System

Abstract

An object of a system according to an embodiment is to analyze a food display and automatically generate an appropriate menu or recipe.SOLUTION: A system includes an image analysis part, a menu proposal part, a recipe generation part, and an image generation part. The image analysis unit analyzes the image of the food display. The menu proposal unit proposes an appropriate menu on the basis of the information specified by the image analysis unit. The recipe generation part generates a recipe on the basis of the menu proposed by the menu proposal part. The image generation unit generates a finished image based on the recipe generated by the recipe generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, manually creating appropriate menus and recipes from food labels was time-consuming and difficult to do efficiently.

[0005] The system according to the embodiment aims to analyze food labels and automatically generate appropriate menus and recipes. [Means for solving the problem]

[0006] The system according to the embodiment includes an image analysis unit, a menu suggestion unit, a recipe generation unit, and an image generation unit. The image analysis unit analyzes an image of a food label. The menu suggestion unit suggests an appropriate menu based on information identified by the image analysis unit. The recipe generation unit generates a recipe based on the menu suggested by the menu suggestion unit. The image generation unit generates a finished image based on the recipe generated by the recipe generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze food labels and automatically generate appropriate menus and recipes. [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 system according to an embodiment of the present invention generates optimal menus, recipes, and finished image images simply by scanning food labels as images. In this system, a user inputs an image of a food label, and a generation AI analyzes the image to identify the type of food and its nutritional components. Based on the identified information, the generation AI proposes an optimal menu and generates a recipe based on the menu. The generation AI then generates an image of the finished product and provides it to the user. For example, a user inputs an image of a food label. For example, the user takes a photo of the food label using a smartphone camera. The image is input into the generation AI. The generation AI then analyzes the input image. The generation AI uses image recognition technology to read the letters and numbers on the food label and identify the type of food and its nutritional components. For example, it extracts information such as calories, protein, fat, and carbohydrates listed on the food label. The generation AI then proposes an optimal menu based on the identified information. For example, it generates a balanced menu tailored to the user's health condition and preferences. The generation AI then generates a recipe based on the menu. For example, it provides a recipe with detailed information on the necessary ingredients and cooking steps. The generation AI then generates an image of the finished product. For example, the system generates photos and illustrations of the finished dish and provides them to the user. This allows the user to easily obtain the optimal menu, recipe, and finished image simply by loading the food label as an image. This allows the user to easily obtain the optimal menu, recipe, and finished image simply by loading the food label as an image. For example, it allows the user to easily prepare healthy meals even in busy daily lives. Furthermore, even beginners in cooking can cook with confidence by referring to the detailed recipes and finished image.

[0029] A food label analysis system according to an embodiment includes an image analysis unit, a menu suggestion unit, a recipe generation unit, and an image generation unit. The image analysis unit analyzes an image of a food label. For example, the image analysis unit reads letters and numbers on the food label and identifies the type of food and nutritional components. The image analysis unit uses a generation AI to read the letters and numbers on the food label using image recognition technology. For example, it extracts information such as calories, protein, fat, and carbohydrates listed on the food label. The menu suggestion unit proposes an optimal menu based on the information identified by the image analysis unit. For example, the menu suggestion unit generates a balanced menu tailored to the user's health condition and preferences. The menu suggestion unit uses a generation AI to propose an optimal menu based on the identified information. For example, it generates a balanced menu tailored to the user's health condition and preferences. The recipe generation unit generates a recipe based on the menu proposed by the menu suggestion unit. For example, the recipe generation unit provides a recipe that details the necessary ingredients and cooking steps. The recipe generation unit uses a generation AI to generate a recipe based on the menu. For example, the system provides a recipe that details the necessary ingredients and cooking steps. The image generation unit generates an image of the finished dish based on the recipe generated by the recipe generation unit. For example, the image generation unit generates a photo or illustration of the finished dish and provides it to the user. The image generation unit uses a generation AI to generate a photo or illustration of the finished dish. For example, the system generates a photo or illustration of the finished dish and provides it to the user. In this way, the food label analysis system according to the embodiment allows the user to easily obtain optimal menus, recipes, and finished image simply by reading the food label as an image.

[0030] The image analysis unit can read letters and numbers on food labels to identify the type of food and nutritional components. Examples of letters and numbers on food labels include, but are not limited to, font, size, and placement. For example, the image analysis unit can read letters and numbers on food labels to identify the type of food and nutritional components. For example, the image analysis unit extracts information such as calories, protein, fat, and carbohydrates listed on food labels. The image analysis unit can also use a generative AI to read letters and numbers on food labels. For example, the generative AI can use image recognition technology to read letters and numbers on food labels and identify the type of food and nutritional components. This allows the letters and numbers on food labels to be accurately read and the type of food and nutritional components to be identified.

[0031] The menu suggestion unit can suggest a nutritionally balanced menu based on the user's health condition and preferences. The user's health condition and preferences include, but are not limited to, examples of health checkup results, questionnaires, and past meal history. The menu suggestion unit suggests a nutritionally balanced menu based on, for example, the user's health condition and preferences. For example, the menu suggestion unit suggests a balanced menu based on the user's health checkup results. The menu suggestion unit can also suggest a menu based on the user's questionnaire results, tailored to the user's preferences. The menu suggestion unit can also suggest a nutritionally balanced menu based on the user's past meal history. This makes it possible to suggest a balanced menu based on the user's health condition and preferences.

[0032] The recipe generation unit can generate a recipe that specifically describes the necessary ingredients and cooking steps. The necessary ingredients and cooking steps include, but are not limited to, for example, the amounts of ingredients, cooking utensils, and cooking time. The recipe generation unit generates a recipe that specifically describes the necessary ingredients and cooking steps. For example, the recipe generation unit provides a recipe that specifically describes the amounts of ingredients. The recipe generation unit can also provide a recipe that specifically describes how to use cooking utensils. The recipe generation unit can also provide a recipe that clearly indicates the cooking time. In this way, a recipe that specifically describes the necessary ingredients and cooking steps can be generated.

[0033] The image generation unit may generate a photo or illustration of a finished dish. The photo or illustration of a finished dish may include, but is not limited to, shooting conditions, illustration style, resolution, etc. The image generation unit may generate, for example, a photo or illustration of a finished dish. For example, the image generation unit may generate a photo of a finished dish taking into consideration shooting conditions. The image generation unit may also select an illustration style to generate an illustration of a finished dish. The image generation unit may also adjust the resolution to generate a photo or illustration of a finished dish. In this way, a photo or illustration of a finished dish may be generated.

[0034] The image analysis unit can adjust the analysis algorithm taking into account differences in font and layout of food labels. Examples of font and layout of food labels include, but are not limited to, font type, character size, layout pattern, etc. Analysis algorithms include, but are not limited to, OCR algorithms and layout analysis algorithms. For example, the image analysis unit automatically recognizes food labels with different fonts and adjusts the analysis algorithm. Furthermore, when the generation AI analyzes food labels with different layouts, the image analysis unit can learn layout patterns and select the optimal analysis method. Furthermore, the image analysis unit can allow the generation AI to simultaneously analyze multiple fonts and layouts and provide the most appropriate analysis results. This allows the analysis algorithm to be optimized taking into account differences in font and layout of food labels.

[0035] The image analysis unit can integrate multiple images to improve analysis accuracy. Methods for integrating multiple images include, but are not limited to, image overlay, feature point matching, and integration algorithms. For example, the image analysis unit can integrate images of food labels taken from multiple angles by the generation AI to improve analysis accuracy. The image analysis unit can also integrate images of food labels taken at different times by the generation AI to improve analysis accuracy. The image analysis unit can also integrate images of food labels taken with different devices by the generation AI to improve analysis accuracy. This allows multiple images to be integrated to improve analysis accuracy.

[0036] The image analysis unit can improve analysis accuracy by correcting the background color and lighting conditions of the food label. Methods for correcting the background color and lighting conditions include, but are not limited to, color correction algorithms and lighting correction algorithms. For example, the image analysis unit uses a generation AI to automatically detect the background color of the food label and adjust the analysis algorithm. The image analysis unit can also use the generation AI to correct lighting conditions to more accurately read the letters and numbers on the food label. The image analysis unit can also maintain analysis accuracy by allowing the generation AI to respond to changes in the background color and lighting conditions. This allows the background color and lighting conditions of the food label to be corrected to improve analysis accuracy.

[0037] The image analysis unit can customize the analysis algorithm by referring to the user's past analysis history. Past analysis history includes, but is not limited to, log data, a history database, and a method for customizing the analysis algorithm. For example, the image analysis unit allows the generation AI to analyze the user's past analysis history and select the optimal analysis algorithm. The image analysis unit can also improve analysis accuracy by having the generation AI learn specific food labeling patterns from the user's past analysis history. The image analysis unit can also automatically adjust the analysis algorithm based on the user's past analysis history. This allows the analysis algorithm to be customized by referring to the user's past analysis history.

[0038] The image analysis unit can analyze region-specific food labeling by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, location information services, and region-specific food labeling analysis methods. For example, the image analysis unit allows the generation AI to prioritize analysis of region-specific food labeling based on the user's geographical location information. The image analysis unit can also allow the generation AI to learn the fonts and layouts of region-specific food labeling to improve analysis accuracy. The image analysis unit can also allow the generation AI to provide region-specific nutritional information by taking into account the user's geographical location information. This allows region-specific food labeling to be analyzed by taking into account the user's geographical location information.

[0039] The image analysis unit can analyze the user's social media activity and prioritize analysis of related food labels. Examples of social media activity include, but are not limited to, analysis of post content, analysis of followers, and prioritized analysis of related food labels. For example, the image analysis unit can analyze the user's social media post content and prioritize analysis of related food labels. The image analysis unit can also analyze related food labels based on the user's social media check-in information. The image analysis unit can also analyze related food labels based on the activity of the user's friends on social media. This allows the user's social media activity to be analyzed and prioritize analysis of related food labels.

[0040] The menu suggestion unit can suggest an optimal menu by referring to the user's past meal history. Past meal history includes, but is not limited to, for example, a meal log, a history database, and a method for suggesting an optimal menu. For example, the menu suggestion unit uses a generation AI to analyze the user's past meal history and suggest a balanced menu. The menu suggestion unit can also suggest a menu using the user's favorite ingredients based on the user's past meal history. The menu suggestion unit can also suggest a menu that takes nutritional balance into consideration based on the user's past meal history. This makes it possible to suggest an optimal menu by referring to the user's past meal history.

[0041] The menu suggestion unit can change the proposed content depending on the season and weather. Methods of acquiring the season and weather include, but are not limited to, weather data, seasonal ingredients, and methods of changing the proposed content. For example, the menu suggestion unit uses the generation AI to propose a menu using ingredients according to the season. The menu suggestion unit can also use the generation AI to propose hot and cold dishes according to the weather. The menu suggestion unit can also use the generation AI to propose a nutritionally balanced menu that matches the season and weather. This allows the proposed content to be adjusted according to the season and weather.

[0042] The menu suggestion unit can suggest safe menus by taking into account the user's allergy information. Allergy information includes, for example, the user's allergy history, methods for identifying allergens, and methods for suggesting safe menus, but is not limited to these examples. For example, the menu suggestion unit uses the generation AI to suggest menus that do not contain allergens based on the user's allergy information. The menu suggestion unit can also suggest menus that use alternative ingredients by taking into account the user's allergy information. The menu suggestion unit can also suggest safe and nutritionally balanced menus by taking into account the user's allergy information. This makes it possible to suggest safe menus by taking into account the user's allergy information.

[0043] The menu suggestion unit can suggest a menu using ingredients specific to the region, taking into account the user's geographical location information. Methods for acquiring ingredients specific to the region include, but are not limited to, regional market data, local ingredient lists, and methods for customizing the suggestions. For example, the menu suggestion unit uses the generation AI to suggest a menu using ingredients specific to the region based on the user's geographical location information. The menu suggestion unit can also suggest a menu using local specialties. The menu suggestion unit can also suggest traditional dishes of the region, taking into account the user's geographical location information. This makes it possible to suggest a menu using ingredients specific to the region, taking into account the user's geographical location information.

[0044] The menu suggestion unit can analyze the user's social media activity and suggest related menus. Social media activity includes, for example, analysis of post content, analysis of followers, and methods of suggesting related menus, but is not limited to these examples. For example, the menu suggestion unit uses a generation AI to analyze the user's social media posts and suggest related menus. The menu suggestion unit can also use the generation AI to suggest related menus based on the user's social media check-in information. The menu suggestion unit can also use the generation AI to suggest related menus based on the activity of the user's friends on social media. In this way, the user's social media activity can be analyzed and related menus can be suggested.

[0045] The menu suggestion unit can customize the suggested content by reflecting the user's past feedback. Past feedback includes, for example, user evaluation data, a method for analyzing the feedback, a method for customizing the suggested content, and the like, but is not limited to these examples. In the menu suggestion unit, for example, the generation AI can suggest a preferred menu based on the user's past feedback. In addition, the menu suggestion unit can also suggest a menu that reflects improvements based on the user's past feedback. In addition, the menu suggestion unit can suggest an optimal menu by taking the user's past feedback into consideration. In this way, the suggested content can be customized by reflecting the user's past feedback.

[0046] The recipe generation unit can adjust the level of detail of the recipe according to the user's cooking skill level. Cooking skill level includes, but is not limited to, for example, the user's self-assessment, past cooking history, and a method for adjusting the level of detail of the recipe. For example, the recipe generation unit may provide a recipe with detailed steps and images for beginners, for intermediate cooks, for simple steps and key points. The recipe generation unit may also provide a recipe that encourages creativity and ingenuity for advanced cooks, for advanced cooks. This allows the level of detail of the recipe to be adjusted according to the user's cooking skill level.

[0047] The recipe generation unit can propose appropriate cooking procedures by taking into account the user's kitchen equipment. Examples of kitchen equipment include, but are not limited to, the user's equipment list, the characteristics of the equipment, and a method for proposing optimal cooking procedures. For example, the recipe generation unit provides a recipe that takes into account available cooking utensils based on the user's kitchen equipment using the generation AI. The recipe generation unit can also provide a recipe that optimizes cooking time and temperature by taking into account the performance of the user's kitchen equipment. The recipe generation unit can also propose efficient cooking procedures by taking into account the layout of the user's kitchen equipment. This makes it possible to propose optimal cooking procedures by taking into account the user's kitchen equipment.

[0048] The recipe generation unit can improve the accuracy of a recipe by referring to the user's past cooking history. Past cooking history includes, but is not limited to, for example, a cooking log, a history database, and a method for improving recipe accuracy. For example, the recipe generation unit uses a generation AI to analyze the user's past cooking history and provide a recipe that reflects the user's preferred seasonings and cooking methods. The recipe generation unit can also suggest a new recipe that incorporates elements of successful recipes from the user's past cooking history. The recipe generation unit can also provide a recipe that includes advice on how to avoid failure based on the user's past cooking history. This allows the accuracy of a recipe to be improved by referring to the user's past cooking history.

[0049] The recipe generation unit can incorporate regional cooking methods by taking into account the user's geographical location information. Regional cooking methods include, but are not limited to, regional culinary culture, traditional cooking methods, and methods of reflecting these in recipes. For example, the recipe generation unit can provide recipes incorporating regional cooking methods based on the user's geographical location information using the generation AI. The recipe generation unit can also suggest recipes incorporating regional traditional cooking methods. The recipe generation unit can also provide recipes using regional ingredients by taking into account the user's geographical location information. This allows regional cooking methods to be incorporated by taking into account the user's geographical location information.

[0050] The recipe generation unit can analyze the user's social media activity and suggest related recipes. Examples of social media activity include, but are not limited to, analysis of post content, analysis of followers, and methods of suggesting related recipes. For example, the recipe generation unit uses a generation AI to analyze the user's social media posts and suggest related recipes. The recipe generation unit can also use the generation AI to suggest related recipes based on the user's social media check-in information. The recipe generation unit can also use the generation AI to suggest related recipes based on the activity of the user's friends on social media. In this way, the user's social media activity can be analyzed and related recipes can be suggested.

[0051] The recipe generation unit can customize the recipe content by reflecting the user's past feedback. Past feedback includes, for example, user evaluation data, a method for analyzing the feedback, a method for customizing the recipe content, and the like, but is not limited to these examples. For example, the recipe generation unit provides a recipe that reflects the user's preferred seasonings and cooking methods based on the user's past feedback. The recipe generation unit can also suggest a recipe that reflects improvements based on the user's past feedback. The recipe generation unit can also provide an optimal recipe by using the generation AI to take the user's past feedback into consideration. This allows the recipe content to be customized by reflecting the user's past feedback.

[0052] The image generation unit can generate an optimal image by referring to the user's past image history. Past image history includes, but is not limited to, an image log, a history database, and a method for generating an optimal image. For example, the image generation unit uses a generation AI to analyze the user's past image history and provide an image that reflects the user's preferred style. The image generation unit can also propose a new image that incorporates elements of successful images from the user's past image history. The image generation unit can also provide an image that includes advice on how to avoid failure based on the user's past image history. This allows the optimal image to be generated by referring to the user's past image history.

[0053] The image generation unit can customize the style of the image according to the user's preferences. Examples of image styles include, but are not limited to, art style, color usage, and design elements. For example, the image generation unit provides an image that reflects the user's preferred colors and design using a generation AI. The image generation unit can also propose an image that incorporates the user's preferred art style using a generation AI. The image generation unit can also provide an image that takes into account the user's preferred fonts and layout. This allows the image style to be customized according to the user's preferences.

[0054] The image generation unit can generate an image at an appropriate resolution by taking into account the user's device information. Device information includes, but is not limited to, for example, the device resolution, screen size, and optimal image generation method. For example, the image generation unit provides an image at an optimal resolution based on the screen resolution of the user's device by the generation AI. The image generation unit can also provide an image with optimized processing speed by taking into account the performance of the user's device. The image generation unit can also provide an image with high visibility by taking into account the screen size of the user's device. This allows the image to be generated at an optimal resolution by taking into account the user's device information.

[0055] The image generation unit can generate an image that reflects a region's unique food culture by taking into account the user's geographical location information. Examples of a region's unique food culture include, but are not limited to, the region's culinary culture, traditional ingredients, and methods of reflecting the region's unique food culture. For example, the image generation unit provides an image that reflects the region's unique food culture based on the user's geographical location information using a generation AI. The image generation unit can also make suggestions that incorporate images of traditional dishes of the region. The image generation unit can also provide images of dishes that use local ingredients by taking into account the user's geographical location information. In this way, an image that reflects a region's unique food culture can be generated by taking into account the user's geographical location information.

[0056] The image generation unit can analyze the user's social media activity and suggest related images. Social media activity includes, but is not limited to, for example, analysis of post content, analysis of followers, and methods for suggesting related images. For example, the image generation unit can have a generation AI analyze the user's social media post content and suggest related images. The image generation unit can also have the generation AI suggest related images based on the user's social media check-in information. The image generation unit can also have the generation AI suggest related images based on the activity of the user's friends on social media. In this way, the user's social media activity can be analyzed and related images can be suggested.

[0057] The image generation unit can customize the image content by reflecting the user's past feedback. Past feedback includes, for example, user evaluation data, a feedback analysis method, and a method for customizing the image content, but is not limited to these examples. For example, the image generation unit provides an image that reflects the user's preferred style based on the user's past feedback. The image generation unit can also suggest an image that reflects improvements based on the user's past feedback. The image generation unit can also provide an optimal image by using the generation AI to take the user's past feedback into consideration. This allows the image content to be customized by reflecting the user's past feedback.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The image analysis unit can improve analysis accuracy by referencing the user's dietary history and learning patterns of past food purchases. For example, it can analyze the types and frequency of foods the user has purchased in the past to improve recognition accuracy for specific food labeling. The image analysis unit can also learn patterns of specific brands or products from the user's dietary history and quickly analyze similar food labels. Furthermore, the image analysis unit can adjust analysis accuracy based on the user's dietary history, taking into account seasonal variations in food labeling. This makes it possible to improve the accuracy of image analysis by utilizing the user's dietary history.

[0060] The recipe generation unit can adjust the difficulty of the recipe according to the user's cooking skill level. For example, for beginners, it can provide recipes with detailed steps and images. For intermediate cooks, it can provide recipes with simple steps and key points. Furthermore, for advanced cooks, it can provide recipes that encourage creativity and allow the user to test their skills. In this way, it is possible to adjust the difficulty of the recipe according to the user's cooking skill level.

[0061] The image analysis unit can prioritize analysis of regional food labels by taking into account the user's geographic location information. For example, the generative AI can prioritize analysis of regional food labels based on the user's geographic location information. The image analysis unit can also learn regional fonts and layouts to improve analysis accuracy. Furthermore, the image analysis unit can provide regional nutritional information. This allows analysis of regional food labels by taking into account the user's geographic location information.

[0062] The recipe generation unit can propose appropriate cooking procedures taking into account the user's kitchen equipment. For example, the generation AI can provide recipes that take into account the available cooking utensils based on the user's kitchen equipment. The generation AI can also provide recipes that optimize cooking times and temperatures by taking into account the performance of the user's kitchen equipment. Furthermore, the generation AI can also propose efficient cooking procedures by taking into account the layout of the user's kitchen equipment. This makes it possible to propose optimal cooking procedures taking into account the user's kitchen equipment.

[0063] The image analysis unit can improve analysis accuracy by integrating multiple images. For example, the generation AI can integrate images of food labels taken from multiple angles to improve analysis accuracy. The generation AI can also integrate images of food labels taken at different times to improve analysis accuracy. Furthermore, the generation AI can also integrate images of food labels taken with different devices to improve analysis accuracy. This makes it possible to integrate multiple images to improve analysis accuracy.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The image analysis unit analyzes the image of the food label. For example, the image analysis unit reads the letters and numbers on the food label and identifies the type of food and its nutritional components. The image analysis unit uses generative AI and image recognition technology to read the letters and numbers on the food label. For example, it extracts information such as calories, protein, fat, and carbohydrates listed on the food label. Step 2: The menu suggestion unit suggests an optimal menu based on the information identified by the image analysis unit. For example, the menu suggestion unit generates a balanced menu based on the user's health condition and preferences. The menu suggestion unit uses a generation AI to suggest an optimal menu based on the identified information. Step 3: The recipe generation unit generates a recipe based on the menu proposed by the menu suggestion unit. For example, the recipe generation unit provides a recipe that details the necessary ingredients and cooking steps. The recipe generation unit uses a generation AI to generate a recipe based on the menu. Step 4: The image generation unit generates a finished image based on the recipe generated by the recipe generation unit. For example, the image generation unit generates a photo or illustration of the finished dish and provides it to the user. The image generation unit uses generative AI to generate a photo or illustration of the finished dish.

[0066] (Example 2) A system according to an embodiment of the present invention generates optimal menus, recipes, and finished image images simply by scanning food labels as images. In this system, a user inputs an image of a food label, and a generation AI analyzes the image to identify the type of food and its nutritional components. Based on the identified information, the generation AI proposes an optimal menu and generates a recipe based on the menu. The generation AI then generates an image of the finished product and provides it to the user. For example, a user inputs an image of a food label. For example, the user takes a photo of the food label using a smartphone camera. The image is input into the generation AI. The generation AI then analyzes the input image. The generation AI uses image recognition technology to read the letters and numbers on the food label and identify the type of food and its nutritional components. For example, it extracts information such as calories, protein, fat, and carbohydrates listed on the food label. The generation AI then proposes an optimal menu based on the identified information. For example, it generates a balanced menu tailored to the user's health condition and preferences. The generation AI then generates a recipe based on the menu. For example, it provides a recipe with detailed information on the necessary ingredients and cooking steps. The generation AI then generates an image of the finished product. For example, the system generates photos and illustrations of the finished dish and provides them to the user. This allows the user to easily obtain the optimal menu, recipe, and finished image simply by loading the food label as an image. This allows the user to easily obtain the optimal menu, recipe, and finished image simply by loading the food label as an image. For example, it allows the user to easily prepare healthy meals even in busy daily lives. Furthermore, even beginners in cooking can cook with confidence by referring to the detailed recipes and finished image.

[0067] A food label analysis system according to an embodiment includes an image analysis unit, a menu suggestion unit, a recipe generation unit, and an image generation unit. The image analysis unit analyzes an image of a food label. For example, the image analysis unit reads letters and numbers on the food label and identifies the type of food and nutritional components. The image analysis unit uses a generation AI to read the letters and numbers on the food label using image recognition technology. For example, it extracts information such as calories, protein, fat, and carbohydrates listed on the food label. The menu suggestion unit proposes an optimal menu based on the information identified by the image analysis unit. For example, the menu suggestion unit generates a balanced menu tailored to the user's health condition and preferences. The menu suggestion unit uses a generation AI to propose an optimal menu based on the identified information. For example, it generates a balanced menu tailored to the user's health condition and preferences. The recipe generation unit generates a recipe based on the menu proposed by the menu suggestion unit. For example, the recipe generation unit provides a recipe that details the necessary ingredients and cooking steps. The recipe generation unit uses a generation AI to generate a recipe based on the menu. For example, the system provides a recipe that details the necessary ingredients and cooking steps. The image generation unit generates an image of the finished dish based on the recipe generated by the recipe generation unit. For example, the image generation unit generates a photo or illustration of the finished dish and provides it to the user. The image generation unit uses a generation AI to generate a photo or illustration of the finished dish. For example, the system generates a photo or illustration of the finished dish and provides it to the user. In this way, the food label analysis system according to the embodiment allows the user to easily obtain optimal menus, recipes, and finished image simply by reading the food label as an image.

[0068] The image analysis unit can read letters and numbers on food labels to identify the type of food and nutritional components. Examples of letters and numbers on food labels include, but are not limited to, font, size, and placement. For example, the image analysis unit can read letters and numbers on food labels to identify the type of food and nutritional components. For example, the image analysis unit extracts information such as calories, protein, fat, and carbohydrates listed on food labels. The image analysis unit can also use a generative AI to read letters and numbers on food labels. For example, the generative AI can use image recognition technology to read letters and numbers on food labels and identify the type of food and nutritional components. This allows the letters and numbers on food labels to be accurately read and the type of food and nutritional components to be identified.

[0069] The menu suggestion unit can suggest a nutritionally balanced menu based on the user's health condition and preferences. The user's health condition and preferences include, but are not limited to, examples of health checkup results, questionnaires, and past meal history. The menu suggestion unit suggests a nutritionally balanced menu based on, for example, the user's health condition and preferences. For example, the menu suggestion unit suggests a balanced menu based on the user's health checkup results. The menu suggestion unit can also suggest a menu based on the user's questionnaire results, tailored to the user's preferences. The menu suggestion unit can also suggest a nutritionally balanced menu based on the user's past meal history. This makes it possible to suggest a balanced menu based on the user's health condition and preferences.

[0070] The recipe generation unit can generate a recipe that specifically describes the necessary ingredients and cooking steps. The necessary ingredients and cooking steps include, but are not limited to, for example, the amounts of ingredients, cooking utensils, and cooking time. The recipe generation unit generates a recipe that specifically describes the necessary ingredients and cooking steps. For example, the recipe generation unit provides a recipe that specifically describes the amounts of ingredients. The recipe generation unit can also provide a recipe that specifically describes how to use cooking utensils. The recipe generation unit can also provide a recipe that clearly indicates the cooking time. In this way, a recipe that specifically describes the necessary ingredients and cooking steps can be generated.

[0071] The image generation unit may generate a photo or illustration of a finished dish. The photo or illustration of a finished dish may include, but is not limited to, shooting conditions, illustration style, resolution, etc. The image generation unit may generate, for example, a photo or illustration of a finished dish. For example, the image generation unit may generate a photo of a finished dish taking into consideration shooting conditions. The image generation unit may also select an illustration style to generate an illustration of a finished dish. The image generation unit may also adjust the resolution to generate a photo or illustration of a finished dish. In this way, a photo or illustration of a finished dish may be generated.

[0072] The image analysis unit can estimate the user's emotions and adjust the accuracy of the image analysis based on the estimated user emotions. Examples of user emotions include, but are not limited to, facial expression recognition, voice analysis, and survey results. Examples of image analysis accuracy include, but are not limited to, recognition rate, error rate, and adjustment algorithm. For example, if the user is feeling stressed, the image analysis unit adjusts the generation AI to increase analysis accuracy and reduce false recognition. Furthermore, if the user is relaxed, the image analysis unit can maintain normal analysis accuracy and prioritize processing speed. Furthermore, if the user is in a hurry, the image analysis unit can provide quick results even if the generation AI slightly reduces analysis accuracy. This allows the accuracy of image analysis to be adjusted according to the user's emotions.

[0073] The image analysis unit can adjust the analysis algorithm taking into account differences in font and layout of food labels. Examples of font and layout of food labels include, but are not limited to, font type, character size, layout pattern, etc. Analysis algorithms include, but are not limited to, OCR algorithms and layout analysis algorithms. For example, the image analysis unit automatically recognizes food labels with different fonts and adjusts the analysis algorithm. Furthermore, when the generation AI analyzes food labels with different layouts, the image analysis unit can learn layout patterns and select the optimal analysis method. Furthermore, the image analysis unit can allow the generation AI to simultaneously analyze multiple fonts and layouts and provide the most appropriate analysis results. This allows the analysis algorithm to be optimized taking into account differences in font and layout of food labels.

[0074] The image analysis unit can integrate multiple images to improve analysis accuracy. Methods for integrating multiple images include, but are not limited to, image overlay, feature point matching, and integration algorithms. For example, the image analysis unit can integrate images of food labels taken from multiple angles by the generation AI to improve analysis accuracy. The image analysis unit can also integrate images of food labels taken at different times by the generation AI to improve analysis accuracy. The image analysis unit can also integrate images of food labels taken with different devices by the generation AI to improve analysis accuracy. This allows multiple images to be integrated to improve analysis accuracy.

[0075] The image analysis unit can improve analysis accuracy by correcting the background color and lighting conditions of the food label. Methods for correcting the background color and lighting conditions include, but are not limited to, color correction algorithms and lighting correction algorithms. For example, the image analysis unit uses a generation AI to automatically detect the background color of the food label and adjust the analysis algorithm. The image analysis unit can also use the generation AI to correct lighting conditions to more accurately read the letters and numbers on the food label. The image analysis unit can also maintain analysis accuracy by allowing the generation AI to respond to changes in the background color and lighting conditions. This allows the background color and lighting conditions of the food label to be corrected to improve analysis accuracy.

[0076] The image analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. Examples of display methods for the analysis results include, but are not limited to, display format, color usage, and layout. For example, when the user is feeling stressed, the image analysis unit can have the generation AI display the analysis results simply to reduce visual burden. Furthermore, when the user is relaxed, the image analysis unit can have the generation AI display the analysis results in detail to provide a wealth of information. Furthermore, when the user is in a hurry, the image analysis unit can have the generation AI display only the key points of the analysis results to provide quick information. This allows the display method of the analysis results to be adjusted according to the user's emotions.

[0077] The image analysis unit can customize the analysis algorithm by referring to the user's past analysis history. Past analysis history includes, but is not limited to, log data, a history database, and a method for customizing the analysis algorithm. For example, the image analysis unit allows the generation AI to analyze the user's past analysis history and select the optimal analysis algorithm. The image analysis unit can also improve analysis accuracy by having the generation AI learn specific food labeling patterns from the user's past analysis history. The image analysis unit can also automatically adjust the analysis algorithm based on the user's past analysis history. This allows the analysis algorithm to be customized by referring to the user's past analysis history.

[0078] The image analysis unit can analyze region-specific food labeling by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, location information services, and region-specific food labeling analysis methods. For example, the image analysis unit allows the generation AI to prioritize analysis of region-specific food labeling based on the user's geographical location information. The image analysis unit can also allow the generation AI to learn the fonts and layouts of region-specific food labeling to improve analysis accuracy. The image analysis unit can also allow the generation AI to provide region-specific nutritional information by taking into account the user's geographical location information. This allows region-specific food labeling to be analyzed by taking into account the user's geographical location information.

[0079] The image analysis unit can analyze the user's social media activity and prioritize analysis of related food labels. Examples of social media activity include, but are not limited to, analysis of post content, analysis of followers, and prioritized analysis of related food labels. For example, the image analysis unit can analyze the user's social media post content and prioritize analysis of related food labels. The image analysis unit can also analyze related food labels based on the user's social media check-in information. The image analysis unit can also analyze related food labels based on the activity of the user's friends on social media. This allows the user's social media activity to be analyzed and prioritize analysis of related food labels.

[0080] The menu suggestion unit can estimate the user's emotions and adjust the menu suggestion method based on the estimated user's emotions. The menu suggestion method includes, for example, the timing of suggestions and a method for customizing the suggestions, but is not limited to these examples. For example, if the user is feeling stressed, the menu suggestion unit can suggest a menu that the generation AI can easily make. Furthermore, if the user is relaxed, the menu suggestion unit can suggest a menu that the generation AI can enjoy over time. Furthermore, if the user is in a hurry, the menu suggestion unit can suggest a menu that the generation AI can make in a short amount of time. This makes it possible to adjust the menu suggestion method according to the user's emotions.

[0081] The menu suggestion unit can suggest an optimal menu by referring to the user's past meal history. Past meal history includes, but is not limited to, for example, a meal log, a history database, and a method for suggesting an optimal menu. For example, the menu suggestion unit uses a generation AI to analyze the user's past meal history and suggest a balanced menu. The menu suggestion unit can also suggest a menu using the user's favorite ingredients based on the user's past meal history. The menu suggestion unit can also suggest a menu that takes nutritional balance into consideration based on the user's past meal history. This makes it possible to suggest an optimal menu by referring to the user's past meal history.

[0082] The menu suggestion unit can change the proposed content depending on the season and weather. Methods of acquiring the season and weather include, but are not limited to, weather data, seasonal ingredients, and methods of changing the proposed content. For example, the menu suggestion unit uses the generation AI to propose a menu using ingredients according to the season. The menu suggestion unit can also use the generation AI to propose hot and cold dishes according to the weather. The menu suggestion unit can also use the generation AI to propose a nutritionally balanced menu that matches the season and weather. This allows the proposed content to be adjusted according to the season and weather.

[0083] The menu suggestion unit can suggest safe menus by taking into account the user's allergy information. Allergy information includes, for example, the user's allergy history, methods for identifying allergens, and methods for suggesting safe menus, but is not limited to these examples. For example, the menu suggestion unit uses the generation AI to suggest menus that do not contain allergens based on the user's allergy information. The menu suggestion unit can also suggest menus that use alternative ingredients by taking into account the user's allergy information. The menu suggestion unit can also suggest safe and nutritionally balanced menus by taking into account the user's allergy information. This makes it possible to suggest safe menus by taking into account the user's allergy information.

[0084] The menu suggestion unit can estimate the user's emotions and determine the priority of menus based on the estimated user emotions. Methods for determining menu priorities include, but are not limited to, the user's emotion score, health condition, and preferences, for example. For example, if the user is feeling stressed, the menu suggestion unit allows the generation AI to preferentially suggest menus that have a relaxing effect. Furthermore, if the user is relaxed, the menu suggestion unit can also preferentially suggest menus that the generation AI can enjoy. Furthermore, if the user is in a hurry, the menu suggestion unit can also preferentially suggest menus that the generation AI can make in a short amount of time. This allows the priority of menus to be determined according to the user's emotions.

[0085] The menu suggestion unit can suggest a menu using ingredients specific to the region, taking into account the user's geographical location information. Methods for acquiring ingredients specific to the region include, but are not limited to, regional market data, local ingredient lists, and methods for customizing the suggestions. For example, the menu suggestion unit uses the generation AI to suggest a menu using ingredients specific to the region based on the user's geographical location information. The menu suggestion unit can also suggest a menu using local specialties. The menu suggestion unit can also suggest traditional dishes of the region, taking into account the user's geographical location information. This makes it possible to suggest a menu using ingredients specific to the region, taking into account the user's geographical location information.

[0086] The menu suggestion unit can analyze the user's social media activity and suggest related menus. Social media activity includes, for example, analysis of post content, analysis of followers, and methods of suggesting related menus, but is not limited to these examples. For example, the menu suggestion unit uses a generation AI to analyze the user's social media posts and suggest related menus. The menu suggestion unit can also use the generation AI to suggest related menus based on the user's social media check-in information. The menu suggestion unit can also use the generation AI to suggest related menus based on the activity of the user's friends on social media. In this way, the user's social media activity can be analyzed and related menus can be suggested.

[0087] The menu suggestion unit can customize the suggested content by reflecting the user's past feedback. Past feedback includes, for example, user evaluation data, a method for analyzing the feedback, a method for customizing the suggested content, and the like, but is not limited to these examples. In the menu suggestion unit, for example, the generation AI can suggest a preferred menu based on the user's past feedback. In addition, the menu suggestion unit can also suggest a menu that reflects improvements based on the user's past feedback. In addition, the menu suggestion unit can suggest an optimal menu by taking the user's past feedback into consideration. In this way, the suggested content can be customized by reflecting the user's past feedback.

[0088] The recipe generation unit can estimate the user's emotions and adjust the way the recipe is presented based on the estimated user's emotions. Recipe presentation methods include, but are not limited to, text format, images, videos, and the like. For example, if the user is feeling stressed, the recipe generation unit can provide a simple and easy-to-understand recipe using the generation AI. Furthermore, if the user is relaxed, the recipe generation unit can provide a recipe with detailed instructions. Furthermore, if the user is in a hurry, the recipe generation unit can provide a short recipe that focuses on the main points. This allows the way the recipe is presented to be adjusted according to the user's emotions.

[0089] The recipe generation unit can adjust the level of detail of the recipe according to the user's cooking skill level. Cooking skill level includes, but is not limited to, for example, the user's self-assessment, past cooking history, and a method for adjusting the level of detail of the recipe. For example, the recipe generation unit may provide a recipe with detailed steps and images for beginners, for intermediate cooks, for simple steps and key points. The recipe generation unit may also provide a recipe that encourages creativity and ingenuity for advanced cooks, for advanced cooks. This allows the level of detail of the recipe to be adjusted according to the user's cooking skill level.

[0090] The recipe generation unit can propose appropriate cooking procedures by taking into account the user's kitchen equipment. Examples of kitchen equipment include, but are not limited to, the user's equipment list, the characteristics of the equipment, and a method for proposing optimal cooking procedures. For example, the recipe generation unit provides a recipe that takes into account available cooking utensils based on the user's kitchen equipment using the generation AI. The recipe generation unit can also provide a recipe that optimizes cooking time and temperature by taking into account the performance of the user's kitchen equipment. The recipe generation unit can also propose efficient cooking procedures by taking into account the layout of the user's kitchen equipment. This makes it possible to propose optimal cooking procedures by taking into account the user's kitchen equipment.

[0091] The recipe generation unit can improve the accuracy of a recipe by referring to the user's past cooking history. Past cooking history includes, but is not limited to, for example, a cooking log, a history database, and a method for improving recipe accuracy. For example, the recipe generation unit uses a generation AI to analyze the user's past cooking history and provide a recipe that reflects the user's preferred seasonings and cooking methods. The recipe generation unit can also suggest a new recipe that incorporates elements of successful recipes from the user's past cooking history. The recipe generation unit can also provide a recipe that includes advice on how to avoid failure based on the user's past cooking history. This allows the accuracy of a recipe to be improved by referring to the user's past cooking history.

[0092] The recipe generation unit can estimate the user's emotions and adjust the length of the recipe based on the estimated user's emotions. Examples of recipe length include, but are not limited to, the number of steps, the length of sentences, and the level of detail of information. For example, if the user is feeling stressed, the recipe generation unit can provide a short and concise recipe. Also, if the user is relaxed, the recipe generation unit can provide a longer recipe with detailed explanations. Also, if the user is in a hurry, the recipe generation unit can provide a short recipe that focuses on the main points. This allows the length of the recipe to be adjusted according to the user's emotions.

[0093] The recipe generation unit can incorporate regional cooking methods by taking into account the user's geographical location information. Regional cooking methods include, but are not limited to, regional culinary culture, traditional cooking methods, and methods of reflecting these in recipes. For example, the recipe generation unit can provide recipes incorporating regional cooking methods based on the user's geographical location information using the generation AI. The recipe generation unit can also suggest recipes incorporating regional traditional cooking methods. The recipe generation unit can also provide recipes using regional ingredients by taking into account the user's geographical location information. This allows regional cooking methods to be incorporated by taking into account the user's geographical location information.

[0094] The recipe generation unit can analyze the user's social media activity and suggest related recipes. Examples of social media activity include, but are not limited to, analysis of post content, analysis of followers, and methods of suggesting related recipes. For example, the recipe generation unit uses a generation AI to analyze the user's social media posts and suggest related recipes. The recipe generation unit can also use the generation AI to suggest related recipes based on the user's social media check-in information. The recipe generation unit can also use the generation AI to suggest related recipes based on the activity of the user's friends on social media. In this way, the user's social media activity can be analyzed and related recipes can be suggested.

[0095] The recipe generation unit can customize the recipe content by reflecting the user's past feedback. Past feedback includes, for example, user evaluation data, a method for analyzing the feedback, a method for customizing the recipe content, and the like, but is not limited to these examples. For example, the recipe generation unit provides a recipe that reflects the user's preferred seasonings and cooking methods based on the user's past feedback. The recipe generation unit can also suggest a recipe that reflects improvements based on the user's past feedback. The recipe generation unit can also provide an optimal recipe by using the generation AI to take the user's past feedback into consideration. This allows the recipe content to be customized by reflecting the user's past feedback.

[0096] The image generation unit can estimate the user's emotions and adjust the way the image is presented based on the estimated user's emotions. Examples of the way the image is presented include, but are not limited to, image style, color usage, and layout. For example, if the user is feeling stressed, the image generation AI can provide a simple and calm image. Also, if the user is relaxed, the image generation unit can provide a detailed and vivid image. Also, if the user is in a hurry, the image generation unit can provide a concise image that gets to the point. This makes it possible to adjust the way the image is presented according to the user's emotions.

[0097] The image generation unit can generate an optimal image by referring to the user's past image history. Past image history includes, but is not limited to, an image log, a history database, and a method for generating an optimal image. For example, the image generation unit uses a generation AI to analyze the user's past image history and provide an image that reflects the user's preferred style. The image generation unit can also propose a new image that incorporates elements of successful images from the user's past image history. The image generation unit can also provide an image that includes advice on how to avoid failure based on the user's past image history. This allows the optimal image to be generated by referring to the user's past image history.

[0098] The image generation unit can customize the style of the image according to the user's preferences. Examples of image styles include, but are not limited to, art style, color usage, and design elements. For example, the image generation unit provides an image that reflects the user's preferred colors and design using a generation AI. The image generation unit can also propose an image that incorporates the user's preferred art style using a generation AI. The image generation unit can also provide an image that takes into account the user's preferred fonts and layout. This allows the image style to be customized according to the user's preferences.

[0099] The image generation unit can generate an image at an appropriate resolution by taking into account the user's device information. Device information includes, but is not limited to, for example, the device resolution, screen size, and optimal image generation method. For example, the image generation unit provides an image at an optimal resolution based on the screen resolution of the user's device by the generation AI. The image generation unit can also provide an image with optimized processing speed by taking into account the performance of the user's device. The image generation unit can also provide an image with high visibility by taking into account the screen size of the user's device. This allows the image to be generated at an optimal resolution by taking into account the user's device information.

[0100] The image generation unit can estimate the user's emotions and determine the priority of images based on the estimated user emotions. Methods for determining the priority of images include, but are not limited to, the user's emotion score, preferences, and past history. For example, if the user is feeling stressed, the image generation unit can cause the generation AI to preferentially provide images that have a relaxing effect. Also, if the user is relaxed, the image generation unit can cause the generation AI to preferentially provide enjoyable images. Also, if the user is in a hurry, the image generation unit can cause the generation AI to preferentially provide concise images that get to the point. In this way, the priority of images can be determined according to the user's emotions.

[0101] The image generation unit can generate an image that reflects a region's unique food culture by taking into account the user's geographical location information. Examples of a region's unique food culture include, but are not limited to, the region's culinary culture, traditional ingredients, and methods of reflecting the region's unique food culture. For example, the image generation unit provides an image that reflects the region's unique food culture based on the user's geographical location information using a generation AI. The image generation unit can also make suggestions that incorporate images of traditional dishes of the region. The image generation unit can also provide images of dishes that use local ingredients by taking into account the user's geographical location information. In this way, an image that reflects a region's unique food culture can be generated by taking into account the user's geographical location information.

[0102] The image generation unit can analyze the user's social media activity and suggest related images. Social media activity includes, but is not limited to, for example, analysis of post content, analysis of followers, and methods for suggesting related images. For example, the image generation unit can have a generation AI analyze the user's social media post content and suggest related images. The image generation unit can also have the generation AI suggest related images based on the user's social media check-in information. The image generation unit can also have the generation AI suggest related images based on the activity of the user's friends on social media. In this way, the user's social media activity can be analyzed and related images can be suggested.

[0103] The image generation unit can customize the image content by reflecting the user's past feedback. Past feedback includes, for example, user evaluation data, a feedback analysis method, and a method for customizing the image content, but is not limited to these examples. For example, the image generation unit provides an image that reflects the user's preferred style based on the user's past feedback. The image generation unit can also suggest an image that reflects improvements based on the user's past feedback. The image generation unit can also provide an optimal image by using the generation AI to take the user's past feedback into consideration. This allows the image content to be customized by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the image analysis unit, menu suggestion unit, recipe generation unit, and image generation unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the image analysis unit acquires an image of a food label using the camera 42 of the smart device 14, and the image is analyzed by the specific processing unit 290 of the data processing device 12. For example, the menu suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests an optimal menu based on the identified information. For example, the recipe generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a recipe based on the proposed menu. For example, the image generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a finished image based on the generated recipe. === Hard Collateral 1-2 === Each of the multiple elements, including the image analysis unit, menu suggestion unit, recipe generation unit, and image generation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the image analysis unit acquires an image of a food label using the camera 42 of the smart glasses 214, and the image is analyzed by the specific processing unit 290 of the data processing device 12. For example, the menu suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests an optimal menu based on the identified information. For example, the recipe generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a recipe based on the proposed menu. For example, the image generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a finished image based on the generated recipe. === Hard Collateral 1-3 === Each of the multiple elements including the image analysis unit, menu suggestion unit, recipe generation unit, and image generation unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the image analysis unit acquires an image of a food label using the camera 42 of the headset terminal 314, and the image is analyzed by the specific processing unit 290 of the data processing device 12. For example, the menu suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests an optimal menu based on the identified information. For example, the recipe generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a recipe based on the proposed menu. For example, the image generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a finished image based on the generated recipe. === Hard Collateral 1-4 === Each of the multiple elements including the image analysis unit, menu suggestion unit, recipe generation unit, and image generation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the image analysis unit acquires an image of a food label using the camera 42 of the robot 414, and the image is analyzed by the specific processing unit 290 of the data processing device 12. For example, the menu suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and proposes an optimal menu based on the identified information. For example, the recipe generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a recipe based on the proposed menu. For example, the image generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a finished image based on the generated recipe.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The image analysis unit can improve analysis accuracy by referencing the user's dietary history and learning patterns of past food purchases. For example, it can analyze the types and frequency of foods the user has purchased in the past to improve recognition accuracy for specific food labeling. The image analysis unit can also learn patterns of specific brands or products from the user's dietary history and quickly analyze similar food labels. Furthermore, the image analysis unit can adjust analysis accuracy based on the user's dietary history, taking into account seasonal variations in food labeling. This makes it possible to improve the accuracy of image analysis by utilizing the user's dietary history.

[0106] The menu suggestion unit can estimate the user's emotions and adjust the menu variations it suggests based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI can suggest multiple simple and easy-to-make menus. Also, if the user is relaxed, the generation AI can suggest a variety of menus that can be enjoyed over time. Furthermore, if the user is in a hurry, the generation AI can suggest multiple menus that can be made in a short amount of time, increasing the number of options. This makes it possible to adjust the menu variations according to the user's emotions.

[0107] The recipe generation unit can adjust the difficulty of the recipe according to the user's cooking skill level. For example, for beginners, it can provide recipes with detailed steps and images. For intermediate cooks, it can provide recipes with simple steps and key points. Furthermore, for advanced cooks, it can provide recipes that encourage creativity and allow the user to test their skills. In this way, it is possible to adjust the difficulty of the recipe according to the user's cooking skill level.

[0108] The image generation unit can estimate the user's emotions and adjust the color usage of the image based on the estimated user's emotions. For example, if the user is feeling stressed, the generation AI can provide an image with calm colors. If the user is relaxed, the generation AI can also provide an image with vivid and bright colors. Furthermore, if the user is in a hurry, the generation AI can provide an image with simple and highly visible colors. This makes it possible to adjust the color usage of the image according to the user's emotions.

[0109] The image analysis unit can prioritize analysis of regional food labels by taking into account the user's geographic location information. For example, the generative AI can prioritize analysis of regional food labels based on the user's geographic location information. The image analysis unit can also learn regional fonts and layouts to improve analysis accuracy. Furthermore, the image analysis unit can provide regional nutritional information. This allows analysis of regional food labels by taking into account the user's geographic location information.

[0110] The menu suggestion unit can estimate the user's emotions and adjust the order of the menu suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI can prioritize suggesting menus that have a relaxing effect. Also, if the user is relaxed, the generation AI can prioritize suggesting menus that are enjoyable. Furthermore, if the user is in a hurry, the generation AI can prioritize suggesting menus that can be made in a short amount of time. This makes it possible to adjust the order of the menu according to the user's emotions.

[0111] The recipe generation unit can propose appropriate cooking procedures taking into account the user's kitchen equipment. For example, the generation AI can provide recipes that take into account the available cooking utensils based on the user's kitchen equipment. The generation AI can also provide recipes that optimize cooking times and temperatures by taking into account the performance of the user's kitchen equipment. Furthermore, the generation AI can also propose efficient cooking procedures by taking into account the layout of the user's kitchen equipment. This makes it possible to propose optimal cooking procedures taking into account the user's kitchen equipment.

[0112] The image generation unit can estimate the user's emotions and adjust the image style based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI can provide a simple and calm style image. If the user is relaxed, the generation AI can provide a detailed and vivid style image. Furthermore, if the user is in a hurry, the generation AI can provide a concise style image that gets to the point. In this way, the image style can be adjusted according to the user's emotions.

[0113] The image analysis unit can improve analysis accuracy by integrating multiple images. For example, the generation AI can integrate images of food labels taken from multiple angles to improve analysis accuracy. The generation AI can also integrate images of food labels taken at different times to improve analysis accuracy. Furthermore, the generation AI can also integrate images of food labels taken with different devices to improve analysis accuracy. This makes it possible to integrate multiple images to improve analysis accuracy.

[0114] The recipe generation unit can estimate the user's emotions and adjust the way the recipe is presented based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI can provide a simple and easy-to-understand recipe. If the user is relaxed, the generation AI can also provide a recipe with detailed instructions. Furthermore, if the user is in a hurry, the generation AI can also provide a short recipe that focuses on the main points. This makes it possible to adjust the way the recipe is presented according to the user's emotions.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The image analysis unit analyzes the image of the food label. For example, the image analysis unit reads the letters and numbers on the food label and identifies the type of food and its nutritional components. The image analysis unit uses generative AI and image recognition technology to read the letters and numbers on the food label. For example, it extracts information such as calories, protein, fat, and carbohydrates listed on the food label. Step 2: The menu suggestion unit suggests an optimal menu based on the information identified by the image analysis unit. For example, the menu suggestion unit generates a balanced menu based on the user's health condition and preferences. The menu suggestion unit uses a generation AI to suggest an optimal menu based on the identified information. Step 3: The recipe generation unit generates a recipe based on the menu proposed by the menu suggestion unit. For example, the recipe generation unit provides a recipe that details the necessary ingredients and cooking steps. The recipe generation unit uses a generation AI to generate a recipe based on the menu. Step 4: The image generation unit generates a finished image based on the recipe generated by the recipe generation unit. For example, the image generation unit generates a photo or illustration of the finished dish and provides it to the user. The image generation unit uses generative AI to generate a photo or illustration of the finished dish.

[0117] 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.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 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. an image analysis unit that analyzes images of food labels; A menu suggestion unit that suggests an appropriate menu based on the information identified by the image analysis unit; a recipe creation unit that creates a recipe based on the menu proposed by the menu proposal unit; an image generation unit that generates a finished image based on the recipe generated by the recipe generation unit; Equipped with A system characterized by:

2. The image analysis unit Reads letters and numbers on food labels to identify food types and nutritional information The system of claim 1 .

3. The menu suggestion unit Providing nutritionally balanced meals based on the user's health condition and preferences The system of claim 1 .

4. The recipe generation unit Generate recipes with detailed ingredients and cooking steps The system of claim 1 .

5. The image generation unit Generate photos and illustrations of the finished dish The system of claim 1 .

6. The image analysis unit Estimate the user's emotions and adjust the accuracy of image analysis based on the estimated user emotions. The system of claim 1 .

7. The image analysis unit Adjusting analysis algorithms to account for differences in fonts and layouts on food labels The system of claim 1 .

8. The image analysis unit Integrating multiple images to improve analysis accuracy The system of claim 1 .

Images