System

Abstract

An object of a system according to an embodiment is to propose an optimal recipe by utilizing ingredients and seasonings at hand.SOLUTION: A system includes a reception unit, an analysis unit, a proposal unit, and a provision unit. The reception unit allows a user to capture a moving image of an ingredient or a seasoning with a smartphone. The analysis unit analyzes the moving image received by the reception unit and specifies the type of the ingredient or the seasoning. The proposal unit proposes a recipe on the basis of the ingredient and the seasoning specified by the analysis unit. The providing unit provides a food image, calories, and nutrition balance after completion based on the recipe proposed by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult and time-consuming to find the optimal recipe using ingredients and seasonings on hand.

[0005] The system according to the embodiment aims to propose optimal recipes using ingredients and seasonings on hand. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a suggestion unit, and a provision unit. The reception unit allows a user to take videos of ingredients and seasonings using a smartphone. The analysis unit analyzes the videos received by the reception unit and identifies the types of ingredients and seasonings. The suggestion unit proposes recipes based on the ingredients and seasonings identified by the analysis unit. The provision unit provides images of the completed dish, as well as calories and nutritional balance, based on the recipe proposed by the suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest optimal recipes using ingredients and seasonings on hand. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A cooking suggestion system according to an embodiment of the present invention allows a user to take videos of ingredients and seasonings using a smartphone, and an AI analyzes the videos to provide optimal recipes, images of the finished dish, and calorie and nutritional balance information in real time. The cooking suggestion system allows a user to take videos of ingredients and seasonings using a smartphone, and an AI analyzes the videos to identify the types of ingredients and seasonings and suggest optimal recipes. Furthermore, based on the suggested recipe, the AI ​​provides an image of the finished dish, calories, and nutritional balance. For example, the cooking suggestion system simply requires a user to take a photo of ingredients and seasonings in their kitchen using a smartphone camera. The cooking suggestion system then analyzes the input video using AI to identify the types of ingredients and seasonings. For example, it identifies ingredients and seasonings such as tomatoes, onions, and soy sauce. Next, the cooking suggestion system suggests optimal recipes based on the analyzed content. For example, it suggests a recipe for a dish using tomatoes, onions, and soy sauce. Next, based on the suggested recipe, the AI ​​provides an image of the finished dish, calories, and nutritional balance. For example, it displays an image of the finished suggested dish, as well as the calories and nutritional balance of the dish. This allows the user to easily get ideas and enjoy creative cooking at home. It also supports the realization of healthy meals, providing users with a better diet. This allows the user to easily get ideas and enjoy creative cooking at home. It also supports the realization of healthy meals, providing users with a better diet.

[0029] A cooking suggestion system according to an embodiment includes a receiving unit, an analysis unit, a suggestion unit, and a providing unit. The receiving unit allows a user to take videos of ingredients and seasonings using a smartphone. When taking videos of ingredients and seasonings using a smartphone, the user may simply take a photo of the ingredients and seasonings in the kitchen using the smartphone camera. The analysis unit analyzes the video received by the receiving unit and identifies the types of ingredients and seasonings. The analysis unit identifies ingredients and seasonings, such as tomatoes, onions, and soy sauce. The analysis unit can identify ingredients and seasonings in the video using image recognition technology or machine learning algorithms. The suggestion unit suggests optimal recipes based on the ingredients and seasonings identified by the analysis unit. The suggestion unit suggests, for example, recipes using tomatoes, onions, and soy sauce. The suggestion unit can suggest recipes taking into consideration the user's preferences, nutritional balance, cooking time, and the like. The providing unit provides images of the completed dishes, as well as calories and nutritional balance, based on the recipes suggested by the suggestion unit. The presentation unit displays, for example, an image of the completed proposed dish, as well as the calories and nutritional balance of the dish. The presentation unit can provide the completed dish image, calories, and nutritional balance using an image generation algorithm and a nutrition calculation algorithm. This allows the dish suggestion system according to the embodiment to easily get ideas and enjoy creative cooking at home. It also supports the realization of healthy meals and provides users with a better diet.

[0030] The cooking suggestion system includes a reception unit that analyzes a user's past shooting history and selects the optimal shooting method. The reception unit analyzes the user's past shooting history and selects the optimal shooting method. For example, the reception unit suggests the optimal shooting method based on the user's past preferred shooting angles and lighting conditions. The reception unit can also analyze successful videos the user has shot in the past and automatically apply similar settings. The system can also select the optimal shooting method by taking into account shooting conditions the user has avoided in the past. This enables better video shooting by selecting the optimal shooting method based on the past shooting history. The analysis of the past shooting history is performed using, for example, data mining or machine learning algorithms. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input past shooting history data into a generation AI and have the generation AI select the optimal shooting method.

[0031] The cooking suggestion system includes a reception unit that performs filtering based on the user's current ingredient inventory when shooting a video. The reception unit performs filtering based on the user's current ingredient inventory when shooting a video. For example, when a user shoots a video of ingredients in the refrigerator, an AI checks the inventory and removes any duplicate ingredients. If an ingredient is in short supply in a video shot by the user, the AI ​​can suggest alternative ingredients to make up for the shortage. If an ingredient is in excess in a video shot by the user, the AI ​​can perform filtering to adjust the amount to an appropriate level. This filtering based on the current ingredient inventory prevents duplication or shortages. The ingredient inventory is grasped, for example, using an inventory management system or barcode scanning. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI. For example, the reception unit can input current ingredient inventory data into a generation AI and have the generation AI perform filtering.

[0032] The cooking suggestion system includes a reception unit that selects a shooting means according to a user's input method when shooting a video. The reception unit selects a shooting means according to the user's input method when shooting a video. For example, when a user gives instructions by voice, an AI can select the optimal shooting means using voice recognition. When a user gives instructions by text, the AI ​​can perform text analysis and select the optimal shooting means. When a user provides images, the AI ​​can perform image analysis and select the optimal shooting means. This enables efficient video shooting by selecting the optimal shooting means according to the user's input method. The selection of the shooting means according to the input method is performed using, for example, voice recognition technology, text analysis technology, or image analysis technology. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data to a generation AI and have the generation AI select the optimal shooting means.

[0033] The recipe suggestion system includes a reception unit that, when shooting a video, prioritizes capturing highly relevant ingredients in consideration of the user's geographical location information. The reception unit, when shooting a video, prioritizes capturing highly relevant ingredients in consideration of the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes capturing ingredients that are easily available in that area. Also, when the user is traveling, the reception unit can prioritize capturing images of local specialties. Also, when the user is at home, the reception unit can prioritize capturing images of ingredients that can be purchased at a nearby supermarket. In this way, highly relevant ingredients can be prioritized by taking the geographical location information into consideration. The geographical location information can be taken into consideration using, for example, GPS data or region-specific ingredient information. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input the user's geographical location data into a generation AI and cause the generation AI to select highly relevant ingredients.

[0034] The dish recommendation system includes a reception unit that analyzes a user's social media activity and captures related ingredients when shooting a video. The reception unit analyzes the user's social media activity and captures related ingredients when shooting a video. For example, the reception unit prioritizes capturing images of ingredients related to a dish shared by the user on social media. The reception unit can also analyze the user's social media posts and capture related ingredients. The system can also capture related ingredients by referring to the activities of the user's friends on social media. In this way, related ingredients can be captured by analyzing social media activity. The analysis of social media activity is performed, for example, by analyzing the content of posts and hashtags. Some or all of the above-mentioned processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and have the generation AI select related ingredients.

[0035] The cooking suggestion system includes a reception unit that customizes the shooting method by reflecting the user's past feedback when shooting a video. The reception unit customizes the shooting method by reflecting the user's past feedback when shooting a video. For example, the reception unit suggests an optimal shooting method based on shooting methods that the user has previously preferred. The system can also select an optimal shooting method by taking into account shooting methods that the user has previously avoided. The system can also customize the shooting method by analyzing the user's past feedback. In this way, the shooting method can be customized by reflecting the past feedback. The reflection of the past feedback is performed, for example, by using user ratings and comment analysis. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into a generation AI and have the generation AI customize the shooting method.

[0036] The dish recommendation system includes an analysis unit that adjusts the level of detail of the analysis based on the freshness and quality of the ingredients during analysis. The analysis unit adjusts the level of detail of the analysis based on the freshness and quality of the ingredients during analysis. For example, detailed analysis results are provided for highly fresh ingredients. Detailed analysis results can also be provided for high-quality ingredients. Brief analysis results can also be provided for ingredients with low freshness or quality. By adjusting the level of detail of the analysis based on the freshness and quality of the ingredients, more accurate analysis results can be provided. The freshness and quality of the ingredients are evaluated using, for example, a freshness sensor or a quality evaluation algorithm. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input freshness data of ingredients to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0037] The dish recommendation system includes an analysis unit that applies different analysis algorithms depending on the category of ingredients during analysis. The analysis unit applies different analysis algorithms depending on the category of ingredients during analysis. For example, for vegetables, an analysis algorithm based on nutritional value is applied. For meat, an analysis algorithm based on cooking method can also be applied. For seasonings, an analysis algorithm based on the amount used can also be applied. By applying different analysis algorithms depending on the category of ingredients, more appropriate analysis results can be provided. The application of analysis algorithms depending on the category of ingredients is performed using, for example, an algorithm for vegetables and an algorithm for meat. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input ingredient category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0038] The dish recommendation system includes an analysis unit that, during analysis, refers to the user's past analysis results to improve the accuracy of the analysis. The analysis unit, during analysis, refers to the user's past analysis results to improve the accuracy of the analysis. For example, the analysis accuracy is improved based on data on ingredients that the user has previously analyzed. The analysis algorithm can also be optimized by analyzing the user's past analysis results. The analysis accuracy can also be improved by referring to the user's past feedback. In this way, the analysis accuracy can be improved by referring to the past analysis results. The reference to the past analysis results is performed, for example, by using historical data or a feedback loop. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0039] The dish recommendation system includes an analysis unit that determines analysis priorities based on when ingredients were obtained during analysis. The analysis unit determines analysis priorities based on when ingredients were obtained during analysis. For example, analysis is prioritized for fresh ingredients. Analysis can also be prioritized for ingredients close to their expiration date. Analysis can also be prioritized for seasonal ingredients. In this way, by determining analysis priorities based on when ingredients were obtained, more appropriate analysis results can be provided. The acquisition date of ingredients is evaluated using, for example, the acquisition date or expiration date. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data on when ingredients were obtained to the generation AI, and have the generation AI determine the analysis priorities.

[0040] The dish recommendation system includes an analysis unit that adjusts the order of analysis based on the relevance of ingredients during analysis. The analysis unit adjusts the order of analysis based on the relevance of ingredients during analysis. For example, ingredients used in the same dish may be analyzed with priority. In addition, ingredients in the same category may be analyzed with priority. In addition, ingredients preferred by the user may be analyzed with priority. By adjusting the order of analysis based on the relevance of ingredients, more appropriate analysis results can be provided. The relevance of ingredients is evaluated using, for example, combinations of ingredients or co-occurrence relationships. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit may input ingredient relevance data into a generation AI and have the generation AI adjust the order of analysis.

[0041] The dish recommendation system includes an analysis unit that adjusts the use of technical terms in the analysis according to the user's level of expertise during analysis. The analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user is a beginner, the analysis unit can provide analysis results in simple language. If the user is an intermediate user, the analysis unit can provide analysis results using appropriate technical terms. If the user is an advanced user, the analysis unit can provide analysis results using detailed technical terms. This allows for more appropriate analysis results to be provided by adjusting the use of technical terms in the analysis according to the user's level of expertise. The level of expertise is evaluated, for example, using the user's past usage history and feedback. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's expertise data into a generation AI and have the generation AI execute the use of technical terms.

[0042] The cooking suggestion system includes a suggestion unit that adjusts the level of detail of the suggestion based on the importance of the recipe when suggesting the recipe. The suggestion unit adjusts the level of detail of the suggestion based on the importance of the recipe when suggesting the recipe. For example, a detailed suggestion is made for an important recipe. A concise suggestion can also be made for a simple recipe. A detailed suggestion can also be made for a recipe in which the user is particularly interested. By adjusting the level of detail of the suggestion based on the importance of the recipe, more appropriate suggestions can be made. The importance of a recipe is evaluated using, for example, main ingredients and cooking time. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input recipe importance data to a generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0043] The cooking recommendation system includes a suggestion unit that applies different suggestion algorithms depending on the recipe category when making a suggestion. The suggestion unit applies different suggestion algorithms depending on the recipe category when making a suggestion. For example, for desserts, a suggestion algorithm based on sweetness and calories can be applied. For main dishes, a suggestion algorithm based on nutritional value and cooking time can be applied. For side dishes, a suggestion algorithm based on ease of preparation can be applied. By applying different suggestion algorithms depending on the recipe category, more appropriate suggestions can be made. The suggestion algorithm depending on the recipe category is applied using, for example, an algorithm for desserts and an algorithm for main dishes. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input recipe category data into a generation AI and cause the generation AI to apply a suggestion algorithm.

[0044] The cooking suggestion system includes a suggestion unit that, when making a suggestion, improves the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit improves the accuracy of the suggestion by referring to the user's past suggestion results. For example, the suggestion unit improves the accuracy of the suggestion based on recipes that the user has previously preferred. The suggestion unit can also analyze the user's past suggestion results and optimize the suggestion algorithm. The suggestion unit can also improve the accuracy of the suggestion by referring to the user's past feedback. In this way, the suggestion accuracy can be improved by referring to the past suggestion results. The reference to the past suggestion results is performed, for example, by using historical data or a feedback loop. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion data into the generation AI and cause the generation AI to improve the accuracy of the suggestions.

[0045] The cooking suggestion system includes a suggestion unit that, at the time of suggestion, determines the priority of suggestions based on the time of recipe submission. The suggestion unit, at the time of suggestion, determines the priority of suggestions based on the time of recipe submission. For example, seasonal recipes are given priority in suggestion. Recipes related to a specific event can also be given priority in suggestion. Recipes that a user prefers during a specific time period can also be given priority in suggestion. In this way, by determining the priority of suggestions based on the time of recipe submission, more appropriate suggestions can be made. The time of recipe submission is evaluated using, for example, the submission date or seasonal ingredients. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can input recipe submission time data into a generation AI and have the generation AI determine the priority of suggestions.

[0046] The cooking recommendation system includes a suggestion unit that adjusts the order of suggestions based on the relevance of the recipes when making suggestions. The suggestion unit adjusts the order of suggestions based on the relevance of the recipes when making suggestions. For example, related recipes may be preferentially suggested based on ingredients used in the same dish. Recipes in the same category may also be preferentially suggested. Recipes that are preferred by the user may also be preferentially suggested. This allows for more appropriate suggestions by adjusting the order of suggestions based on the relevance of the recipes. The relevance of the recipes is evaluated using, for example, combinations of ingredients or co-occurrence relationships. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit may input recipe relevance data into a generation AI and cause the generation AI to adjust the order of suggestions.

[0047] The dish suggestion system includes a suggestion unit that adjusts the use of technical terms in the suggestion according to the user's level of expertise when making a suggestion. The suggestion unit adjusts the use of technical terms in the suggestion according to the user's level of expertise when making a suggestion. For example, if the user is a beginner, the suggestion unit can make the suggestion using simple language. If the user is an intermediate user, the suggestion unit can make the suggestion using appropriate technical terms. If the user is an advanced user, the suggestion unit can make the suggestion using detailed technical terms. This allows for more appropriate suggestions to be made by adjusting the use of technical terms in the suggestion according to the user's level of expertise. The level of expertise is evaluated, for example, using the user's past usage history and feedback. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can input the user's expertise data into a generation AI and cause the generation AI to use technical terms.

[0048] The dish recommendation system includes a providing unit that adjusts the level of detail of the information to be provided based on the degree of completion of the dish when the information is provided. The providing unit adjusts the level of detail of the information to be provided based on the degree of completion of the dish when the information is provided. For example, detailed information is provided for a highly completed dish. Brief information can also be provided for a simple dish. Detailed information can also be provided for a dish in which the user is particularly interested. In this way, by adjusting the level of detail of the information to be provided based on the degree of completion of the dish, more appropriate information can be provided. The degree of completion of the dish is evaluated using, for example, an evaluation standard for the degree of completion or a level of detailed information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the degree of completion of the dish to a generating AI and have the generating AI adjust the level of detail of the information.

[0049] The dish recommendation system includes a serving unit that applies different serving algorithms depending on the category of the dish when serving the food. The serving unit applies different serving algorithms depending on the category of the dish when serving the food. For example, for desserts, a serving algorithm based on sweetness and calories can be applied. For main dishes, a serving algorithm based on nutritional value and cooking time can also be applied. For side dishes, a serving algorithm based on ease of cooking can also be applied. By applying different serving algorithms depending on the category of the dish, more appropriate information can be provided. The application of a serving algorithm depending on the category of the dish is performed using, for example, an algorithm for desserts and an algorithm for main dishes. Some or all of the above-mentioned processing in the serving unit may be performed using, for example, AI, or may be performed without using AI. For example, the serving unit can input dish category data into a generation AI and cause the generation AI to apply a serving algorithm.

[0050] The dish recommendation system includes a serving unit that, when serving, improves the accuracy of serving by referring to the user's past serving results. The serving unit, when serving, improves the accuracy of serving by referring to the user's past serving results. For example, the serving accuracy is improved based on the user's past favorite dishes. The serving algorithm can also be optimized by analyzing the user's past serving results. The serving accuracy can also be improved by referring to the user's past feedback. In this way, the serving accuracy can be improved by referring to the past serving results. The reference to the past serving results is performed, for example, by using historical data or a feedback loop. Some or all of the above-mentioned processing in the serving unit may be performed, for example, using AI, or may be performed without using AI. For example, the serving unit can input the user's past serving data into the generation AI and cause the generation AI to improve the accuracy of serving.

[0051] The dish recommendation system includes a providing unit that, at the time of provision, determines the priority of information to be provided based on the time of submission of the dish. The providing unit, at the time of provision, determines the priority of information to be provided based on the time of submission of the dish. For example, in the case of seasonal dishes, information is provided preferentially. In addition, information can be provided preferentially in the case of dishes related to a specific event. In addition, information can be provided preferentially for dishes that the user prefers during a specific time period. In this way, by determining the priority of information to be provided based on the time of submission of the dish, more appropriate information can be provided. The time of submission of the dish is evaluated using, for example, the submission date or seasonal ingredients. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the time of submission of the dish to a generation AI and have the generation AI determine the priority of the information.

[0052] The dish recommendation system includes a providing unit that adjusts the order of information to be provided based on the relevance of dishes when providing the information. The providing unit adjusts the order of information to be provided based on the relevance of dishes when providing the information. For example, related information may be provided preferentially based on ingredients used in the same dish. Information on dishes in the same category may also be provided preferentially. Information on dishes preferred by the user may also be provided preferentially. In this way, by adjusting the order of information to be provided based on the relevance of dishes, more appropriate information can be provided. The relevance of dishes is evaluated using, for example, combinations of ingredients or co-occurrence relationships. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input dish relevance data to a generation AI and cause the generation AI to adjust the order of the information.

[0053] The dish recommendation system includes a providing unit that adjusts the use of technical terms in the information to be provided according to the user's level of expertise. The providing unit adjusts the use of technical terms in the information to be provided according to the user's level of expertise. For example, if the user is a beginner, the providing unit can provide information using simple language. If the user is an intermediate user, the providing unit can provide information using appropriate technical terms. If the user is an advanced user, the providing unit can provide information using detailed technical terms. This allows for more appropriate information to be provided by adjusting the use of technical terms in the information to be provided according to the user's level of expertise. The level of expertise is evaluated, for example, using the user's past usage history and feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's expertise data into a generation AI and have the generation AI execute the use of technical terms.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The cooking recommendation system includes a suggestion unit that analyzes the frequency of use of ingredients by the user and prioritizes suggesting frequently used ingredients. For example, ingredients such as tomatoes and onions that the user frequently uses are prioritized. The system can also identify and suggest frequently used ingredients based on the user's history of ingredients used in the past. Furthermore, if the user prefers a particular ingredient, it can prioritize suggesting recipes that include that ingredient. This allows the system to suggest optimal recipes based on the frequency of use of ingredients by the user, thereby enabling suggestions that match the user's preferences.

[0056] The cooking recommendation system includes a suggestion unit that analyzes the user's past meal history and suggests balanced meals. For example, if the user has not eaten many vegetables in the past, recipes containing a lot of vegetables can be suggested. Also, if the user has eaten many high-calorie meals in the past, low-calorie recipes can be suggested. Furthermore, if the user is lacking in a particular nutrient, recipes containing that nutrient can be suggested. In this way, by suggesting balanced meals based on the user's past meal history, it is possible to support a healthy eating lifestyle.

[0057] The cooking recommendation system includes a recommendation unit that analyzes the storage status of the user's ingredients and prioritizes recommendations for ingredients that are difficult to store. For example, it prioritizes recommendations for fresh vegetables and fruits that are difficult to store. It can also prioritize recommendations for ingredients that are close to their expiration date. Furthermore, it can monitor the storage status of ingredients in the refrigerator in real time and prioritize recommendations for ingredients that are difficult to store. This makes it possible to reduce food waste by suggesting optimal recipes based on the storage status of the user's ingredients.

[0058] The cooking recommendation system includes a suggestion unit that analyzes a user's ingredient purchase history and prioritizes recommendations of ingredients that are frequently purchased. For example, ingredients such as chicken and tomatoes that are frequently purchased by the user are prioritized. The system can also identify and recommend ingredients that are frequently purchased by the user based on the user's past purchase history. Furthermore, if a user has a preference for a particular ingredient, it can prioritize recommendations of recipes that include that ingredient. This allows the system to suggest optimal recipes based on the user's purchase history, thereby enabling recommendations that match the user's preferences.

[0059] The cooking recommendation system includes a suggestion unit that analyzes the nutritional value of the user's ingredients and suggests nutritionally balanced recipes. For example, if the user is deficient in a particular nutrient, the system suggests recipes that contain that nutrient. It can also suggest recipes that limit the nutrient the user is consuming in excess. It can also suggest nutritionally balanced recipes based on the user's health condition. This allows the system to support a healthy diet by suggesting optimal recipes based on the user's nutritional value.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The reception unit allows the user to take a video of the ingredients and seasonings using their smartphone. All the user needs to do is take a video of the ingredients and seasonings in their kitchen using their smartphone camera. Step 2: The analysis unit analyzes the video received by the reception unit and identifies the types of ingredients and seasonings. The analysis unit uses image recognition technology and machine learning algorithms to identify ingredients and seasonings such as tomatoes, onions, and soy sauce. Step 3: The suggestion unit proposes optimal recipes based on the ingredients and seasonings identified by the analysis unit. The suggestion unit can propose recipes taking into consideration the user's preferences, nutritional balance, cooking time, etc. Step 4: The providing unit provides an image of the completed dish, as well as calories and nutritional balance, based on the recipe proposed by the suggesting unit. The providing unit can provide an image of the completed dish, calories and nutritional balance, using an image generation algorithm and a nutrition calculation algorithm.

[0062] (Example 2) A cooking suggestion system according to an embodiment of the present invention allows a user to take videos of ingredients and seasonings using a smartphone, and an AI analyzes the videos to provide optimal recipes, images of the finished dish, and calorie and nutritional balance information in real time. The cooking suggestion system allows a user to take videos of ingredients and seasonings using a smartphone, and an AI analyzes the videos to identify the types of ingredients and seasonings and suggest optimal recipes. Furthermore, based on the suggested recipe, the AI ​​provides an image of the finished dish, calories, and nutritional balance. For example, the cooking suggestion system simply requires a user to take a photo of ingredients and seasonings in their kitchen using a smartphone camera. The cooking suggestion system then analyzes the input video using AI to identify the types of ingredients and seasonings. For example, it identifies ingredients and seasonings such as tomatoes, onions, and soy sauce. Next, the cooking suggestion system suggests optimal recipes based on the analyzed content. For example, it suggests a recipe for a dish using tomatoes, onions, and soy sauce. Next, based on the suggested recipe, the AI ​​provides an image of the finished dish, calories, and nutritional balance. For example, it displays an image of the finished suggested dish, as well as the calories and nutritional balance of the dish. This allows the user to easily get ideas and enjoy creative cooking at home. It also supports the realization of healthy meals, providing users with a better diet. This allows the user to easily get ideas and enjoy creative cooking at home. It also supports the realization of healthy meals, providing users with a better diet.

[0063] A cooking suggestion system according to an embodiment includes a receiving unit, an analysis unit, a suggestion unit, and a providing unit. The receiving unit allows a user to take videos of ingredients and seasonings using a smartphone. When taking videos of ingredients and seasonings using a smartphone, the user may simply take a photo of the ingredients and seasonings in the kitchen using the smartphone camera. The analysis unit analyzes the video received by the receiving unit and identifies the types of ingredients and seasonings. The analysis unit identifies ingredients and seasonings, such as tomatoes, onions, and soy sauce. The analysis unit can identify ingredients and seasonings in the video using image recognition technology or machine learning algorithms. The suggestion unit suggests optimal recipes based on the ingredients and seasonings identified by the analysis unit. The suggestion unit suggests, for example, recipes using tomatoes, onions, and soy sauce. The suggestion unit can suggest recipes taking into consideration the user's preferences, nutritional balance, cooking time, and the like. The providing unit provides images of the completed dishes, as well as calories and nutritional balance, based on the recipes suggested by the suggestion unit. The presentation unit displays, for example, an image of the completed proposed dish, as well as the calories and nutritional balance of the dish. The presentation unit can provide the completed dish image, calories, and nutritional balance using an image generation algorithm and a nutrition calculation algorithm. This allows the dish suggestion system according to the embodiment to easily get ideas and enjoy creative cooking at home. It also supports the realization of healthy meals and provides users with a better diet.

[0064] The cooking recommendation system includes a reception unit that estimates a user's emotions and adjusts the timing of video recording based on the estimated user emotions. The reception unit estimates the user's emotions and adjusts the timing of video recording based on the estimated user emotions. For example, if a user is feeling stressed, the AI ​​can prompt the user to record a video at a time when the user is able to relax. Also, if the user is excited, the AI ​​can immediately start recording a video to capture that moment. Also, if the user is tired, the AI ​​can suggest recording a video after a break. This allows video recording to be optimally timed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 above-described processing in the reception unit may be performed using an AI, for example, or without an AI. For example, the reception unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0065] The cooking suggestion system includes a reception unit that analyzes a user's past shooting history and selects the optimal shooting method. The reception unit analyzes the user's past shooting history and selects the optimal shooting method. For example, the reception unit suggests the optimal shooting method based on the user's past preferred shooting angles and lighting conditions. The reception unit can also analyze successful videos the user has shot in the past and automatically apply similar settings. The system can also select the optimal shooting method by taking into account shooting conditions the user has avoided in the past. This enables better video shooting by selecting the optimal shooting method based on the past shooting history. The analysis of the past shooting history is performed using, for example, data mining or machine learning algorithms. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input past shooting history data into a generation AI and have the generation AI select the optimal shooting method.

[0066] The cooking suggestion system includes a reception unit that performs filtering based on the user's current ingredient inventory when shooting a video. The reception unit performs filtering based on the user's current ingredient inventory when shooting a video. For example, when a user shoots a video of ingredients in the refrigerator, an AI checks the inventory and removes any duplicate ingredients. If an ingredient is in short supply in a video shot by the user, the AI ​​can suggest alternative ingredients to make up for the shortage. If an ingredient is in excess in a video shot by the user, the AI ​​can perform filtering to adjust the amount to an appropriate level. This filtering based on the current ingredient inventory prevents duplication or shortages. The ingredient inventory is grasped, for example, using an inventory management system or barcode scanning. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI. For example, the reception unit can input current ingredient inventory data into a generation AI and have the generation AI perform filtering.

[0067] The cooking suggestion system includes a reception unit that selects a shooting means according to a user's input method when shooting a video. The reception unit selects a shooting means according to the user's input method when shooting a video. For example, when a user gives instructions by voice, an AI can select the optimal shooting means using voice recognition. When a user gives instructions by text, the AI ​​can perform text analysis and select the optimal shooting means. When a user provides images, the AI ​​can perform image analysis and select the optimal shooting means. This enables efficient video shooting by selecting the optimal shooting means according to the user's input method. The selection of the shooting means according to the input method is performed using, for example, voice recognition technology, text analysis technology, or image analysis technology. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data to a generation AI and have the generation AI select the optimal shooting means.

[0068] The recipe suggestion system includes a reception unit that estimates a user's emotions and prioritizes ingredients to be photographed based on the estimated user emotions. The reception unit estimates the user's emotions and prioritizes ingredients to be photographed based on the estimated user emotions. For example, if the user is excited, the AI ​​can prioritize photographing fresh ingredients to match the user's emotions. Also, if the user is relaxed, the AI ​​can prioritize photographing ingredients that are easy to cook to match the user's emotions. Also, if the user is tired, the AI ​​can prioritize photographing ingredients that are nutritious to match the user's emotions. This allows for more appropriate ingredients to be photographed by prioritizing ingredients to be photographed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 above-described processing in the reception unit may be performed using an AI, for example, or without an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of ingredients to be photographed.

[0069] The recipe suggestion system includes a reception unit that, when shooting a video, prioritizes capturing highly relevant ingredients in consideration of the user's geographical location information. The reception unit, when shooting a video, prioritizes capturing highly relevant ingredients in consideration of the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes capturing ingredients that are easily available in that area. Also, when the user is traveling, the reception unit can prioritize capturing images of local specialties. Also, when the user is at home, the reception unit can prioritize capturing images of ingredients that can be purchased at a nearby supermarket. In this way, highly relevant ingredients can be prioritized by taking the geographical location information into consideration. The geographical location information can be taken into consideration using, for example, GPS data or region-specific ingredient information. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input the user's geographical location data into a generation AI and cause the generation AI to select highly relevant ingredients.

[0070] The dish recommendation system includes a reception unit that analyzes a user's social media activity and captures related ingredients when shooting a video. The reception unit analyzes the user's social media activity and captures related ingredients when shooting a video. For example, the reception unit prioritizes capturing images of ingredients related to a dish shared by the user on social media. The reception unit can also analyze the user's social media posts and capture related ingredients. The system can also capture related ingredients by referring to the activities of the user's friends on social media. In this way, related ingredients can be captured by analyzing social media activity. The analysis of social media activity is performed, for example, by analyzing the content of posts and hashtags. Some or all of the above-mentioned processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and have the generation AI select related ingredients.

[0071] The cooking suggestion system includes a reception unit that customizes the shooting method by reflecting the user's past feedback when shooting a video. The reception unit customizes the shooting method by reflecting the user's past feedback when shooting a video. For example, the reception unit suggests an optimal shooting method based on shooting methods that the user has previously preferred. The system can also select an optimal shooting method by taking into account shooting methods that the user has previously avoided. The system can also customize the shooting method by analyzing the user's past feedback. In this way, the shooting method can be customized by reflecting the past feedback. The reflection of the past feedback is performed, for example, by using user ratings and comment analysis. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into a generation AI and have the generation AI customize the shooting method.

[0072] The dish recommendation system includes an analysis unit that estimates a user's emotions and adjusts the presentation method of the analysis based on the estimated user emotions. The analysis unit estimates the user's emotions and adjusts the presentation method of the analysis based on the estimated user emotions. For example, if the user is relaxed, the analysis results can be displayed in a visually easy-to-understand manner. If the user is in a hurry, the analysis results can be displayed concisely. If the user is excited, the analysis results can be displayed in detail. This allows for adjusting the presentation method of the analysis according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0073] The dish recommendation system includes an analysis unit that adjusts the level of detail of the analysis based on the freshness and quality of the ingredients during analysis. The analysis unit adjusts the level of detail of the analysis based on the freshness and quality of the ingredients during analysis. For example, detailed analysis results are provided for highly fresh ingredients. Detailed analysis results can also be provided for high-quality ingredients. Brief analysis results can also be provided for ingredients with low freshness or quality. By adjusting the level of detail of the analysis based on the freshness and quality of the ingredients, more accurate analysis results can be provided. The freshness and quality of the ingredients are evaluated using, for example, a freshness sensor or a quality evaluation algorithm. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input freshness data of ingredients to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0074] The dish recommendation system includes an analysis unit that applies different analysis algorithms depending on the category of ingredients during analysis. The analysis unit applies different analysis algorithms depending on the category of ingredients during analysis. For example, for vegetables, an analysis algorithm based on nutritional value is applied. For meat, an analysis algorithm based on cooking method can also be applied. For seasonings, an analysis algorithm based on the amount used can also be applied. By applying different analysis algorithms depending on the category of ingredients, more appropriate analysis results can be provided. The application of analysis algorithms depending on the category of ingredients is performed using, for example, an algorithm for vegetables and an algorithm for meat. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input ingredient category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0075] The dish recommendation system includes an analysis unit that, during analysis, refers to the user's past analysis results to improve the accuracy of the analysis. The analysis unit, during analysis, refers to the user's past analysis results to improve the accuracy of the analysis. For example, the analysis accuracy is improved based on data on ingredients that the user has previously analyzed. The analysis algorithm can also be optimized by analyzing the user's past analysis results. The analysis accuracy can also be improved by referring to the user's past feedback. In this way, the analysis accuracy can be improved by referring to the past analysis results. The reference to the past analysis results is performed, for example, by using historical data or a feedback loop. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0076] The dish recommendation system includes an analysis unit that estimates a user's emotions and adjusts the length of the analysis based on the estimated user emotions. The analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. If the user is excited, the analysis unit can provide a visually stimulating analysis result. This allows for adjusting the length of the analysis according to the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 above-described processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0077] The dish recommendation system includes an analysis unit that determines analysis priorities based on when ingredients were obtained during analysis. The analysis unit determines analysis priorities based on when ingredients were obtained during analysis. For example, analysis is prioritized for fresh ingredients. Analysis can also be prioritized for ingredients close to their expiration date. Analysis can also be prioritized for seasonal ingredients. In this way, by determining analysis priorities based on when ingredients were obtained, more appropriate analysis results can be provided. The acquisition date of ingredients is evaluated using, for example, the acquisition date or expiration date. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data on when ingredients were obtained to the generation AI, and have the generation AI determine the analysis priorities.

[0078] The dish recommendation system includes an analysis unit that adjusts the order of analysis based on the relevance of ingredients during analysis. The analysis unit adjusts the order of analysis based on the relevance of ingredients during analysis. For example, ingredients used in the same dish may be analyzed with priority. In addition, ingredients in the same category may be analyzed with priority. In addition, ingredients preferred by the user may be analyzed with priority. By adjusting the order of analysis based on the relevance of ingredients, more appropriate analysis results can be provided. The relevance of ingredients is evaluated using, for example, combinations of ingredients or co-occurrence relationships. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit may input ingredient relevance data into a generation AI and have the generation AI adjust the order of analysis.

[0079] The dish recommendation system includes an analysis unit that adjusts the use of technical terms in the analysis according to the user's level of expertise during analysis. The analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user is a beginner, the analysis unit can provide analysis results in simple language. If the user is an intermediate user, the analysis unit can provide analysis results using appropriate technical terms. If the user is an advanced user, the analysis unit can provide analysis results using detailed technical terms. This allows for more appropriate analysis results to be provided by adjusting the use of technical terms in the analysis according to the user's level of expertise. The level of expertise is evaluated, for example, using the user's past usage history and feedback. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's expertise data into a generation AI and have the generation AI execute the use of technical terms.

[0080] The dish suggestion system includes a suggestion unit that estimates a user's emotions and adjusts the way suggestions are presented based on the estimated user emotions. The suggestion unit estimates the user's emotions and adjusts the way suggestions are presented based on the estimated user emotions. For example, if the user is relaxed, the suggestion unit provides visually easy-to-understand suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions. If the user is excited, the suggestion unit can provide detailed suggestions. This allows for more appropriate suggestions to be presented by adjusting the way suggestions are presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit may input user emotion data into the generation AI and cause the generation AI to adjust the way suggestions are presented.

[0081] The cooking suggestion system includes a suggestion unit that adjusts the level of detail of the suggestion based on the importance of the recipe when suggesting the recipe. The suggestion unit adjusts the level of detail of the suggestion based on the importance of the recipe when suggesting the recipe. For example, a detailed suggestion is made for an important recipe. A concise suggestion can also be made for a simple recipe. A detailed suggestion can also be made for a recipe in which the user is particularly interested. By adjusting the level of detail of the suggestion based on the importance of the recipe, more appropriate suggestions can be made. The importance of a recipe is evaluated using, for example, main ingredients and cooking time. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input recipe importance data to a generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0082] The cooking recommendation system includes a suggestion unit that applies different suggestion algorithms depending on the recipe category when making a suggestion. The suggestion unit applies different suggestion algorithms depending on the recipe category when making a suggestion. For example, for desserts, a suggestion algorithm based on sweetness and calories can be applied. For main dishes, a suggestion algorithm based on nutritional value and cooking time can be applied. For side dishes, a suggestion algorithm based on ease of preparation can be applied. By applying different suggestion algorithms depending on the recipe category, more appropriate suggestions can be made. The suggestion algorithm depending on the recipe category is applied using, for example, an algorithm for desserts and an algorithm for main dishes. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input recipe category data into a generation AI and cause the generation AI to apply a suggestion algorithm.

[0083] The cooking suggestion system includes a suggestion unit that, when making a suggestion, improves the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit improves the accuracy of the suggestion by referring to the user's past suggestion results. For example, the suggestion unit improves the accuracy of the suggestion based on recipes that the user has previously preferred. The suggestion unit can also analyze the user's past suggestion results and optimize the suggestion algorithm. The suggestion unit can also improve the accuracy of the suggestion by referring to the user's past feedback. In this way, the suggestion accuracy can be improved by referring to the past suggestion results. The reference to the past suggestion results is performed, for example, by using historical data or a feedback loop. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion data into the generation AI and cause the generation AI to improve the accuracy of the suggestions.

[0084] The dish suggestion system includes a suggestion unit that estimates a user's emotions and adjusts the length of suggestions based on the estimated user emotions. The suggestion unit estimates the user's emotions and adjusts the length of suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit may provide short, concise suggestions. If the user is relaxed, the suggestion unit may provide detailed suggestions. If the user is excited, the suggestion unit may provide visually stimulating suggestions. This allows for more appropriate suggestions by adjusting the length of suggestions according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the suggestions.

[0085] The cooking suggestion system includes a suggestion unit that, at the time of suggestion, determines the priority of suggestions based on the time of recipe submission. The suggestion unit, at the time of suggestion, determines the priority of suggestions based on the time of recipe submission. For example, seasonal recipes are given priority in suggestion. Recipes related to a specific event can also be given priority in suggestion. Recipes that a user prefers during a specific time period can also be given priority in suggestion. In this way, by determining the priority of suggestions based on the time of recipe submission, more appropriate suggestions can be made. The time of recipe submission is evaluated using, for example, the submission date or seasonal ingredients. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can input recipe submission time data into a generation AI and have the generation AI determine the priority of suggestions.

[0086] The cooking recommendation system includes a suggestion unit that adjusts the order of suggestions based on the relevance of the recipes when making suggestions. The suggestion unit adjusts the order of suggestions based on the relevance of the recipes when making suggestions. For example, related recipes may be preferentially suggested based on ingredients used in the same dish. Recipes in the same category may also be preferentially suggested. Recipes that are preferred by the user may also be preferentially suggested. This allows for more appropriate suggestions by adjusting the order of suggestions based on the relevance of the recipes. The relevance of the recipes is evaluated using, for example, combinations of ingredients or co-occurrence relationships. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit may input recipe relevance data into a generation AI and cause the generation AI to adjust the order of suggestions.

[0087] The dish suggestion system includes a suggestion unit that adjusts the use of technical terms in the suggestion according to the user's level of expertise when making a suggestion. The suggestion unit adjusts the use of technical terms in the suggestion according to the user's level of expertise when making a suggestion. For example, if the user is a beginner, the suggestion unit can make the suggestion using simple language. If the user is an intermediate user, the suggestion unit can make the suggestion using appropriate technical terms. If the user is an advanced user, the suggestion unit can make the suggestion using detailed technical terms. This allows for more appropriate suggestions to be made by adjusting the use of technical terms in the suggestion according to the user's level of expertise. The level of expertise is evaluated, for example, using the user's past usage history and feedback. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can input the user's expertise data into a generation AI and cause the generation AI to use technical terms.

[0088] The dish recommendation system includes a providing unit that estimates a user's emotions and adjusts the presentation method of information to be provided based on the estimated user emotions. The providing unit estimates the user's emotions and adjusts the presentation method of information to be provided based on the estimated user emotions. For example, if the user is relaxed, visually easy-to-understand information can be provided. If the user is in a hurry, concise information can be provided. If the user is excited, detailed information can be provided. This allows for adjusting the presentation method of information to be provided according to the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation 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 above-described processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit may input user emotion data into the generation AI and cause the generation AI to adjust the presentation method of information.

[0089] The dish recommendation system includes a providing unit that adjusts the level of detail of the information to be provided based on the degree of completion of the dish when the information is provided. The providing unit adjusts the level of detail of the information to be provided based on the degree of completion of the dish when the information is provided. For example, detailed information is provided for a highly completed dish. Brief information can also be provided for a simple dish. Detailed information can also be provided for a dish in which the user is particularly interested. In this way, by adjusting the level of detail of the information to be provided based on the degree of completion of the dish, more appropriate information can be provided. The degree of completion of the dish is evaluated using, for example, an evaluation standard for the degree of completion or a level of detailed information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the degree of completion of the dish to a generating AI and have the generating AI adjust the level of detail of the information.

[0090] The dish recommendation system includes a serving unit that applies different serving algorithms depending on the category of the dish when serving the food. The serving unit applies different serving algorithms depending on the category of the dish when serving the food. For example, for desserts, a serving algorithm based on sweetness and calories can be applied. For main dishes, a serving algorithm based on nutritional value and cooking time can also be applied. For side dishes, a serving algorithm based on ease of cooking can also be applied. By applying different serving algorithms depending on the category of the dish, more appropriate information can be provided. The application of a serving algorithm depending on the category of the dish is performed using, for example, an algorithm for desserts and an algorithm for main dishes. Some or all of the above-mentioned processing in the serving unit may be performed using, for example, AI, or may be performed without using AI. For example, the serving unit can input dish category data into a generation AI and cause the generation AI to apply a serving algorithm.

[0091] The dish recommendation system includes a serving unit that, when serving, improves the accuracy of serving by referring to the user's past serving results. The serving unit, when serving, improves the accuracy of serving by referring to the user's past serving results. For example, the serving accuracy is improved based on the user's past favorite dishes. The serving algorithm can also be optimized by analyzing the user's past serving results. The serving accuracy can also be improved by referring to the user's past feedback. In this way, the serving accuracy can be improved by referring to the past serving results. The reference to the past serving results is performed, for example, by using historical data or a feedback loop. Some or all of the above-mentioned processing in the serving unit may be performed, for example, using AI, or may be performed without using AI. For example, the serving unit can input the user's past serving data into the generation AI and cause the generation AI to improve the accuracy of serving.

[0092] The dish recommendation system includes a providing unit that estimates a user's emotions and adjusts the length of information to be provided based on the estimated user emotions. The providing unit estimates the user's emotions and adjusts the length of information to be provided based on the estimated user emotions. For example, if the user is in a hurry, short, concise information can be provided. If the user is relaxed, detailed information can be provided. If the user is excited, visually stimulating information can be provided. This allows for adjusting the length of information to be provided according to the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without AI. For example, the providing unit may input user emotion data into the generation AI and cause the generation AI to adjust the length of the information.

[0093] The dish recommendation system includes a providing unit that, at the time of provision, determines the priority of information to be provided based on the time of submission of the dish. The providing unit, at the time of provision, determines the priority of information to be provided based on the time of submission of the dish. For example, in the case of seasonal dishes, information is provided preferentially. In addition, information can be provided preferentially in the case of dishes related to a specific event. In addition, information can be provided preferentially for dishes that the user prefers during a specific time period. In this way, by determining the priority of information to be provided based on the time of submission of the dish, more appropriate information can be provided. The time of submission of the dish is evaluated using, for example, the submission date or seasonal ingredients. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the time of submission of the dish to a generation AI and have the generation AI determine the priority of the information.

[0094] The dish recommendation system includes a providing unit that adjusts the order of information to be provided based on the relevance of dishes when providing the information. The providing unit adjusts the order of information to be provided based on the relevance of dishes when providing the information. For example, related information may be provided preferentially based on ingredients used in the same dish. Information on dishes in the same category may also be provided preferentially. Information on dishes preferred by the user may also be provided preferentially. In this way, by adjusting the order of information to be provided based on the relevance of dishes, more appropriate information can be provided. The relevance of dishes is evaluated using, for example, combinations of ingredients or co-occurrence relationships. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input dish relevance data to a generation AI and cause the generation AI to adjust the order of the information.

[0095] The dish recommendation system includes a providing unit that adjusts the use of technical terms in the information to be provided according to the user's level of expertise. The providing unit adjusts the use of technical terms in the information to be provided according to the user's level of expertise. For example, if the user is a beginner, the providing unit can provide information using simple language. If the user is an intermediate user, the providing unit can provide information using appropriate technical terms. If the user is an advanced user, the providing unit can provide information using detailed technical terms. This allows for more appropriate information to be provided by adjusting the use of technical terms in the information to be provided according to the user's level of expertise. The level of expertise is evaluated, for example, using the user's past usage history and feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's expertise data into a generation AI and have the generation AI execute the use of technical terms. === Hard Collateral 1-1 === For example, each of the multiple elements including the reception unit, analysis unit, suggestion unit, and provision unit is realized by at least one of the smart device 14 and the data processing device 12. For example, the reception unit accepts user input using the camera 42 or microphone 38B of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes video to identify the types of ingredients and seasonings. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and proposes an optimal recipe. The provision unit is realized by the control unit 46A of the smart device 14 and displays an image of the completed dish, as well as calories and nutritional balance. === Hard Collateral 1-2 === For example, each of the multiple elements including the reception unit, analysis unit, suggestion unit, and provision unit is realized by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit accepts user input using the camera 42 or microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes video to identify the types of ingredients and seasonings. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests optimal recipes. The provision unit is realized by the control unit 46A of the smart glasses 214 and displays an image of the completed dish, as well as calories and nutritional balance. === Hard Collateral 1-3 === For example, each of the multiple elements including the reception unit, analysis unit, suggestion unit, and provision unit is realized by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit accepts user input using the camera 42 or microphone 238 of the headset terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes video to identify the types of ingredients and seasonings. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and proposes an optimal recipe. The provision unit is realized by the control unit 46A of the headset terminal 314, and displays an image of the completed dish, as well as calories and nutritional balance. === Hard Collateral 1-4 === For example, each of the multiple elements including the reception unit, analysis unit, suggestion unit, and provision unit is realized by at least one of the robot 414 and the data processing device 12. For example, the reception unit accepts user input using the camera 42 or microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the video to identify the types of ingredients and seasonings. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests an optimal recipe. The provision unit is realized by the control unit 46A of the robot 414 and displays an image of the completed dish, as well as calories and nutritional balance.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The cooking recommendation system includes a suggestion unit that analyzes the frequency of use of ingredients by the user and prioritizes suggesting frequently used ingredients. For example, ingredients such as tomatoes and onions that the user frequently uses are prioritized. The system can also identify and suggest frequently used ingredients based on the user's history of ingredients used in the past. Furthermore, if the user prefers a particular ingredient, it can prioritize suggesting recipes that include that ingredient. This allows the system to suggest optimal recipes based on the frequency of use of ingredients by the user, thereby enabling suggestions that match the user's preferences.

[0098] The cooking recommendation system includes a suggestion unit that estimates the user's emotions and adjusts the difficulty of recipes based on the estimated user emotions. For example, if the user is relaxed, a more difficult recipe can be suggested. If the user is tired, an easy recipe can be suggested. Furthermore, if the user is excited, a challenging recipe can be suggested. This allows for more appropriate suggestions by adjusting the difficulty of the recipe according to the user's emotions.

[0099] The cooking recommendation system includes a suggestion unit that analyzes the user's past meal history and suggests balanced meals. For example, if the user has not eaten many vegetables in the past, recipes containing a lot of vegetables can be suggested. Also, if the user has eaten many high-calorie meals in the past, low-calorie recipes can be suggested. Furthermore, if the user is lacking in a particular nutrient, recipes containing that nutrient can be suggested. In this way, by suggesting balanced meals based on the user's past meal history, it is possible to support a healthy eating lifestyle.

[0100] The cooking recommendation system includes a suggestion unit that estimates the user's emotions and suggests ingredient combinations based on the estimated user emotions. For example, if the user is relaxed, the system suggests recipes that combine ingredients with a relaxing effect. If the user is stressed, the system can suggest recipes that combine ingredients with a stress-reducing effect. Furthermore, if the user is excited, the system can suggest recipes that combine ingredients that replenish energy. This allows the system to suggest more appropriate ingredient combinations based on the user's emotions.

[0101] The cooking recommendation system includes a recommendation unit that analyzes the storage status of the user's ingredients and prioritizes recommendations for ingredients that are difficult to store. For example, it prioritizes recommendations for fresh vegetables and fruits that are difficult to store. It can also prioritize recommendations for ingredients that are close to their expiration date. Furthermore, it can monitor the storage status of ingredients in the refrigerator in real time and prioritize recommendations for ingredients that are difficult to store. This makes it possible to reduce food waste by suggesting optimal recipes based on the storage status of the user's ingredients.

[0102] The cooking recommendation system includes a suggestion unit that estimates the user's emotions and adjusts the cooking time based on the estimated user's emotions. For example, if the user is relaxed, a recipe with a long cooking time can be suggested. If the user is in a hurry, a recipe with a short cooking time can be suggested. Furthermore, if the user is excited, a recipe with a moderate cooking time can be suggested. This allows for more appropriate suggestions by adjusting the cooking time according to the user's emotions.

[0103] The cooking recommendation system includes a suggestion unit that analyzes a user's ingredient purchase history and prioritizes recommendations of ingredients that are frequently purchased. For example, ingredients such as chicken and tomatoes that are frequently purchased by the user are prioritized. The system can also identify and recommend ingredients that are frequently purchased by the user based on the user's past purchase history. Furthermore, if a user has a preference for a particular ingredient, it can prioritize recommendations of recipes that include that ingredient. This allows the system to suggest optimal recipes based on the user's purchase history, thereby enabling recommendations that match the user's preferences.

[0104] The cooking recommendation system includes a presentation unit that estimates the user's emotions and adjusts the presentation method of the recipe based on the estimated user's emotions. For example, if the user is relaxed, a visually beautiful presentation can be provided. If the user is in a hurry, a concise and easy-to-understand presentation can be provided. Furthermore, if the user is excited, a detailed and interesting presentation can be provided. In this way, by adjusting the presentation method of the recipe according to the user's emotions, more appropriate information can be provided.

[0105] The cooking recommendation system includes a suggestion unit that analyzes the nutritional value of the user's ingredients and suggests nutritionally balanced recipes. For example, if the user is deficient in a particular nutrient, the system suggests recipes that contain that nutrient. It can also suggest recipes that limit the nutrient the user is consuming in excess. It can also suggest nutritionally balanced recipes based on the user's health condition. This allows the system to support a healthy diet by suggesting optimal recipes based on the user's nutritional value.

[0106] The cooking recommendation system includes a suggestion unit that estimates the user's emotions and customizes recipes based on the estimated user emotions. For example, if the user is relaxed, the system can suggest recipes that add elements that make cooking fun. If the user is in a hurry, the system can also suggest recipes that incorporate time-saving techniques. Furthermore, if the user is excited, the system can suggest recipes that incorporate new cooking methods or ingredients. This allows for more appropriate suggestions by customizing recipes according to the user's emotions.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The reception unit allows the user to take a video of the ingredients and seasonings using their smartphone. All the user needs to do is take a video of the ingredients and seasonings in their kitchen using their smartphone camera. Step 2: The analysis unit analyzes the video received by the reception unit and identifies the types of ingredients and seasonings. The analysis unit uses image recognition technology and machine learning algorithms to identify ingredients and seasonings such as tomatoes, onions, and soy sauce. Step 3: The suggestion unit proposes optimal recipes based on the ingredients and seasonings identified by the analysis unit. The suggestion unit can propose recipes taking into consideration the user's preferences, nutritional balance, cooking time, etc. Step 4: The providing unit provides an image of the completed dish, as well as calories and nutritional balance, based on the recipe proposed by the suggesting unit. The providing unit can provide an image of the completed dish, calories and nutritional balance, using an image generation algorithm and a nutrition calculation algorithm.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 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.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0149] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0151] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0152] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0153] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0154] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0155] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0156] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0157] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0159] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0164] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0165] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0166] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0170] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0171] 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.

[0172] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0173] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0174] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0175] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0177] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0178] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0179] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0180] [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception desk where users can take videos of ingredients and seasonings using their smartphones; an analysis unit that analyzes the video received by the reception unit and identifies the types of ingredients and seasonings; a suggestion unit that suggests recipes based on the ingredients and seasonings identified by the analysis unit; a providing unit that provides an image of the completed dish, calories, and nutritional balance based on the recipe proposed by the proposing unit. A system characterized by:

2. The reception unit Estimate the user's emotions and adjust the timing of video recording based on the estimated user emotions.

2. The system of claim 1.

3. The reception unit Analyze the user's past photography history and select the photography method 2. The system of claim 1.

4. The reception unit When shooting video, filtering is performed based on the user's current food inventory.

2. The system of claim 1.

5. The reception unit When shooting video, select the shooting method according to the user's input method 2. The system of claim 1.

6. The reception unit Estimate the user's emotions and prioritize ingredients to photograph based on the estimated emotions.

2. The system of claim 1.

7. The reception unit When shooting video, the system takes into account the user's geographic location information and prioritizes the capture of relevant ingredients.

2. The system of claim 1.

8. The reception unit When shooting a video, the app analyzes the user's social media activity and captures related ingredients.

2. The system of claim 1.

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