For example, the reproduction unit can suggest alternative ingredients, taking into account the user's dietary restrictions. For example, the reproduction unit can provide a reproduction procedure that takes nutritional balance into account, based on the user's dietary restrictions. For example, the reproduction unit can provide a reproduction procedure that takes nutritional balance into account, based on the user's dietary restrictions. This makes it possible to provide a safer and more appropriate reproduction procedure by taking the user's dietary restrictions into account. Some or all of the above processing in the reproduction unit may be performed using AI, for example, or without AI. For example, the reproduction unit can input the user's dietary restriction data into a generating AI and have the generating AI perform the adjustment of the reproduction procedure.

[0047] The information provider can supplement current information by referencing past data when providing historical background. For example, the information provider can supplement and provide current information based on ancient documents and research data. For example, the information provider can supplement and provide current information based on ancient documents and research data. For example, the information provider can refer to past cookbooks and recipes and provide information by comparing them with modern cooking methods. For example, the information provider can refer to past cookbooks and recipes and provide information by comparing them with modern cooking methods. For example, the information provider can supplement and provide information on the origins and changes of dishes based on historical events and cultural backgrounds. For example, the information provider can supplement and provide information on the origins and changes of dishes based on historical events and cultural backgrounds. This allows for more accurate supplementation of current information by referencing past data. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input past data into a generating AI and have the generating AI perform the supplementation of current information.

[0048] The information provider can apply different information provision methods for each category of cuisine when providing historical background information. For example, in the case of Japanese cuisine, the information provider will provide information that emphasizes traditional techniques and cultural background. For example, in the case of Western cuisine, the information provider will provide information that emphasizes historical events and regional changes. For example, in the case of Chinese cuisine, the information provider will provide information that emphasizes regional characteristics and historical background. By applying different information provision methods for each category of cuisine, it becomes possible to provide more appropriate information. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input the category data of cuisine into a generating AI and have the generating AI execute the application of information provision methods.

[0049] The information provider can prioritize information based on the user's interests when providing historical background. For example, if the user is interested in a particular region, the information provider will prioritize providing information related to that region. For example, if the user is interested in a particular era, the information provider will prioritize providing information related to that era. For example, if the user is interested in a particular era, the information provider will prioritize providing information related to that era. For example, if the user is interested in a particular dish, the information provider will prioritize providing information related to that dish. For example, if the user is interested in a particular dish, the information provider will prioritize providing information related to that dish. By prioritizing information based on the user's interests, it becomes possible to provide more appropriate information. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input user interest data into a generating AI and have the generating AI perform the determination of information prioritization.

[0050] The information provider can improve the accuracy of the information by referring to relevant literature and materials when providing historical background. For example, the information provider can improve the accuracy of the information by referring to relevant academic papers and research materials. For example, the information provider can improve the accuracy of the information by referring to relevant academic papers and research materials. For example, the information provider can improve the reliability of the information by analyzing historical literature and ancient documents. For example, the information provider can improve the reliability of the information by analyzing historical literature and ancient documents. For example, the information provider can improve the accuracy of the information by referring to expert opinions and explanations. For example, the information provider can improve the accuracy of the information by referring to expert opinions and explanations. In this way, the accuracy of the information can be improved by referring to relevant literature and materials. Some or all of the above processing in the information provider may be performed using AI, for example, or without using AI. For example, the information provider can input data from relevant literature and materials into a generating AI and have the generating AI perform the information accuracy improvement.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The analytics unit can prioritize analyzing recipes that have been well-received in the past, based on the user's meal history. For example, the analytics unit can identify recipes that the user has previously given high ratings to and suggest new recipes based on that. The analytics unit can also analyze the user's preferences for specific ingredients and cooking methods from their meal history and customize recipes accordingly. Furthermore, the analytics unit can analyze the user's meal history and suggest recipes that take nutritional balance into consideration. This allows for more personalized recipe analysis by taking into account the user's past meal history.

[0053] The generation unit can adjust the difficulty of the cooking procedure based on the user's cooking skill level. For example, it can provide basic procedures for beginners and more advanced procedures for intermediate users. For advanced users, it can provide recipes that include specialized techniques and detailed procedures. Furthermore, the generation unit can dynamically adjust the level of detail of the procedure based on user feedback. This makes it possible to provide the optimal cooking procedure according to the user's skill level.

[0054] The recipe reproduction unit can suggest alternative ingredients, taking into account the user's current ingredient inventory. For example, if a particular ingredient is in short supply, the unit will suggest a substitute and adjust the recipe accordingly. The reproduction unit can also monitor the user's ingredient inventory in real time and provide the optimal reproduction procedure. Furthermore, it can suggest cost-effective recipes based on the user's ingredient inventory. This makes it possible to reproduce dishes while making the most of the user's ingredient inventory.

[0055] The service provider can provide information based on the user's geographical location, taking into account local ingredients and cooking methods. For example, if a user is in a specific region, it can suggest recipes using ingredients available in that area. The service provider can also suggest cooking methods suitable for the local climate and environment. Furthermore, it can provide background information based on local food culture and customs. This enables the provision of more realistic and appropriate information that takes the user's geographical location into account.

[0056] The information provider can improve the accuracy of the information by referring to relevant literature and materials when providing historical background. For example, they can improve the accuracy of the information by referring to relevant academic papers and research materials. The information provider can also enhance the reliability of the information by analyzing historical literature and ancient documents. Furthermore, the information provider can improve the accuracy of the information by referring to expert opinions and explanations. In this way, the accuracy of the information can be improved by referring to relevant literature and materials.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The analysis unit analyzes traditional cooking recipes. The analysis unit uses natural language processing techniques to analyze the recipes and extract ingredients and cooking methods. Specifically, it uses morphological analysis to segment the recipe text and identify each ingredient and cooking method. It can also use grammatical analysis to analyze the sentence structure of the recipe and extract cooking steps. Furthermore, it uses semantic analysis to understand the meaning of the recipe and identify the relationships between ingredients and cooking methods. Step 2: The generation unit generates specific procedures based on the data analyzed by the analysis unit. The generation unit generates cooking procedures step by step based on the extracted data. The level of detail for each step is adjusted to generate procedures that are easy for the user to understand. It can also select the algorithm to be used and generate the optimal procedure. Step 3: The reproduction unit reproduces the dish based on the procedure generated by the generation unit. The reproduction unit reproduces the dish taking into account the use of cooking utensils and cooking time. It can also provide a procedure for reproducing the dish using specific cooking utensils, optimizing cooking time, and providing a procedure for efficiently reproducing the dish. Step 4: The providing department analyzes research and historical documents to provide information about the origins and background of traditional dishes. The providing department uses image recognition technology to analyze historical documents and identify the historical background of traditional dishes. It also uses object detection technology to identify important information within historical documents and reflects it in the analysis results. Furthermore, it can also use character recognition technology to analyze the text of historical documents and identify the historical background.

[0059] (Example of form 2) The cooking reproduction system according to an embodiment of the present invention is a system that uses AI to analyze traditional cooking recipes and reproduce their taste and techniques more accurately. This cooking reproduction system analyzes traditional cooking recipes, extracting data by analyzing ingredients and cooking methods in detail. Next, the AI ​​generates specific procedures for reproducing the taste and techniques of the dish based on the extracted data. Furthermore, the AI ​​conducts research and analyzes historical documents to provide information about the origin and background of traditional dishes. For example, the AI ​​analyzes historical documents to identify the historical background of a particular traditional dish, and based on this information, users can understand not only the taste and techniques of the dish but also its origin and background. This system promotes the preservation and dissemination of traditional dishes and deepens users' understanding of the cuisine. As a result, the cooking reproduction system can accurately reproduce the taste and techniques of traditional dishes and provide information about their origin and background.

[0060] The cooking reproduction system according to this embodiment comprises an analysis unit, a generation unit, a reproduction unit, and a serving unit. The analysis unit analyzes a recipe for a traditional dish. The analysis unit analyzes the recipe using, for example, natural language processing technology to extract ingredients and cooking methods. For example, the analysis unit uses morphological analysis to segment the text of the recipe and identify each ingredient and cooking method. The analysis unit can also use grammatical analysis to analyze the sentence structure of the recipe and extract cooking steps. Furthermore, the analysis unit can use semantic analysis to understand the meaning of the recipe and identify the relationships between ingredients and cooking methods. The generation unit generates specific steps based on the data analyzed by the analysis unit. For example, the generation unit generates cooking steps step by step based on the extracted data. For example, the generation unit adjusts the level of detail of each step to generate steps that are easy for the user to understand. The generation unit can also select an algorithm to use and generate the optimal steps. The reproduction unit reproduces the dish based on the steps generated by the generation unit. The reproduction unit reproduces the dish considering, for example, the use of cooking utensils and cooking time. For example, the reproduction unit provides procedures for reproducing a dish using specific cooking utensils. The reproduction unit can also provide procedures for optimizing cooking time and efficiently reproducing the dish. The provision unit analyzes research and historical documents to provide information about the origins and background of traditional dishes. For example, the provision unit uses image recognition technology to analyze historical documents and identify the historical background of traditional dishes. For example, the provision unit uses object detection technology to identify important information within historical documents and reflect it in the analysis results. The provision unit can also use character recognition technology to analyze the text of historical documents and identify the historical background. As a result, the cooking reproduction system according to this embodiment can accurately reproduce the taste and techniques of traditional dishes and provide information about their origins and background.

[0061] The analysis unit can analyze recipes using natural language processing techniques and extract ingredients and cooking methods. Natural language processing techniques include, for example, morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit can use morphological analysis to segment the text of a recipe and identify each ingredient and cooking method. For example, morphological analysis is a technique that divides text into words and identifies the part of speech of each word. The analysis unit can also use grammatical analysis to analyze the sentence structure of a recipe and extract cooking steps. For example, grammatical analysis is a technique that analyzes the structure of a sentence and identifies sentence elements such as subject, predicate, and object. Furthermore, the analysis unit can use semantic analysis to understand the meaning of a recipe and identify the relationships between ingredients and cooking methods. For example, semantic analysis is a technique that analyzes the meaning of text and identifies the meaning of words and phrases. In this way, by using natural language processing techniques, the ingredients and cooking methods of a recipe can be accurately extracted. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input recipe text data into a generating AI, which can then extract ingredients and cooking methods.

[0062] The generation unit can generate cooking instructions step by step based on the extracted data. The generation unit can, for example, adjust the level of detail of each step to generate instructions that are easy for the user to understand. The generation unit can, for example, select an algorithm to use and generate the optimal procedure. For example, the generation unit can use a machine learning algorithm to generate the optimal procedure. The generation unit can, for example, use a machine learning algorithm to generate the optimal procedure. This makes it easier to reproduce dishes by generating specific cooking instructions based on the extracted data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the extracted data into a generation AI and have the generation AI generate the cooking instructions.

[0063] The reproduction unit can reproduce a dish based on the generated procedure. The reproduction unit reproduces a dish considering, for example, the use of cooking utensils and cooking time. For example, the reproduction unit provides a procedure for reproducing a dish using specific cooking utensils. The reproduction unit can also provide a procedure for reproducing a dish efficiently by optimizing the cooking time. This allows for the accurate reproduction of the taste and techniques of traditional dishes by reproducing them based on the generated procedure. Some or all of the above-described processes in the reproduction unit may be performed using, for example, AI, or not using AI. For example, the reproduction unit can input the generated procedure into a generating AI and have the generating AI perform the dish reproduction.

[0064] The service provider can analyze ancient documents using image recognition technology to identify the historical background of traditional cuisine. Image recognition technology includes, for example, object detection and character recognition. The service provider can, for example, use object detection technology to identify important information within ancient documents and reflect it in the analysis results. For example, object detection technology is a technology that detects specific objects in an image and identifies their position and shape. The service provider can also analyze the text of ancient documents using character recognition technology to identify the historical background. For example, character recognition technology is a technology that recognizes characters in an image and converts them into text data. In this way, by using image recognition technology, the historical background of traditional cuisine can be accurately identified from ancient documents. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input image data of ancient documents into a generating AI and have the generating AI perform the identification of the historical background.

[0065] The information provider can provide information to the user based on the identified historical context. For example, the information provider can provide text information, image information, audio information, etc., based on the identified historical context. For example, the information provider can provide text information based on the identified historical context. For example, the information provider can provide text information based on the identified historical context. The information provider can also provide image information based on the identified historical context. For example, the information provider can also provide image information based on the identified historical context. The information provider can also provide audio information based on the identified historical context. For example, the information provider can also provide audio information based on the identified historical context. By providing information based on the identified historical context, users can deepen their understanding of the origins and background of traditional cuisine. Some or all of the processing described above in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input data on the identified historical context into a generating AI and have the generating AI perform the information provision.

[0066] The analysis unit can estimate the user's emotions and adjust the recipe analysis method based on the estimated user emotions. For example, if the user is excited, the analysis unit provides detailed analysis results and explains the background and techniques of the recipe in detail. If the user is relaxed, the analysis unit provides concise and easy-to-understand analysis results, focusing on the minimum necessary information. If the user is in a hurry, the analysis unit performs the analysis quickly, extracting and providing only the main ingredients and steps. By adjusting the recipe analysis method according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI adjust the analysis method based on that emotion.

[0067] The analysis unit can improve the accuracy of its analysis of recipes based on regional food culture or customs. For example, the analysis unit can classify the ingredients and cooking methods of recipes based on regional food culture and reflect this in the analysis results. For example, the analysis unit can classify specific ingredients and cooking methods based on regional food culture and reflect this in the analysis results. For example, the analysis unit can identify dishes related to regional customs and festivals and include their background information in the analysis results. For example, the analysis unit can identify dishes related to regional customs and festivals and include their background information in the analysis results. For example, the analysis unit can customize the analysis results by considering regionally specific ingredients and cooking techniques. For example, the analysis unit can customize the analysis results by considering regionally specific ingredients and cooking techniques. This improves the accuracy of the analysis by considering regional food culture and customs. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on regional food culture and customs into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0068] The analysis unit can perform analysis based on seasonal variations in ingredients when analyzing recipes. For example, the analysis unit analyzes the ingredients of a recipe, taking into account the availability of seasonal ingredients. For example, the analysis unit analyzes the ingredients of a recipe, taking into account the availability of seasonal ingredients. For example, the analysis unit considers seasonal variations in flavor and nutritional value of ingredients and reflects these in the analysis results. For example, the analysis unit considers seasonal variations in flavor and nutritional value and reflects these in the analysis results. For example, the analysis unit considers seasonal price fluctuations of ingredients and proposes cost-effective recipes. For example, the analysis unit considers seasonal price fluctuations of ingredients and proposes cost-effective recipes. By considering seasonal variations in ingredients, more realistic analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input seasonal ingredient data into a generating AI and have the generating AI perform the analysis.

[0069] The analysis unit can estimate the user's emotions and determine the priority of recipes to analyze based on the estimated emotions. For example, if the user is excited, the analysis unit will prioritize analyzing complex and challenging recipes. For example, if the user is relaxed, the analysis unit will prioritize analyzing simple and easy-to-make recipes. For example, if the user is in a hurry, the analysis unit will prioritize analyzing recipes that can be made in a short amount of time. By determining the priority of recipes to analyze according to the user's emotions, the analysis unit can provide analysis results that meet the user's needs. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit may input user emotion data into the generation AI and have the generation AI determine the priority of recipes.

[0070] The analysis unit can prioritize analyzing recipes that are highly relevant by considering the user's eating history. For example, the analysis unit prioritizes analyzing recipes that are highly relevant based on the user's cooking history. For example, the analysis unit prioritizes analyzing recipes that are highly relevant based on the user's cooking history. For example, the analysis unit identifies preferred ingredients and cooking methods from the user's eating history and analyzes recipes based on that. For example, the analysis unit identifies preferred ingredients and cooking methods from the user's eating history and analyzes recipes based on that. For example, the analysis unit analyzes the user's eating history and prioritizes analyzing recipes that take nutritional balance into consideration. For example, the analysis unit analyzes the user's eating history and prioritizes analyzing recipes that take nutritional balance into consideration. In this way, by considering the user's eating history, highly relevant recipes can be prioritized. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's meal history data into the generating AI and have the generating AI perform an analysis to identify highly relevant recipes.

[0071] The analysis unit can perform recipe analysis while taking the user's allergy information into consideration. For example, the analysis unit can prioritize analyzing recipes that do not contain allergens based on the user's allergy information. For example, the analysis unit can prioritize analyzing recipes that do not contain allergens based on the user's allergy information. For example, the analysis unit can suggest alternative ingredients while taking the user's allergy information into consideration. For example, the analysis unit can suggest alternative ingredients while taking the user's allergy information into consideration. For example, the analysis unit can analyze cooking methods that minimize the risk of allergens based on the user's allergy information. For example, the analysis unit can analyze cooking methods that minimize the risk of allergens based on the user's allergy information. In this way, safe recipes can be provided by taking the user's allergy information into consideration. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's allergy information data into a generating AI and have the generating AI perform an analysis that takes the allergy information into consideration.

[0072] The generation unit can estimate the user's emotions and adjust the way the cooking instructions are presented based on the estimated emotions. For example, if the user is relaxed, the generation unit provides detailed and careful instructions. For example, if the user is in a hurry, the generation unit provides concise and to-the-point instructions. For example, if the user is excited, the generation unit provides cooking instructions with visually appealing effects. By adjusting the way the cooking instructions are presented according to the user's emotions, more appropriate cooking instructions can be provided. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI adjust the way the cooking procedure is expressed.

[0073] The generation unit can adjust the level of detail in the cooking instructions based on the difficulty of the dish. For example, for simple dishes, the generation unit provides only basic instructions. For example, for difficult dishes, the generation unit provides detailed instructions and points to note. For example, for intermediate level dishes, the generation unit provides instructions with a moderate level of detail. By adjusting the level of detail in the instructions based on the difficulty of the dish, cooking instructions tailored to the user's skill level can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input cooking difficulty data into a generation AI and have the generation AI adjust the level of detail in the instructions.

[0074] The generation unit can apply different generation algorithms depending on the category of cuisine when generating cooking procedures. For example, in the case of Japanese cuisine, the generation unit generates procedures based on traditional techniques and ingredients. For example, in the case of Western cuisine, the generation unit generates procedures based on modern cooking techniques and ingredients. For example, in the case of Chinese cuisine, the generation unit generates procedures based on specific cooking techniques and ingredients. By applying different generation algorithms depending on the category of cuisine, more appropriate cooking procedures can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the category data of the cuisine into a generation AI and have the generation AI execute the application of the generation algorithm.

[0075] The generation unit can estimate the user's emotions and adjust the length of the cooking instructions based on the estimated emotions. For example, if the user is in a hurry, the generation unit provides short, concise cooking instructions. For example, if the user is relaxed, the generation unit provides longer cooking instructions with detailed explanations. For example, if the user is excited, the generation unit provides cooking instructions with visually stimulating effects. By adjusting the length of the cooking instructions according to the user's emotions, a more appropriate cooking instruction can be provided. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the length of the cooking procedure.

[0076] The generation unit can determine the priority of cooking procedures based on the user's cooking experience when generating cooking procedures. For example, if the user is a beginner, the generation unit will prioritize providing basic procedures. For example, if the user is an intermediate cook, the generation unit will prioritize providing advanced procedures. For example, if the user is an advanced cook, the generation unit will prioritize providing specialized procedures. By determining the priority of procedures based on the user's cooking experience, the generation unit can provide more appropriate cooking procedures. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's cooking experience data into a generation AI and have the generation AI determine the priority of procedures.

[0077] The generation unit can customize cooking procedures by taking into account the user's kitchen equipment. For example, the generation unit customizes the procedures based on the cooking utensils the user owns. For example, the generation unit customizes the procedures based on the size and layout of the user's kitchen. For example, the generation unit provides the optimal procedures based on the user's cooking environment. For example, the generation unit provides the optimal procedures based on the user's cooking environment. This allows for the provision of more realistic cooking procedures by taking into account the user's kitchen equipment. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's kitchen equipment data into a generation AI and have the generation AI perform the procedure customization.

[0078] The reproduction unit can estimate the user's emotions and adjust the method of reproducing the dish based on the estimated emotions. For example, if the user is relaxed, the reproduction unit provides a detailed and careful reproduction method. For example, if the user is in a hurry, the reproduction unit provides a concise and to-the-point reproduction method. For example, if the user is excited, the reproduction unit provides a reproduction method with visually appealing effects. By adjusting the method of reproducing the dish according to the user's emotions, a more appropriate reproduction method can be provided. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the reproduction unit may be performed using AI, for example, or without AI. For example, the reproduction unit can input user emotion data into a generating AI and have the generating AI adjust the reproduction method.

[0079] The reproduction unit can analyze the user's past cooking history to select the optimal reproduction method when reproducing a dish. For example, the reproduction unit can select the optimal reproduction method based on the user's past cooking history. For example, the reproduction unit can select the optimal reproduction method based on the user's past cooking history. For example, the reproduction unit can identify the user's preferred cooking method from the user's past cooking history and select a reproduction method based on that. For example, the reproduction unit can identify the user's preferred cooking method from the user's past cooking history and select a reproduction method based on that. For example, the reproduction unit can analyze the user's past cooking history and select the most efficient reproduction method. For example, the reproduction unit can analyze the user's past cooking history and select the most efficient reproduction method. In this way, by analyzing the user's past cooking history, the optimal reproduction method can be provided. Some or all of the above processing in the reproduction unit may be performed using AI, for example, or without AI. For example, the reproduction unit can input the user's past cooking history data into a generating AI and have the generating AI select the optimal reproduction method.

[0080] The reproduction unit can customize the reproduction procedure when reproducing a dish, taking into account the user's current ingredient inventory. For example, the reproduction unit customizes the reproduction procedure based on the user's current ingredient inventory. For example, the reproduction unit customizes the reproduction procedure based on the user's current ingredient inventory. For example, the reproduction unit suggests alternative ingredients based on the user's ingredient inventory. For example, the reproduction unit suggests alternative ingredients based on the user's ingredient inventory. For example, the reproduction unit provides the optimal reproduction procedure, taking into account the user's ingredient inventory. For example, the reproduction unit provides the optimal reproduction procedure, taking into account the user's ingredient inventory. This allows for the provision of a more realistic reproduction procedure by considering the user's current ingredient inventory. Some or all of the above processing in the reproduction unit may be performed using AI, for example, or without AI. For example, the reproduction unit can input the user's ingredient inventory data into a generating AI and have the generating AI perform the customization of the reproduction procedure.

[0081] The reproduction unit can estimate the user's emotions and determine the priority of dishes to reproduce based on the estimated emotions. For example, if the user is excited, the reproduction unit will prioritize reproducing complex and challenging dishes. If the user is relaxed, the reproduction unit will prioritize reproducing simple and easy-to-make dishes. If the user is in a hurry, the reproduction unit will prioritize reproducing dishes that can be made in a short time. This allows for a more appropriate reproduction method by determining the priority of dishes to reproduce according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the reproduction unit may be performed using AI, for example, or without AI. For example, the reproduction unit can input user emotion data into a generative AI and have the generative AI determine the reproduction priority.

[0082] The reproduction unit can select the optimal reproduction method when reproducing a dish, taking into account the user's geographical location information. For example, the reproduction unit selects a reproduction method based on the user's geographical location information, taking into account ingredients and cooking methods specific to the region. For example, the reproduction unit selects a reproduction method based on the user's geographical location information, taking into account ingredients and cooking methods specific to the region. For example, the reproduction unit proposes the optimal way to obtain ingredients, taking into account the user's geographical location information. For example, the reproduction unit proposes the optimal way to obtain ingredients, taking into account the user's geographical location information. For example, the reproduction unit selects a reproduction method suitable for the local climate and environment, taking into account the user's geographical location information. For example, the reproduction unit selects a reproduction method suitable for the local climate and environment, taking into account the user's geographical location information. This allows for the provision of more realistic reproduction methods by considering the user's geographical location information. Some or all of the above processing in the reproduction unit may be performed using AI, for example, or without AI. For example, the reproduction unit can input the user's geographical location information data into a generating AI and have the generating AI select the optimal reproduction method.

[0083] The reproduction unit can adjust the reproduction procedure when reproducing a dish, taking into account the user's dietary restrictions. For example, the reproduction unit can provide an allergen-free reproduction procedure based on the user's dietary restrictions. For example, the reproduction unit can provide an allergen-free reproduction procedure based on the user's dietary restrictions. For example, the reproduction unit can suggest alternative ingredients, taking into account the user's dietary restrictions. For example, the reproduction unit can suggest alternative ingredients, taking into account the user's dietary restrictions. For example, the reproduction unit can provide a reproduction procedure that takes nutritional balance into account, based on the user's dietary restrictions. For example, the reproduction unit can provide a reproduction procedure that takes nutritional balance into account, based on the user's dietary restrictions. This makes it possible to provide a safer and more appropriate reproduction procedure by taking the user's dietary restrictions into account. Some or all of the above processing in the reproduction unit may be performed using AI, for example, or without AI. For example, the reproduction unit can input the user's dietary restriction data into a generating AI and have the generating AI perform the adjustment of the reproduction procedure.

[0084] The service provider can estimate the user's emotions and adjust the way historical background is displayed based on the estimated emotions. For example, if the user is excited, the service provider will provide a display method with visually appealing effects. For example, if the user is relaxed, the service provider will provide detailed information in calming colors. For example, if the user is relaxed, the service provider will provide detailed information in calming colors. For example, if the user is in a hurry, the service provider will provide a concise and to-the-point display method. By adjusting the way historical background is displayed according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provision unit may be performed using AI, for example, or without AI. For example, the service provision unit can input user emotion data into a generating AI and have the generating AI adjust the display method.

[0085] The information provider can supplement current information by referencing past data when providing historical background. For example, the information provider can supplement and provide current information based on ancient documents and research data. For example, the information provider can supplement and provide current information based on ancient documents and research data. For example, the information provider can refer to past cookbooks and recipes and provide information by comparing them with modern cooking methods. For example, the information provider can refer to past cookbooks and recipes and provide information by comparing them with modern cooking methods. For example, the information provider can supplement and provide information on the origins and changes of dishes based on historical events and cultural backgrounds. For example, the information provider can supplement and provide information on the origins and changes of dishes based on historical events and cultural backgrounds. This allows for more accurate supplementation of current information by referencing past data. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input past data into a generating AI and have the generating AI perform the supplementation of current information.

[0086] The information provider can apply different information provision methods for each category of cuisine when providing historical background information. For example, in the case of Japanese cuisine, the information provider will provide information that emphasizes traditional techniques and cultural background. For example, in the case of Western cuisine, the information provider will provide information that emphasizes historical events and regional changes. For example, in the case of Chinese cuisine, the information provider will provide information that emphasizes regional characteristics and historical background. By applying different information provision methods for each category of cuisine, it becomes possible to provide more appropriate information. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input the category data of cuisine into a generating AI and have the generating AI execute the application of information provision methods.

[0087] The information provider can estimate the user's emotions and adjust the importance of historical context based on the estimated emotions. For example, if the user is excited, the information provider will highlight and provide detailed and important information. For example, if the user is relaxed, the information provider will provide concise and to-the-point information. For example, if the user is in a hurry, the information provider will provide only the most important information. This allows for more appropriate information to be provided by adjusting the importance of historical context according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provision unit may be performed using AI, for example, or without AI. For example, the service provision unit can input user emotion data into a generating AI and have the generating AI perform importance adjustments.

[0088] The information provider can prioritize information based on the user's interests when providing historical background. For example, if the user is interested in a particular region, the information provider will prioritize providing information related to that region. For example, if the user is interested in a particular era, the information provider will prioritize providing information related to that era. For example, if the user is interested in a particular era, the information provider will prioritize providing information related to that era. For example, if the user is interested in a particular dish, the information provider will prioritize providing information related to that dish. For example, if the user is interested in a particular dish, the information provider will prioritize providing information related to that dish. By prioritizing information based on the user's interests, it becomes possible to provide more appropriate information. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input user interest data into a generating AI and have the generating AI perform the determination of information prioritization.

[0089] The information provider can improve the accuracy of the information by referring to relevant literature and materials when providing historical background. For example, the information provider can improve the accuracy of the information by referring to relevant academic papers and research materials. For example, the information provider can improve the accuracy of the information by referring to relevant academic papers and research materials. For example, the information provider can improve the reliability of the information by analyzing historical literature and ancient documents. For example, the information provider can improve the reliability of the information by analyzing historical literature and ancient documents. For example, the information provider can improve the accuracy of the information by referring to expert opinions and explanations. For example, the information provider can improve the accuracy of the information by referring to expert opinions and explanations. In this way, the accuracy of the information can be improved by referring to relevant literature and materials. Some or all of the above processing in the information provider may be performed using AI, for example, or without using AI. For example, the information provider can input data from relevant literature and materials into a generating AI and have the generating AI perform the information accuracy improvement. === Hard Collateral 1-1 === Each of the multiple elements described above, including the analysis unit, generation unit, reproduction unit, and provision unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart device 14 and analyzes recipes using natural language processing technology to extract ingredients and cooking methods. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates specific procedures based on the analyzed data. The reproduction unit is implemented, for example, by the control unit 46A of the smart device 14 and reproduces the dish based on the generated procedures. The provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes research and historical documents to provide information about the origin and background of traditional dishes. === Hard Collateral 1-2 === Each of the multiple elements described above, including the analysis unit, generation unit, reproduction unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart glasses 214, which analyzes recipes using natural language processing techniques and extracts ingredients and cooking methods. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates specific procedures based on the analyzed data. The reproduction unit is implemented by the control unit 46A of the smart glasses 214, which reproduces the dish based on the generated procedures. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes research and historical documents and provides information about the origin and background of traditional dishes. === Hard Collateral 1-3 === Each of the multiple elements described above, including the analysis unit, generation unit, reproduction unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the headset terminal 314 and analyzes recipes using natural language processing technology to extract ingredients and cooking methods. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates specific procedures based on the analyzed data. The reproduction unit is implemented by the control unit 46A of the headset terminal 314 and reproduces the dish based on the generated procedures. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes research and historical documents to provide information about the origins and background of traditional dishes. === Hard Collateral 1-4 === Each of the multiple elements described above, including the analysis unit, generation unit, reproduction unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the robot 414, which analyzes recipes using natural language processing techniques and extracts ingredients and cooking methods. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which generates specific procedures based on the analyzed data. The reproduction unit is implemented, for example, by the control unit 46A of the robot 414, which reproduces the dish based on the generated procedures. The provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes research and historical documents and provides information about the origins and background of traditional dishes.

[0090] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0091] The analytics unit can prioritize analyzing recipes that have been well-received in the past, based on the user's meal history. For example, the analytics unit can identify recipes that the user has previously given high ratings to and suggest new recipes based on that. The analytics unit can also analyze the user's preferences for specific ingredients and cooking methods from their meal history and customize recipes accordingly. Furthermore, the analytics unit can analyze the user's meal history and suggest recipes that take nutritional balance into consideration. This allows for more personalized recipe analysis by taking into account the user's past meal history.

[0092] The generation unit can adjust the difficulty of the cooking procedure based on the user's cooking skill level. For example, it can provide basic procedures for beginners and more advanced procedures for intermediate users. For advanced users, it can provide recipes that include specialized techniques and detailed procedures. Furthermore, the generation unit can dynamically adjust the level of detail of the procedure based on user feedback. This makes it possible to provide the optimal cooking procedure according to the user's skill level.

[0093] The recipe reproduction unit can suggest alternative ingredients, taking into account the user's current ingredient inventory. For example, if a particular ingredient is in short supply, the unit will suggest a substitute and adjust the recipe accordingly. The reproduction unit can also monitor the user's ingredient inventory in real time and provide the optimal reproduction procedure. Furthermore, it can suggest cost-effective recipes based on the user's ingredient inventory. This makes it possible to reproduce dishes while making the most of the user's ingredient inventory.

[0094] The service provider can provide information based on the user's geographical location, taking into account local ingredients and cooking methods. For example, if a user is in a specific region, it can suggest recipes using ingredients available in that area. The service provider can also suggest cooking methods suitable for the local climate and environment. Furthermore, it can provide background information based on local food culture and customs. This enables the provision of more realistic and appropriate information that takes the user's geographical location into account.

[0095] The information provider can improve the accuracy of the information by referring to relevant literature and materials when providing historical background. For example, they can improve the accuracy of the information by referring to relevant academic papers and research materials. The information provider can also enhance the reliability of the information by analyzing historical literature and ancient documents. Furthermore, the information provider can improve the accuracy of the information by referring to expert opinions and explanations. In this way, the accuracy of the information can be improved by referring to relevant literature and materials.

[0096] The analysis unit can estimate the user's emotions and adjust the recipe analysis method based on those emotions. For example, if the user is excited, it can provide detailed analysis results and explain the recipe's background and techniques in detail. If the user is relaxed, it can provide concise and easy-to-understand analysis results, focusing on only the essential information. Furthermore, if the user is in a hurry, it can perform a rapid analysis and extract and provide only the main ingredients and steps. In this way, by adjusting the recipe analysis method according to the user's emotions, more appropriate analysis results can be provided.

[0097] The generation unit can estimate the user's emotions and adjust the way the cooking instructions are presented based on those emotions. For example, if the user is relaxed, it can provide detailed and thorough instructions. If the user is in a hurry, it can provide concise and to-the-point instructions. Furthermore, if the user is excited, it can provide instructions with visually appealing effects. In this way, by adjusting the way the cooking instructions are presented according to the user's emotions, more appropriate cooking instructions can be provided.

[0098] The reproduction unit can estimate the user's emotions and adjust the cooking reproduction method based on those emotions. For example, if the user is relaxed, it can provide a detailed and careful reproduction method. If the user is in a hurry, it can provide a concise and to-the-point reproduction method. Furthermore, if the user is excited, it can provide a reproduction method with visually appealing effects. In this way, by adjusting the cooking reproduction method according to the user's emotions, a more appropriate reproduction method can be provided.

[0099] The system can estimate the user's emotions and adjust how historical background information is displayed based on those emotions. For example, if the user is excited, it can provide a display method with visually appealing effects. If the user is relaxed, it can provide detailed information in calming colors. Furthermore, if the user is in a hurry, it can provide a concise and to-the-point display method. By adjusting how historical background information is displayed according to the user's emotions, it becomes possible to provide more appropriate information.

[0100] The information provider can estimate the user's emotions and adjust the importance of historical context based on those emotions. For example, if the user is excited, detailed and important information can be emphasized. If the user is relaxed, concise and to-the-point information can be provided. Furthermore, if the user is in a hurry, only the most important information can be provided. This allows for more appropriate information delivery by adjusting the importance of historical context according to the user's emotions.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The analysis unit analyzes traditional cooking recipes. The analysis unit uses natural language processing techniques to analyze the recipes and extract ingredients and cooking methods. Specifically, it uses morphological analysis to segment the recipe text and identify each ingredient and cooking method. It can also use grammatical analysis to analyze the sentence structure of the recipe and extract cooking steps. Furthermore, it uses semantic analysis to understand the meaning of the recipe and identify the relationships between ingredients and cooking methods. Step 2: The generation unit generates specific procedures based on the data analyzed by the analysis unit. The generation unit generates cooking procedures step by step based on the extracted data. The level of detail for each step is adjusted to generate procedures that are easy for the user to understand. It can also select the algorithm to be used and generate the optimal procedure. Step 3: The reproduction unit reproduces the dish based on the procedure generated by the generation unit. The reproduction unit reproduces the dish taking into account the use of cooking utensils and cooking time. It can also provide a procedure for reproducing the dish using specific cooking utensils, optimizing cooking time, and providing a procedure for efficiently reproducing the dish. Step 4: The providing department analyzes research and historical documents to provide information about the origins and background of traditional dishes. The providing department uses image recognition technology to analyze historical documents and identify the historical background of traditional dishes. It also uses object detection technology to identify important information within historical documents and reflects it in the analysis results. Furthermore, it can also use character recognition technology to analyze the text of historical documents and identify the historical background.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0105] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0106] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0113] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0114] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0115] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0116] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0117] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0121] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

[0124] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0126] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0147] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0148] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0149] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0150] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0154] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0156] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0158] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0159] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0160] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0164] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0165] 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.

[0166] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0167] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0168] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0169] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0171] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0172] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0173] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis unit analyzes the recipe, A generation unit that generates specific procedures based on the data analyzed by the analysis unit, A reproduction unit that reproduces a dish based on the procedure generated by the generation unit, It comprises a provision unit that analyzes research or ancient documents and provides information about the origins and background of traditional cuisine. A system characterized by the following features.

2. The aforementioned analysis unit, We use natural language processing technology to analyze recipes and extract ingredients and cooking methods. The system according to feature 1.

3. The generating unit is Based on the extracted data, the cooking procedure is generated step by step. The system according to feature 1.

4. The reproduction unit is, Recreate the dish based on the generated instructions. The system according to feature 1.

5. The aforementioned supply unit is, Using image recognition technology, we analyze ancient documents to identify the historical background of traditional cuisine. The system according to feature 1.

6. The aforementioned supply unit is, Provide information to users based on identified historical context. The system according to feature 1.

7. The aforementioned analysis unit, The system estimates the user's emotions and adjusts the recipe analysis method based on those estimated emotions. The system according to feature 1.

8. The aforementioned analysis unit, When analyzing recipes, improve the accuracy of the analysis based on regional food culture or customs. The system according to feature 1.

Images