system

Abstract

The system according to the embodiment aims to efficiently unify cooking recipes and make them easier to manage. [Solution] A system according to an embodiment includes a photographing unit, an analysis unit, a processing unit, and a storage unit. The photographing unit takes a photograph or captures an image of a recipe. The analysis unit analyzes the image photographed by the photographing unit and converts it into text data. The processing unit analyzes the text data converted by the analysis unit using natural language processing technology and formats it into a template format. The storage unit saves the recipe data formatted by the processing unit.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that managing cooking recipes is complicated and it is difficult to centralize them efficiently.

[0005] The system according to the embodiment aims to efficiently unify cooking recipes and make them easier to manage. [Means for solving the problem]

[0006] The system according to the embodiment includes a photographing unit, an analysis unit, a processing unit, and a storage unit. The photographing unit takes a photograph or captures an image of a recipe. The analysis unit analyzes the image photographed by the photographing unit and converts it into text data. The processing unit analyzes the text data converted by the analysis unit using natural language processing technology and formats it into a template format. The storage unit stores the recipe data formatted by the processing unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently unify cooking recipes and make them easier to manage. [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 recipe management system according to an embodiment of the present invention is designed for people struggling with managing cooking recipes. This recipe management system centralizes and easily manages favorite recipes from recipe books and apps. Users use their smartphones to take photos or capture images of recipes, analyze the images, and convert them into text data. The converted text data is then formatted into a template format using natural language processing technology. This allows users to easily manage recipes in their preferred format. Specifically, the system consists of the following steps: First, a user takes a photo or captures an image of a recipe using their smartphone. For example, they can take a photo of a page from a recipe book. The image is converted into text data using technology that analyzes the text within the image. Next, the converted text data is analyzed using natural language processing technology and formatted into a template format. For example, the recipe's ingredients and steps are organized for easy viewing and formatting in the user's preferred format. Finally, the formatted recipe data is saved on the user's smartphone for easy access at any time. Users can manage recipes in their preferred format, achieving easy-to-view and easy-to-use recipe management. This service simplifies recipe management, allowing users to centralize and efficiently manage their favorite recipes. This allows the recipe management system to be easily managed by the user in a format of their choice.

[0029] A recipe management system according to an embodiment includes a photographing unit, an analysis unit, a processing unit, and a storage unit. The photographing unit photographs or captures images of recipes. For example, the photographing unit can photograph pages of a recipe book using a smartphone camera. The photographing unit can also capture images using a digital camera or scanner. For example, the photographing unit can acquire high-resolution images using a digital camera. The photographing unit can also convert printed recipes into digital data using a scanner. The analysis unit analyzes the images photographed by the photographing unit and converts them into text data. For example, the analysis unit can analyze characters in the images using optical character recognition (OCR) technology and convert them into text data. The analysis unit can also perform highly accurate character analysis using deep learning-based character recognition technology. For example, the analysis unit can recognize handwritten characters using a deep learning model and convert them into text data. The processing unit analyzes the text data converted by the analysis unit using natural language processing technology and formats it into a template format. For example, the processing unit can analyze the text data using morphological analysis and perform grammatical analysis. The processing unit can also use semantic analysis to understand the meaning of the text data and format it into a template format. For example, the processing unit uses morphological analysis to divide the text data and uses grammatical analysis to analyze the structure of the sentence. Furthermore, the processing unit uses semantic analysis to understand the meaning of the text data and format it into a template format. The storage unit stores the recipe data formatted by the processing unit. For example, the storage unit stores the formatted recipe data on a user's smartphone so that it can be easily accessed at any time. The storage unit can also store the recipe data using cloud storage. For example, the storage unit stores the recipe data in cloud storage so that it can be accessed from multiple devices. This allows the recipe management system according to the embodiment to easily manage recipes in a format preferred by the user.

[0030] The analysis unit may include technology for analyzing characters in an image. The analysis unit may analyze characters in an image using, for example, OCR (optical character recognition) technology. For example, the analysis unit may recognize printed characters using OCR technology and convert them into text data. The analysis unit may also perform highly accurate character analysis using deep learning-based character recognition technology. For example, the analysis unit may recognize handwritten characters using a deep learning model and convert them into text data. This allows the characters in an image to be accurately analyzed and converted into text data. 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 may input image data to a generation AI and cause the generation AI to analyze the characters in the image.

[0031] The processing unit may include technology for performing natural language processing. The processing unit may analyze text data using, for example, morphological analysis. For example, the processing unit may divide the text data using morphological analysis and perform grammatical analysis. The processing unit may also understand the meaning of the text data using semantic analysis and format it into a template format. For example, the processing unit may divide the text data using morphological analysis and analyze the structure of the sentence using grammatical analysis. Furthermore, the processing unit may understand the meaning of the text data using semantic analysis and format it into a template format. In this way, the text data can be analyzed by natural language processing and formatted into a template format. Some or all of the above-described processing in the processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the processing unit may input text data to a generation AI and cause the generation AI to perform natural language processing.

[0032] The processing unit can organize the ingredients and steps of a recipe in an easy-to-read format and format the recipe in a format preferred by the user. For example, the processing unit organizes the ingredients and steps of a recipe in an easy-to-read format. For example, the processing unit displays the ingredients in a list format and the steps step by step. The processing unit can also format the recipe in a format preferred by the user. For example, the processing unit adjusts the font size and color based on the user's settings. Furthermore, the processing unit can adjust the format based on the user's past preference data. For example, the processing unit automatically applies a format that the user has previously preferred. This allows the recipe to be organized in a format preferred by the user in an easy-to-read format. Some or all of the above-described processing in the processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the processing unit can input recipe data to a generation AI and cause the generation AI to format the recipe.

[0033] The storage unit can store the formatted recipe data on the user's smartphone for easy access at any time. The storage unit, for example, stores the formatted recipe data on the user's smartphone. For example, the storage unit can store the recipe data in the smartphone's internal storage for easy access at any time. The storage unit can also store the recipe data using cloud storage. For example, the storage unit can store the recipe data in cloud storage for access from multiple devices. The storage unit can also make the recipe data easily searchable using a search function. For example, the storage unit can tag and classify the recipe data, allowing a specific recipe to be quickly found using the search function. This allows the user to easily access the formatted recipe data at any time. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the recipe data to a generation AI and have the generation AI select a storage method.

[0034] The photographing unit can automatically select the optimal photographing mode depending on the type of recipe when photographing. The photographing unit automatically selects the optimal photographing mode depending on, for example, the type of recipe. For example, for a dessert recipe, the photographing unit automatically selects a photographing mode with bright colors. Furthermore, for a main dish, the photographing unit can automatically select a photographing mode that emphasizes the texture of the ingredients. Furthermore, for a soup recipe, the photographing unit can automatically select a photographing mode that brings out the transparency of the liquid. This makes it possible to automatically select the optimal photographing mode depending on the type of recipe. Some or all of the above-described processing in the photographing unit may be performed using, or without, AI, for example. For example, the photographing unit can input the type of recipe to the generation AI and cause the generation AI to select the optimal photographing mode.

[0035] The photographing unit can suggest the optimal photographing angle by referring to the user's past photographing history when photographing. The photographing unit can suggest the optimal photographing angle by referring to the user's past photographing history, for example. For example, the photographing unit automatically suggests a photographing angle that the user has previously preferred. The photographing unit can also suggest the angle that looks the most beautiful based on the user's past photographing history. Furthermore, the photographing unit can also suggest the optimal photographing angle based on the user's evaluation of images taken in the past. This makes it possible to suggest the optimal photographing angle by referring to the user's past photographing history. Some or all of the above-described processing in the photographing unit may be performed using, or without, AI, for example. For example, the photographing unit can input the user's photographing history data into a generation AI and cause the generation AI to suggest the optimal photographing angle.

[0036] When photographing, the photographing unit can prioritize photographing highly relevant recipes based on the user's geographical location information. The photographing unit, for example, prioritizes photographing highly relevant recipes taking into account the user's geographical location information. For example, when the user is in a specific area, the photographing unit prioritizes photographing recipes using local specialties of that area. Furthermore, when the user is traveling, the photographing unit can prioritize photographing recipes of local specialties of the travel destination. Furthermore, when the user is at home, the photographing unit can prioritize photographing home-cooked recipes. This makes it possible to prioritize photographing highly relevant recipes taking into account the user's geographical location information. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI. For example, the photographing unit can input the user's geographical location information to the generation AI and cause the generation AI to select highly relevant recipes.

[0037] The photographing unit can analyze the user's social media activity and photograph related recipes when photographing. The photographing unit, for example, analyzes the user's social media activity. For example, the photographing unit prioritizes photographing recipes that the user has shared on social media. The photographing unit can also prioritize photographing recipes that the user has "liked" on social media. Furthermore, the photographing unit can prioritize photographing recipes from cooking bloggers that the user follows. This makes it possible to analyze the user's social media activity and photograph related recipes. Some or all of the above-described processing in the photographing unit may be performed, for example, using AI or without AI. For example, the photographing unit can input the user's social media data into a generation AI and have the generation AI select related recipes.

[0038] The analysis unit can apply an optimal analysis algorithm depending on the resolution of the image during analysis. The analysis unit applies, for example, an optimal analysis algorithm depending on the resolution of the image. For example, the analysis unit applies a detailed analysis algorithm to a high-resolution image. The analysis unit can also apply a simple analysis algorithm to a low-resolution image. Furthermore, the analysis unit can also apply a balanced analysis algorithm to a medium-resolution image. This makes it possible to apply an optimal analysis algorithm depending on the resolution of the image. 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 image data to a generation AI and cause the generation AI to select an optimal analysis algorithm.

[0039] During analysis, the analysis unit can apply different analysis methods depending on the category of the recipe. For example, the analysis unit applies different analysis methods depending on the category of the recipe. For example, the analysis unit applies a method that emphasizes color analysis to a dessert recipe. The analysis unit can also apply a method that emphasizes ingredient shape analysis to a main dish. Furthermore, the analysis unit can apply a method that emphasizes liquid transparency analysis to a soup recipe. This makes it possible to apply different analysis methods depending on the category of the recipe. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI, for example. For example, the analysis unit can input recipe data into a generation AI and have the generation AI select the optimal analysis method.

[0040] During analysis, the analysis unit can determine the analysis priority based on when the images were taken. The analysis unit determines the analysis priority based on, for example, when the images were taken. For example, the analysis unit prioritizes analyzing recently taken images. The analysis unit can also prioritize analyzing images taken during a specific event. Furthermore, the analysis unit can prioritize analyzing images taken within a period specified by the user. This makes it possible to determine the analysis priority based on when the images were taken. 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 image data to a generation AI and have the generation AI determine the analysis priority.

[0041] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the image. The analysis unit improves the accuracy of the analysis by, for example, referring to literature related to the image. For example, the analysis unit improves the analysis accuracy by referring to literature related to ingredients in the image. The analysis unit can also improve the analysis accuracy by referring to literature related to cooking procedures in the image. Furthermore, the analysis unit can improve the analysis accuracy by referring to literature related to the names of dishes in the image. This makes it possible to improve the analysis accuracy by referring to literature related to the image. 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 related literature data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0042] During processing, the processing unit can adjust the level of detail of the template based on the importance of the recipe. The processing unit adjusts the level of detail of the template based on, for example, the importance of the recipe. For example, the processing unit applies a detailed template to an important recipe. The processing unit can also apply a standard template to a general recipe. Furthermore, the processing unit can also apply a simplified template to a simple recipe. In this way, the level of detail of the template can be adjusted based on the importance of the recipe. Some or all of the above-described processing in the processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the processing unit can input recipe data to a generation AI and cause the generation AI to adjust the level of detail of the template.

[0043] During processing, the processing unit can apply different template formats depending on the category of the recipe. For example, the processing unit applies different template formats depending on the category of the recipe. For example, the processing unit applies a colorful template to a dessert recipe. The processing unit can also apply a template that emphasizes the texture of ingredients to a main dish. Furthermore, the processing unit can apply a template that enhances the transparency of the liquid to a soup recipe. This makes it possible to apply the optimal template format depending on the category of the recipe. Some or all of the above-mentioned processing in the processing unit may be performed using, or without, AI, for example. For example, the processing unit can input recipe data to a generation AI and cause the generation AI to select the optimal template format.

[0044] During processing, the processing unit can determine the priority of templates based on the time of recipe submission. The processing unit determines the priority of templates based on, for example, the time of recipe submission. For example, the processing unit may prioritize recently submitted recipes as templates. The processing unit may also prioritize recipes submitted during a specific event as templates. Furthermore, the processing unit may also prioritize recipes submitted within a period specified by the user as templates. This makes it possible to determine the priority of templates based on the time of recipe submission. Some or all of the above-described processing in the processing unit may be performed using, or without, AI. For example, the processing unit may input recipe data to a generation AI and cause the generation AI to determine the priority of templates.

[0045] During processing, the processing unit can adjust the order of templates based on the relevance of the recipes. The processing unit adjusts the order of templates based on, for example, the relevance of the recipes. For example, the processing unit prioritizes creating templates from highly relevant recipes. The processing unit can also postpone creating templates from less relevant recipes. Furthermore, the processing unit can adjust the order of templates based on the relevance specified by the user. This makes it possible to adjust the order of templates based on the relevance of the recipes. Some or all of the above-described processing in the processing unit may be performed using, or without, AI, for example. For example, the processing unit can input recipe data to a generation AI and cause the generation AI to adjust the order of the templates.

[0046] The storage unit can select the optimal storage method by referring to the user's past storage history when saving. The storage unit selects the optimal storage method by referring to the user's past storage history, for example. For example, the storage unit automatically selects a format that the user has frequently saved in the past. The storage unit can also suggest the most efficient storage method based on the user's past storage history. Furthermore, the storage unit can select the optimal storage method based on evaluations of recipes that the user has saved in the past. In this way, the optimal storage method can be selected by referring to the user's past storage history. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the user's storage history data to a generation AI and have the generation AI select the optimal storage method.

[0047] The storage unit can apply different storage formats depending on the category of the recipe when saving. For example, the storage unit applies different storage formats depending on the category of the recipe. For example, the storage unit applies a colorful storage format to a dessert recipe. The storage unit can also apply a storage format that emphasizes the texture of ingredients to a main dish. Furthermore, the storage unit can apply a storage format that highlights the transparency of the liquid to a soup recipe. In this way, different storage formats can be applied depending on the category of the recipe. Some or all of the above-mentioned processing in the storage unit may be performed using AI, for example, or may be performed without using AI. For example, the storage unit can input recipe data to a generation AI and cause the generation AI to select the optimal storage format.

[0048] When saving data, the storage unit can prioritize saving highly relevant data by taking into account the user's geographical location information. The storage unit, for example, prioritizes saving highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the storage unit can prioritize saving recipes using local specialties of that area. Furthermore, when the user is traveling, the storage unit can prioritize saving recipes for local specialties of the travel destination. Furthermore, when the user is at home, the storage unit can prioritize saving recipes for home-cooked meals. This makes it possible to prioritize saving highly relevant data by taking into account the user's geographical location information. Some or all of the above-described processing in the storage unit may be performed using AI, for example, or may be performed without using AI. For example, the storage unit can input the user's geographical location information to the generation AI and cause the generation AI to select highly relevant data.

[0049] The storage unit can analyze the user's social media activity and store related data when saving. The storage unit, for example, analyzes the user's social media activity. For example, the storage unit can prioritize saving recipes that the user has shared on social media. The storage unit can also prioritize saving recipes that the user has "liked" on social media. Furthermore, the storage unit can prioritize saving recipes from cooking bloggers that the user follows. This makes it possible to analyze the user's social media activity and store related data. Some or all of the above-mentioned processing in the storage unit may be performed using, or without, AI, for example. For example, the storage unit can input the user's social media data into a generation AI and have the generation AI select related data.

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

[0051] The analysis unit can further recognize ingredients in the image and provide nutritional information for the ingredients. For example, the analysis unit can identify ingredients in the image and display nutritional information such as the calories and vitamin content of each ingredient. The analysis unit can also provide allergen information for the ingredients. Furthermore, the analysis unit can evaluate the nutritional balance based on the user's dietary history and suggest recipes to supplement necessary nutrients. This allows the user to obtain information to maintain a healthy diet.

[0052] The processing unit can further evaluate the difficulty of the recipe and suggest recipes according to the user's skill level. For example, the processing unit can evaluate the difficulty based on the recipe steps and the types of ingredients. The processing unit can also estimate the skill level based on the user's past cooking history and feedback. Furthermore, the processing unit can suggest easy or challenging recipes according to the user's skill level. This allows the user to choose recipes that suit their skills, increasing the enjoyment of cooking.

[0053] The processing unit can further estimate the cooking time of the recipe and suggest recipes that fit the user's schedule. For example, the processing unit estimates the cooking time based on the recipe steps and the preparation time of the ingredients. The processing unit can also link with the user's calendar or schedule app to suggest recipes that fit the user's free time. Furthermore, the processing unit can suggest recipes that can be made in a short time if the user is in a hurry, or more elaborate recipes if the user has more time. This allows the user to enjoy cooking efficiently.

[0054] The storage unit may further include a recipe rating function. For example, the storage unit may allow a user to input a rating after trying a recipe. The storage unit may also display ratings and comments from other users. Furthermore, the storage unit may preferentially display highly rated recipes, allowing the user to easily find popular recipes. This allows the user to select a recipe while taking into consideration the opinions of other users, thereby improving cooking satisfaction.

[0055] The camera unit can also automatically recognize recipe ingredients and select the optimal shooting mode for each ingredient. For example, the camera unit automatically selects a mode that emphasizes the vividness of vegetables or a mode that brings out the texture of meat. The camera unit can also select a mode that emphasizes the fine details of spices and herbs. Furthermore, when multiple ingredients are mixed, the camera unit can combine the optimal modes for each ingredient to capture the photo. This allows users to take photos that bring out the best in each ingredient.

[0056] The shooting unit can also analyze the user's past shooting data and automatically adjust the optimal shooting settings. For example, the shooting unit can learn the shooting settings that the user has used favorably in the past and suggest new shooting settings based on that. The shooting unit can also automatically adjust exposure and white balance to match the user's shooting style. Furthermore, the shooting unit can select the most beautiful settings from the user's shooting history and apply them when taking a photo. This allows the user to take beautiful photos in a consistent style.

[0057] The photography unit can further provide a photography mode that emphasizes local specialties and seasonal ingredients based on the user's geographical location information. For example, if the user is in a specific region, the photography unit automatically selects a mode that beautifully captures the local specialties. The photography unit can also apply filters and effects to emphasize seasonal ingredients. Furthermore, if the user is traveling, the photography unit can provide a mode that beautifully captures the local specialty dishes of the travel destination. This allows the user to take attractive photos that are appropriate for the region and season.

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

[0059] Step 1: The photography unit takes a photograph or captures an image of the recipe. For example, a smartphone camera can be used to photograph a page in a recipe book, or a digital camera or scanner can be used to capture the image. A digital camera can be used to obtain a high-resolution image, or a scanner can be used to convert a printed recipe into digital data. Step 2: The analysis unit analyzes the image captured by the image capture unit and converts it into text data. For example, OCR (optical character recognition) technology or deep learning-based character recognition technology can be used to analyze the characters in the image and convert them into text data. Handwritten characters can also be recognized. Step 3: The processing unit analyzes the text data converted by the analysis unit using natural language processing technology and formats it into a template format. For example, the processing unit uses morphological analysis, grammatical analysis, and semantic analysis to divide the text data, analyze the structure of the sentences, understand the meaning, and format it into a template format. Step 4: The storage unit stores the recipe data formatted by the processing unit. For example, the formatted recipe data can be stored on the user's smartphone so that it can be easily accessed at any time. It is also possible to store the recipe data using cloud storage so that it can be accessed from multiple devices.

[0060] (Example 2) A recipe management system according to an embodiment of the present invention is designed for people struggling with managing cooking recipes. This recipe management system centralizes and easily manages favorite recipes from recipe books and apps. Users use their smartphones to take photos or capture images of recipes, analyze the images, and convert them into text data. The converted text data is then formatted into a template format using natural language processing technology. This allows users to easily manage recipes in their preferred format. Specifically, the system consists of the following steps: First, a user takes a photo or captures an image of a recipe using their smartphone. For example, they can take a photo of a page from a recipe book. The image is converted into text data using technology that analyzes the text within the image. Next, the converted text data is analyzed using natural language processing technology and formatted into a template format. For example, the recipe's ingredients and steps are organized for easy viewing and formatting in the user's preferred format. Finally, the formatted recipe data is saved on the user's smartphone for easy access at any time. Users can manage recipes in their preferred format, achieving easy-to-view and easy-to-use recipe management. This service simplifies recipe management, allowing users to centralize and efficiently manage their favorite recipes. This allows the recipe management system to be easily managed by the user in a format of their choice.

[0061] A recipe management system according to an embodiment includes a photographing unit, an analysis unit, a processing unit, and a storage unit. The photographing unit photographs or captures images of recipes. For example, the photographing unit can photograph pages of a recipe book using a smartphone camera. The photographing unit can also capture images using a digital camera or scanner. For example, the photographing unit can acquire high-resolution images using a digital camera. The photographing unit can also convert printed recipes into digital data using a scanner. The analysis unit analyzes the images photographed by the photographing unit and converts them into text data. For example, the analysis unit can analyze characters in the images using optical character recognition (OCR) technology and convert them into text data. The analysis unit can also perform highly accurate character analysis using deep learning-based character recognition technology. For example, the analysis unit can recognize handwritten characters using a deep learning model and convert them into text data. The processing unit analyzes the text data converted by the analysis unit using natural language processing technology and formats it into a template format. For example, the processing unit can analyze the text data using morphological analysis and perform grammatical analysis. The processing unit can also use semantic analysis to understand the meaning of the text data and format it into a template format. For example, the processing unit uses morphological analysis to divide the text data and uses grammatical analysis to analyze the structure of the sentence. Furthermore, the processing unit uses semantic analysis to understand the meaning of the text data and format it into a template format. The storage unit stores the recipe data formatted by the processing unit. For example, the storage unit stores the formatted recipe data on a user's smartphone so that it can be easily accessed at any time. The storage unit can also store the recipe data using cloud storage. For example, the storage unit stores the recipe data in cloud storage so that it can be accessed from multiple devices. This allows the recipe management system according to the embodiment to easily manage recipes in a format preferred by the user.

[0062] The analysis unit may include technology for analyzing characters in an image. The analysis unit may analyze characters in an image using, for example, OCR (optical character recognition) technology. For example, the analysis unit may recognize printed characters using OCR technology and convert them into text data. The analysis unit may also perform highly accurate character analysis using deep learning-based character recognition technology. For example, the analysis unit may recognize handwritten characters using a deep learning model and convert them into text data. This allows the characters in an image to be accurately analyzed and converted into text data. 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 may input image data to a generation AI and cause the generation AI to analyze the characters in the image.

[0063] The processing unit may include technology for performing natural language processing. The processing unit may analyze text data using, for example, morphological analysis. For example, the processing unit may divide the text data using morphological analysis and perform grammatical analysis. The processing unit may also understand the meaning of the text data using semantic analysis and format it into a template format. For example, the processing unit may divide the text data using morphological analysis and analyze the structure of the sentence using grammatical analysis. Furthermore, the processing unit may understand the meaning of the text data using semantic analysis and format it into a template format. In this way, the text data can be analyzed by natural language processing and formatted into a template format. Some or all of the above-described processing in the processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the processing unit may input text data to a generation AI and cause the generation AI to perform natural language processing.

[0064] The processing unit can organize the ingredients and steps of a recipe in an easy-to-read format and format the recipe in a format preferred by the user. For example, the processing unit organizes the ingredients and steps of a recipe in an easy-to-read format. For example, the processing unit displays the ingredients in a list format and the steps step by step. The processing unit can also format the recipe in a format preferred by the user. For example, the processing unit adjusts the font size and color based on the user's settings. Furthermore, the processing unit can adjust the format based on the user's past preference data. For example, the processing unit automatically applies a format that the user has previously preferred. This allows the recipe to be organized in a format preferred by the user in an easy-to-read format. Some or all of the above-described processing in the processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the processing unit can input recipe data to a generation AI and cause the generation AI to format the recipe.

[0065] The storage unit can store the formatted recipe data on the user's smartphone for easy access at any time. The storage unit, for example, stores the formatted recipe data on the user's smartphone. For example, the storage unit can store the recipe data in the smartphone's internal storage for easy access at any time. The storage unit can also store the recipe data using cloud storage. For example, the storage unit can store the recipe data in cloud storage for access from multiple devices. The storage unit can also make the recipe data easily searchable using a search function. For example, the storage unit can tag and classify the recipe data, allowing a specific recipe to be quickly found using the search function. This allows the user to easily access the formatted recipe data at any time. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the recipe data to a generation AI and have the generation AI select a storage method.

[0066] The image capturing unit can estimate the user's emotions and provide appropriate timing for capturing images based on the estimated user's emotions. The image capturing unit, for example, estimates the user's emotions. For example, the image capturing unit can analyze the user's facial expressions using facial expression recognition technology to estimate emotions. The image capturing unit can also analyze the tone and speed of the user's voice using voice analysis technology to estimate emotions. For example, the image capturing unit can analyze the tone and speed of the user's voice and calculate an emotion score. The image capturing unit can also estimate the user's emotions using biometric data. For example, the image capturing unit can collect heart rate and electrodermal activity using a sensor to estimate emotions. Next, the image capturing unit adjusts the timing for capturing images based on the estimated user's emotions. For example, if the user is excited, the image capturing unit can advance the timing for capturing images to start capturing immediately. Also, if the user is relaxed, the image capturing unit can delay the timing for capturing images to allow the user time to get ready. Furthermore, if the user is feeling stressed, the image capturing unit can adjust the timing for capturing images to wait until the user calms down. This makes it possible to provide optimal timing for capturing images according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the image capture unit may be performed using AI, for example, or may be performed without using AI. For example, the image capture unit may input the user's emotion data into the generation AI and have the generation AI adjust the timing of shooting.

[0067] The photographing unit can automatically select the optimal photographing mode depending on the type of recipe when photographing. The photographing unit automatically selects the optimal photographing mode depending on, for example, the type of recipe. For example, for a dessert recipe, the photographing unit automatically selects a photographing mode with bright colors. Furthermore, for a main dish, the photographing unit can automatically select a photographing mode that emphasizes the texture of the ingredients. Furthermore, for a soup recipe, the photographing unit can automatically select a photographing mode that brings out the transparency of the liquid. This makes it possible to automatically select the optimal photographing mode depending on the type of recipe. Some or all of the above-described processing in the photographing unit may be performed using, or without, AI, for example. For example, the photographing unit can input the type of recipe to the generation AI and cause the generation AI to select the optimal photographing mode.

[0068] The photographing unit can suggest the optimal photographing angle by referring to the user's past photographing history when photographing. The photographing unit can suggest the optimal photographing angle by referring to the user's past photographing history, for example. For example, the photographing unit automatically suggests a photographing angle that the user has previously preferred. The photographing unit can also suggest the angle that looks the most beautiful based on the user's past photographing history. Furthermore, the photographing unit can also suggest the optimal photographing angle based on the user's evaluation of images taken in the past. This makes it possible to suggest the optimal photographing angle by referring to the user's past photographing history. Some or all of the above-described processing in the photographing unit may be performed using, or without, AI, for example. For example, the photographing unit can input the user's photographing history data into a generation AI and cause the generation AI to suggest the optimal photographing angle.

[0069] The photographing unit can estimate the user's emotions and prioritize recipes to be photographed based on the estimated user's emotions. The photographing unit, for example, estimates the user's emotions. For example, the photographing unit can analyze the user's facial expressions using facial expression recognition technology to estimate emotions. The photographing unit can also analyze the tone and speed of the user's voice using voice analysis technology to estimate emotions. Furthermore, the photographing unit can estimate the user's emotions using biometric data. Next, the photographing unit prioritizes recipes to be photographed based on the estimated user's emotions. For example, if the user is excited, the photographing unit can prioritize photographing recipes that the user wants to make quickly. Also, if the user is relaxed, the photographing unit can prioritize photographing recipes that take time to make. Furthermore, if the user is stressed, the photographing unit can prioritize photographing recipes that are easy to make. In this way, the priority of recipes to be photographed can be determined according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the photographing unit may be performed using AI, or may be performed without using AI. For example, the photographing unit may input user emotion data into the generation AI and cause the generation AI to determine the priority of recipes to be photographed.

[0070] When photographing, the photographing unit can prioritize photographing highly relevant recipes based on the user's geographical location information. The photographing unit, for example, prioritizes photographing highly relevant recipes taking into account the user's geographical location information. For example, when the user is in a specific area, the photographing unit prioritizes photographing recipes using local specialties of that area. Furthermore, when the user is traveling, the photographing unit can prioritize photographing recipes of local specialties of the travel destination. Furthermore, when the user is at home, the photographing unit can prioritize photographing home-cooked recipes. This makes it possible to prioritize photographing highly relevant recipes taking into account the user's geographical location information. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI. For example, the photographing unit can input the user's geographical location information to the generation AI and cause the generation AI to select highly relevant recipes.

[0071] The photographing unit can analyze the user's social media activity and photograph related recipes when photographing. The photographing unit, for example, analyzes the user's social media activity. For example, the photographing unit prioritizes photographing recipes that the user has shared on social media. The photographing unit can also prioritize photographing recipes that the user has "liked" on social media. Furthermore, the photographing unit can prioritize photographing recipes from cooking bloggers that the user follows. This makes it possible to analyze the user's social media activity and photograph related recipes. Some or all of the above-described processing in the photographing unit may be performed, for example, using AI or without AI. For example, the photographing unit can input the user's social media data into a generation AI and have the generation AI select related recipes.

[0072] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions. For example, the analysis unit can analyze the user's facial expressions using facial expression recognition technology to estimate emotions. The analysis unit can also use voice analysis technology to analyze the tone and speed of the user's voice to estimate emotions. The analysis unit can also estimate the user's emotions using biometric data. Next, the analysis unit adjusts the accuracy of the analysis based on the estimated user's emotions. For example, the analysis unit can perform a detailed analysis when the user is relaxed. The analysis unit can also perform a simplified analysis when the user is in a hurry. Furthermore, the analysis unit can increase the accuracy of the analysis to reduce false recognition when the user is stressed. This allows the accuracy of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can 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 analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data to the generation AI and cause the generation AI to adjust the accuracy of the analysis.

[0073] The analysis unit can apply an optimal analysis algorithm depending on the resolution of the image during analysis. The analysis unit applies, for example, an optimal analysis algorithm depending on the resolution of the image. For example, the analysis unit applies a detailed analysis algorithm to a high-resolution image. The analysis unit can also apply a simple analysis algorithm to a low-resolution image. Furthermore, the analysis unit can also apply a balanced analysis algorithm to a medium-resolution image. This makes it possible to apply an optimal analysis algorithm depending on the resolution of the image. 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 image data to a generation AI and cause the generation AI to select an optimal analysis algorithm.

[0074] During analysis, the analysis unit can apply different analysis methods depending on the category of the recipe. For example, the analysis unit applies different analysis methods depending on the category of the recipe. For example, the analysis unit applies a method that emphasizes color analysis to a dessert recipe. The analysis unit can also apply a method that emphasizes ingredient shape analysis to a main dish. Furthermore, the analysis unit can apply a method that emphasizes liquid transparency analysis to a soup recipe. This makes it possible to apply different analysis methods depending on the category of the recipe. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI, for example. For example, the analysis unit can input recipe data into a generation AI and have the generation AI select the optimal analysis method.

[0075] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions. For example, the analysis unit can analyze the user's facial expressions using facial expression recognition technology to estimate emotions. The analysis unit can also use voice analysis technology to analyze the tone and speed of the user's voice to estimate emotions. The analysis unit can also estimate the user's emotions using biometric data. Next, the analysis unit adjusts the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This makes it possible to adjust the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can 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 analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data into the generation AI and cause the generation AI to adjust the display method of the analysis results.

[0076] During analysis, the analysis unit can determine the analysis priority based on when the images were taken. The analysis unit determines the analysis priority based on, for example, when the images were taken. For example, the analysis unit prioritizes analyzing recently taken images. The analysis unit can also prioritize analyzing images taken during a specific event. Furthermore, the analysis unit can prioritize analyzing images taken within a period specified by the user. This makes it possible to determine the analysis priority based on when the images were taken. 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 image data to a generation AI and have the generation AI determine the analysis priority.

[0077] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the image. The analysis unit improves the accuracy of the analysis by, for example, referring to literature related to the image. For example, the analysis unit improves the analysis accuracy by referring to literature related to ingredients in the image. The analysis unit can also improve the analysis accuracy by referring to literature related to cooking procedures in the image. Furthermore, the analysis unit can improve the analysis accuracy by referring to literature related to the names of dishes in the image. This makes it possible to improve the analysis accuracy by referring to literature related to the image. 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 related literature data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0078] The processing unit can estimate the user's emotion and select a template format based on the estimated user's emotion. The processing unit, for example, estimates the user's emotion. For example, the processing unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The processing unit can also analyze the tone and speed of the user's voice using voice analysis technology to estimate the emotion. The processing unit can also estimate the user's emotion using biometric data. Next, the processing unit selects a template format based on the estimated user's emotion. For example, the processing unit can select a detailed template format if the user is relaxed. The processing unit can also select a simple template format if the user is in a hurry. The processing unit can also select a template format with high visibility if the user is stressed. This makes it possible to select an optimal template format according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can 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 processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the processing unit may input user emotion data to the generation AI and cause the generation AI to select a template format.

[0079] During processing, the processing unit can adjust the level of detail of the template based on the importance of the recipe. The processing unit adjusts the level of detail of the template based on, for example, the importance of the recipe. For example, the processing unit applies a detailed template to an important recipe. The processing unit can also apply a standard template to a general recipe. Furthermore, the processing unit can also apply a simplified template to a simple recipe. In this way, the level of detail of the template can be adjusted based on the importance of the recipe. Some or all of the above-described processing in the processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the processing unit can input recipe data to a generation AI and cause the generation AI to adjust the level of detail of the template.

[0080] During processing, the processing unit can apply different template formats depending on the category of the recipe. For example, the processing unit applies different template formats depending on the category of the recipe. For example, the processing unit applies a colorful template to a dessert recipe. The processing unit can also apply a template that emphasizes the texture of ingredients to a main dish. Furthermore, the processing unit can apply a template that enhances the transparency of the liquid to a soup recipe. This makes it possible to apply the optimal template format depending on the category of the recipe. Some or all of the above-mentioned processing in the processing unit may be performed using, or without, AI, for example. For example, the processing unit can input recipe data to a generation AI and cause the generation AI to select the optimal template format.

[0081] The processing unit can estimate the user's emotion and adjust the length of the template based on the estimated user's emotion. The processing unit, for example, estimates the user's emotion. For example, the processing unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The processing unit can also use voice analysis technology to analyze the tone and speed of the user's voice to estimate the emotion. Furthermore, the processing unit can estimate the user's emotion using biometric data. Next, the processing unit adjusts the length of the template based on the estimated user's emotion. For example, if the user is in a hurry, the processing unit can select a short, to-the-point template. If the user is relaxed, the processing unit can select a longer template with detailed explanations. Furthermore, if the user is excited, the processing unit can select a template with visually stimulating effects. In this way, the length of the template can be adjusted according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can 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 processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the processing unit may input user emotion data to the generation AI and cause the generation AI to adjust the length of the template.

[0082] During processing, the processing unit can determine the priority of templates based on the time of recipe submission. The processing unit determines the priority of templates based on, for example, the time of recipe submission. For example, the processing unit may prioritize recently submitted recipes as templates. The processing unit may also prioritize recipes submitted during a specific event as templates. Furthermore, the processing unit may also prioritize recipes submitted within a period specified by the user as templates. This makes it possible to determine the priority of templates based on the time of recipe submission. Some or all of the above-described processing in the processing unit may be performed using, or without, AI. For example, the processing unit may input recipe data to a generation AI and cause the generation AI to determine the priority of templates.

[0083] During processing, the processing unit can adjust the order of templates based on the relevance of the recipes. The processing unit adjusts the order of templates based on, for example, the relevance of the recipes. For example, the processing unit prioritizes creating templates from highly relevant recipes. The processing unit can also postpone creating templates from less relevant recipes. Furthermore, the processing unit can adjust the order of templates based on the relevance specified by the user. This makes it possible to adjust the order of templates based on the relevance of the recipes. Some or all of the above-described processing in the processing unit may be performed using, or without, AI, for example. For example, the processing unit can input recipe data to a generation AI and cause the generation AI to adjust the order of the templates.

[0084] The storage unit can estimate the user's emotions and determine the priority of data to be saved based on the estimated user's emotions. The storage unit, for example, estimates the user's emotions. For example, the storage unit can analyze the user's facial expressions using facial expression recognition technology to estimate the emotions. The storage unit can also analyze the tone and speed of the user's voice using voice analysis technology to estimate the emotions. Furthermore, the storage unit can estimate the user's emotions using biometric data. Next, the storage unit determines the priority of data to be saved based on the estimated user's emotions. For example, if the user is excited, the storage unit can prioritize saving recipes that the user wants to review immediately. Also, if the user is relaxed, the storage unit can prioritize saving recipes that take a long time to make. Furthermore, if the user is stressed, the storage unit can prioritize saving recipes that are easy to make. In this way, the priority of data to be saved can be determined according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative 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 storage unit may be performed using AI, or may be performed without using AI. For example, the storage unit may input user emotion data into the generation AI and have the generation AI determine the priority of the data to be stored.

[0085] The storage unit can select the optimal storage method by referring to the user's past storage history when saving. The storage unit selects the optimal storage method by referring to the user's past storage history, for example. For example, the storage unit automatically selects a format that the user has frequently saved in the past. The storage unit can also suggest the most efficient storage method based on the user's past storage history. Furthermore, the storage unit can select the optimal storage method based on evaluations of recipes that the user has saved in the past. In this way, the optimal storage method can be selected by referring to the user's past storage history. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the user's storage history data to a generation AI and have the generation AI select the optimal storage method.

[0086] The storage unit can apply different storage formats depending on the category of the recipe when saving. For example, the storage unit applies different storage formats depending on the category of the recipe. For example, the storage unit applies a colorful storage format to a dessert recipe. The storage unit can also apply a storage format that emphasizes the texture of ingredients to a main dish. Furthermore, the storage unit can apply a storage format that highlights the transparency of the liquid to a soup recipe. In this way, different storage formats can be applied depending on the category of the recipe. Some or all of the above-mentioned processing in the storage unit may be performed using AI, for example, or may be performed without using AI. For example, the storage unit can input recipe data to a generation AI and cause the generation AI to select the optimal storage format.

[0087] The storage unit can estimate the user's emotion and adjust the display method of the stored data based on the estimated user's emotion. The storage unit, for example, estimates the user's emotion. For example, the storage unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The storage unit can also use voice analysis technology to analyze the tone and speed of the user's voice to estimate the emotion. The storage unit can also estimate the user's emotion using biometric data. Next, the storage unit adjusts the display method of the stored data based on the estimated user's emotion. For example, if the user is nervous, the storage unit can provide a simple, highly visible display method. If the user is relaxed, the storage unit can provide a display method that includes detailed information. If the user is in a hurry, the storage unit can provide a display method that focuses on the main points. This makes it possible to adjust the display method of the stored data according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can 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 storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit may input user emotion data to the generation AI and cause the generation AI to adjust the display method of the stored data.

[0088] When saving data, the storage unit can prioritize saving highly relevant data by taking into account the user's geographical location information. The storage unit, for example, prioritizes saving highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the storage unit can prioritize saving recipes using local specialties of that area. Furthermore, when the user is traveling, the storage unit can prioritize saving recipes for local specialties of the travel destination. Furthermore, when the user is at home, the storage unit can prioritize saving recipes for home-cooked meals. This makes it possible to prioritize saving highly relevant data by taking into account the user's geographical location information. Some or all of the above-described processing in the storage unit may be performed using AI, for example, or may be performed without using AI. For example, the storage unit can input the user's geographical location information to the generation AI and cause the generation AI to select highly relevant data.

[0089] The storage unit can analyze the user's social media activity and store related data when saving. The storage unit, for example, analyzes the user's social media activity. For example, the storage unit can prioritize saving recipes that the user has shared on social media. The storage unit can also prioritize saving recipes that the user has "liked" on social media. Furthermore, the storage unit can prioritize saving recipes from cooking bloggers that the user follows. This makes it possible to analyze the user's social media activity and store related data. Some or all of the above-mentioned processing in the storage unit may be performed using, or without, AI, for example. For example, the storage unit can input the user's social media data into a generation AI and have the generation AI select related data. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned photographing unit, analysis unit, processing unit, and storage unit, is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the photographing unit can photograph a page of a recipe book using the camera 42 of the smart device 14. The analysis unit uses OCR technology by the specific processing unit 290 of the data processing device 12 to analyze characters in the image and convert them into text data. The processing unit performs natural language processing by the specific processing unit 290 of the data processing device 12 and formats the data into a template format. The storage unit stores the formatted recipe data in the storage 50 of the smart device 14, allowing it to be easily accessed at any time. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned photographing unit, analyzing unit, processing unit, and storage unit, is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the photographing unit can photograph a page of a recipe book using the camera 42 of the smart glasses 214. The analyzing unit uses OCR technology by the specific processing unit 290 of the data processing device 12 to analyze characters in the image and convert them into text data. The processing unit performs natural language processing by the specific processing unit 290 of the data processing device 12 and formats the data into a template format. The storage unit stores the formatted recipe data in the storage 50 of the smart glasses 214, allowing it to be easily accessed at any time. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned photographing unit, analyzing unit, processing unit, and storage unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the photographing unit can photograph a page of a recipe book using the camera 42 of the headset type terminal 314. The analyzing unit uses OCR technology by the specific processing unit 290 of the data processing device 12 to analyze characters in the image and convert it into text data. The processing unit performs natural language processing by the specific processing unit 290 of the data processing device 12 and formats the data into a template format. The storage unit stores the formatted recipe data in the storage 50 of the headset type terminal 314, allowing it to be easily accessed at any time. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned photographing unit, analyzing unit, processing unit, and storage unit is realized, for example, in at least one of the robot 414 and the data processing device 12. For example, the photographing unit can photograph a page of a recipe book using the camera 42 of the robot 414. The analyzing unit uses OCR technology by the specific processing unit 290 of the data processing device 12 to analyze characters in the image and convert it into text data. The processing unit performs natural language processing by the specific processing unit 290 of the data processing device 12 and formats the data into a template format. The storage unit stores the formatted recipe data in the storage 50 of the robot 414, allowing it to be easily accessed at any time.

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

[0091] The recipe management system may further include a voice input unit. The voice input unit allows a user to input a recipe by voice. For example, when a user vocally describes the ingredients and steps of a recipe, the voice input unit converts the voice into text data. The voice input unit may also search for and edit recipes in response to a user's voice command. Furthermore, the voice input unit may infer emotions from the user's voice and provide appropriate feedback based on the inferred emotions. This allows a user to manage recipes without using their hands, improving convenience, especially while cooking.

[0092] The analysis unit can further recognize ingredients in the image and provide nutritional information for the ingredients. For example, the analysis unit can identify ingredients in the image and display nutritional information such as the calories and vitamin content of each ingredient. The analysis unit can also provide allergen information for the ingredients. Furthermore, the analysis unit can evaluate the nutritional balance based on the user's dietary history and suggest recipes to supplement necessary nutrients. This allows the user to obtain information to maintain a healthy diet.

[0093] The processing unit can further evaluate the difficulty of the recipe and suggest recipes according to the user's skill level. For example, the processing unit can evaluate the difficulty based on the recipe steps and the types of ingredients. The processing unit can also estimate the skill level based on the user's past cooking history and feedback. Furthermore, the processing unit can suggest easy or challenging recipes according to the user's skill level. This allows the user to choose recipes that suit their skills, increasing the enjoyment of cooking.

[0094] The processing unit can further estimate the cooking time of the recipe and suggest recipes that fit the user's schedule. For example, the processing unit estimates the cooking time based on the recipe steps and the preparation time of the ingredients. The processing unit can also link with the user's calendar or schedule app to suggest recipes that fit the user's free time. Furthermore, the processing unit can suggest recipes that can be made in a short time if the user is in a hurry, or more elaborate recipes if the user has more time. This allows the user to enjoy cooking efficiently.

[0095] The storage unit may further include a recipe rating function. For example, the storage unit may allow a user to input a rating after trying a recipe. The storage unit may also display ratings and comments from other users. Furthermore, the storage unit may preferentially display highly rated recipes, allowing the user to easily find popular recipes. This allows the user to select a recipe while taking into consideration the opinions of other users, thereby improving cooking satisfaction.

[0096] The photographing unit can estimate the user's emotions and automatically apply a filter when photographing based on the estimated user's emotions. For example, the photographing unit can apply a bright filter when the user is having fun and a soft filter when the user is relaxed. The photographing unit can also apply a calm filter when the user is feeling stressed. Furthermore, the photographing unit can adjust the strength of the filter according to the user's emotions. This allows the user to take a photo with an atmosphere that matches their emotions.

[0097] The camera unit can also automatically recognize recipe ingredients and select the optimal shooting mode for each ingredient. For example, the camera unit automatically selects a mode that emphasizes the vividness of vegetables or a mode that brings out the texture of meat. The camera unit can also select a mode that emphasizes the fine details of spices and herbs. Furthermore, when multiple ingredients are mixed, the camera unit can combine the optimal modes for each ingredient to capture the photo. This allows users to take photos that bring out the best in each ingredient.

[0098] The shooting unit can also analyze the user's past shooting data and automatically adjust the optimal shooting settings. For example, the shooting unit can learn the shooting settings that the user has used favorably in the past and suggest new shooting settings based on that. The shooting unit can also automatically adjust exposure and white balance to match the user's shooting style. Furthermore, the shooting unit can select the most beautiful settings from the user's shooting history and apply them when taking a photo. This allows the user to take beautiful photos in a consistent style.

[0099] The photographing unit can estimate the user's emotions and adjust the order of recipes to be photographed based on the estimated user's emotions. For example, the photographing unit can prioritize photographing new recipes when the user is excited, and re-photograph a favorite recipe when the user is relaxed. The photographing unit can also prioritize photographing simple recipes when the user is feeling stressed. Furthermore, the photographing unit can adjust the photographing order in real time according to the user's emotions. This allows the user to efficiently photograph recipes that match their emotions.

[0100] The photography unit can further provide a photography mode that emphasizes local specialties and seasonal ingredients based on the user's geographical location information. For example, if the user is in a specific region, the photography unit automatically selects a mode that beautifully captures the local specialties. The photography unit can also apply filters and effects to emphasize seasonal ingredients. Furthermore, if the user is traveling, the photography unit can provide a mode that beautifully captures the local specialty dishes of the travel destination. This allows the user to take attractive photos that are appropriate for the region and season.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The photography unit takes a photograph or captures an image of the recipe. For example, a smartphone camera can be used to photograph a page in a recipe book, or a digital camera or scanner can be used to capture the image. A digital camera can be used to obtain a high-resolution image, or a scanner can be used to convert a printed recipe into digital data. Step 2: The analysis unit analyzes the image captured by the image capture unit and converts it into text data. For example, OCR (optical character recognition) technology or deep learning-based character recognition technology can be used to analyze the characters in the image and convert them into text data. Handwritten characters can also be recognized. Step 3: The processing unit analyzes the text data converted by the analysis unit using natural language processing technology and formats it into a template format. For example, the processing unit uses morphological analysis, grammatical analysis, and semantic analysis to divide the text data, analyze the structure of the sentences, understand the meaning, and format it into a template format. Step 4: The storage unit stores the recipe data formatted by the processing unit. For example, the formatted recipe data can be stored on the user's smartphone so that it can be easily accessed at any time. It is also possible to store the recipe data using cloud storage so that it can be accessed from multiple devices.

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

[0104] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] 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.

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

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

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

[0109] The data processing device 12 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0117] 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.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

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

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

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

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

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

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

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

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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.

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

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

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

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

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

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

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

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0153] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

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

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 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 photography unit that takes a photograph or captures an image of the recipe; an analysis unit that analyzes the image captured by the imaging unit and converts it into text data; a processing unit that analyzes the text data converted by the analysis unit using a natural language processing technique and formats the data into a template format; a storage unit for storing the recipe data shaped by the processing unit; A system characterized by:

2. The analysis unit Includes technology to analyze text in images 2. The system of claim 1.

3. The processing unit Includes technology for natural language processing 2. The system of claim 1.

4. The processing unit Organize recipe ingredients and steps in a user-friendly format 2. The system of claim 1.

5. The storage unit The formatted recipe data is saved on the user's smartphone for easy access at any time.

2. The system of claim 1.

6. The imaging unit is Estimates the user's emotions and provides appropriate timing for taking photos based on the estimated user emotions.

2. The system of claim 1.

7. The imaging unit is When shooting, the optimal shooting mode is automatically selected depending on the type of recipe.

2. The system of claim 1.

8. The imaging unit is When taking a photo, the system suggests the optimal shooting angle by referring to the user's past shooting history.

2. The system of claim 1.

Images