system

Abstract

The system according to this embodiment aims to enable users to easily find the optimal recipe and understand the specific cooking procedure. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a suggestion unit, and a guidance unit. The reception unit reads a photograph of a meal. The analysis unit analyzes the photograph read by the reception unit and identifies the ingredients or type of dish. The suggestion unit suggests a recipe based on the information obtained by the analysis unit. The guidance unit provides instructions on the specific cooking procedure of the recipe suggested by the suggestion unit using audio or video.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for a user to find an optimal recipe for daily meals and it takes time to understand cooking procedures.

[0005] The system according to the embodiment aims to enable a user to easily find an optimal recipe and understand specific cooking procedures.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a suggestion unit, and a guidance unit. The reception unit reads a photograph of a meal. The analysis unit analyzes the photograph read by the reception unit and identifies the ingredients or type of dish. The suggestion unit suggests a recipe based on the information obtained by the analysis unit. The guidance unit provides instructions on the specific cooking steps of the recipe suggested by the suggestion unit using audio or video. [Effects of the Invention]

[0007] The system according to this embodiment can enable users to easily find the optimal recipe and understand the specific cooking procedure. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The recipe suggestion system according to an embodiment of the present invention is a system in which the AI ​​suggests the optimal recipe and further analyzes the calories and nutritional components of a meal based on photos of the user's daily meals. When the user uploads a photo of a meal to the AI, the AI ​​analyzes the photo and identifies the ingredients and type of dish. Furthermore, the AI ​​analyzes the calories and nutritional components and suggests the optimal recipe or meal menu to the user based on the results. The suggested recipe or meal menu provides specific cooking instructions through voice and video, so the user can easily make delicious and nutritious meals. This provides an enjoyable recipe time even on busy days and helps maintain a healthy diet. For example, the AI ​​analyzes a photo of a meal taken by the user and calculates the calories and nutritional components of that meal. Based on the results, it suggests an appropriate meal menu to the user. This allows the user to maintain a healthy diet. Thus, the recipe suggestion system can analyze photos of the user's meals and suggest the optimal recipe or meal menu.

[0029] The recipe suggestion system according to this embodiment comprises a reception unit, an analysis unit, a suggestion unit, and a guidance unit. The reception unit receives a photo of a meal from the user. The user can use, for example, a smartphone or a digital camera to receive the photo. The reception unit can accept photos in JPEG or PNG format. The analysis unit analyzes the photo received by the reception unit to identify the ingredients and type of dish. The analysis unit uses, for example, an image recognition algorithm or a machine learning model to identify the ingredients and type of dish included in the photo. The analysis unit can perform analysis based on a list of ingredients or a category of dish. The suggestion unit proposes the optimal recipe based on the information obtained by the analysis unit. The suggestion unit can propose the optimal recipe considering, for example, the user's preferences and nutritional balance. The suggestion unit makes a proposal that includes a list of ingredients and cooking procedures for the recipe. The guidance unit teaches the specific cooking procedures for the recipe proposed by the suggestion unit using audio and video. The guidance unit can teach the user the cooking procedures using, for example, an audio guide or a video tutorial. Thus, the recipe suggestion system according to this embodiment can propose the optimal recipe and teach the specific cooking procedures when the user receives a photo of a meal.

[0030] The analysis unit can analyze calories or nutrients from a photograph of a meal. For example, the analysis unit can analyze a photograph of a meal and calculate calories and nutrients. The analysis unit can use image recognition technology to identify ingredients contained in a photograph and calculate the calories and nutrients of those ingredients. For example, the analysis unit can identify the types and quantities of ingredients contained in a photograph and calculate calories and nutrients based on that. The analysis unit can also refer to a nutritional database of ingredients to calculate the calories and nutrients of the identified ingredients. As a result, the analysis unit can analyze calories and nutrients from a photograph of a meal and suggest appropriate meal menus to the user.

[0031] The suggestion unit can propose meal menus based on calorie or nutritional information obtained by the analysis unit. For example, the suggestion unit can propose an appropriate meal menu to the user based on the calorie and nutritional information obtained by the analysis unit. The suggestion unit can propose the optimal meal menu considering the user's health condition and dietary preferences. For example, if the user is on a diet, the suggestion unit will propose a low-calorie meal menu. Also, if the user wants to consume more of a particular nutrient, the suggestion unit can propose a meal menu that contains a lot of that nutrient. In this way, the suggestion unit can support the user's healthy eating habits by proposing an appropriate meal menu based on calorie and nutritional information.

[0032] The instruction unit can provide specific cooking instructions via audio or video. For example, the instruction unit can teach the specific cooking steps of a recipe suggested by the suggestion unit using audio guides or video tutorials. The instruction unit can provide real-time audio guidance as the user cooks. For example, the instruction unit will guide the user through the next step by audio as they progress through the cooking process. The instruction unit can also visually demonstrate the cooking process using video tutorials. For example, the instruction unit can show each step of the cooking process in a video, allowing the user to follow along as they cook. In this way, the instruction unit makes it easy for users to cook by providing specific cooking instructions via audio and video.

[0033] The reception desk can analyze the user's past meal photo history and select the optimal loading method. For example, the reception desk can analyze the user's past meal photo history and select the optimal loading method. The reception desk can prioritize suggesting loading methods that the user has frequently used in the past. Furthermore, the reception desk can suggest the optimal loading method for a specific time period based on the user's past meal photo history. In addition, the reception desk can analyze the user's past meal photo history and select the most efficient loading method. Thus, the reception desk can provide the optimal loading method by analyzing the user's past meal photo history.

[0034] The reception unit can filter food photos when they are loaded, based on the user's current health status and dietary restrictions. For example, the reception unit can refer to the user's health data and filter the food photos when they are loaded. If the user has specific dietary restrictions, the reception unit can filter the food photos based on those restrictions. The reception unit can also prioritize loading appropriate food photos based on the user's current health status. Furthermore, the reception unit can refer to the user's health data and select appropriate food photos. In this way, the reception unit can select appropriate food photos based on the user's health status and dietary restrictions.

[0035] The reception system can prioritize loading relevant food photos based on the user's geographical location when loading food photos. For example, the reception system can obtain the user's geographical location and filter food photos based on that information. If the user is in a specific region, the reception system can prioritize loading food photos that use ingredients from that region. The reception system can also suggest recipes using local ingredients based on the user's current location. Furthermore, the reception system can refer to the user's geographical location and prioritize loading relevant food photos. This allows the reception system to prioritize loading relevant food photos based on the user's geographical location.

[0036] The reception system can analyze the user's social media activity when loading food photos and load relevant photos. For example, the reception system can analyze the user's social media activity and filter food photos based on that information. The reception system can prioritize loading food photos that the user has shared on social media. The reception system can also suggest relevant food photos based on the user's social media activity. Furthermore, the reception system can analyze the content of the user's social media posts and load relevant food photos. In this way, the reception system can load relevant food photos based on the user's social media activity.

[0037] The analysis unit can adjust the analysis results based on the freshness or quality of the ingredients. For example, the analysis unit adjusts the analysis results considering the freshness and quality of the ingredients. If the ingredients are very fresh, the analysis unit can rate their nutritional value higher. Conversely, if the ingredients are of poor quality, the analysis unit can rate their nutritional value lower. Furthermore, the analysis unit can adjust the analysis results based on the freshness and quality of the ingredients. As a result, the analysis unit can provide more accurate analysis results by adjusting the results based on the freshness and quality of the ingredients.

[0038] The analysis unit can apply different analysis algorithms depending on the type of meal or cooking method. For example, the analysis unit can apply different analysis algorithms depending on the type of meal and cooking method. In the case of raw food, the analysis unit can perform analysis that emphasizes specific nutritional components. Furthermore, the analysis unit can analyze different nutritional components depending on the cooking method. In addition, the analysis unit can apply the optimal analysis algorithm based on the type of meal. As a result, the accuracy of the analysis is improved by applying the optimal analysis algorithm according to the type of meal and cooking method.

[0039] The analysis unit can determine the priority of analysis based on when the meal was photographed. For example, the analysis unit can determine the priority of analysis based on when the meal was photographed. The analysis unit can prioritize the analysis of recently taken meal photos. It can also prioritize the analysis of meal photos taken during a specific time period. Furthermore, the analysis unit can determine the priority of analysis based on the user's eating patterns. As a result, the analysis unit can perform more appropriate analysis by determining the priority of analysis based on when the meal was photographed.

[0040] The analysis unit can improve the accuracy of its analysis based on dietary literature. For example, the analysis unit can refer to dietary literature and use that information to improve the accuracy of its analysis. The analysis unit can refer to dietary literature and supplement the analysis results. Furthermore, the analysis unit can improve the accuracy of its analysis based on the latest research findings. In addition, the analysis unit can analyze dietary literature and reflect that information in its analysis results. Thus, the analysis unit improves the accuracy of its analysis by referring to dietary literature.

[0041] The suggestion function can adjust the level of detail of its suggestions based on the calories or nutritional content of a meal. For example, the suggestion function can adjust the level of detail of its suggestions based on the calories or nutritional content of a meal. In the case of a high-calorie meal, the suggestion function can provide detailed nutritional information. Conversely, in the case of a low-calorie meal, the suggestion function can provide concise nutritional information. Furthermore, the suggestion function can adjust the level of detail of its suggestions based on the nutritional content. This allows the suggestion function to provide more appropriate suggestions by adjusting the level of detail of its suggestions based on the calories and nutritional content of a meal.

[0042] The suggestion unit can apply different suggestion algorithms based on the user's past eating history. For example, the suggestion unit can analyze the user's past eating history and apply different suggestion algorithms based on that information. The suggestion unit can analyze the user's past eating history and make optimal suggestions. Furthermore, the suggestion unit can apply different suggestion algorithms based on the user's eating patterns. In addition, the suggestion unit can refer to the user's past eating history to improve the accuracy of its suggestions. As a result, the suggestion unit can make optimal suggestions based on the user's past eating history.

[0043] The suggestion function can prioritize suggestions based on when the meal was photographed. For example, the suggestion function can prioritize suggestions based on when the meal was photographed. The suggestion function can make suggestions based on recently taken meal photos. It can also make suggestions based on meal photos taken during a specific time period. Furthermore, the suggestion function can prioritize suggestions based on the user's eating patterns. This allows the suggestion function to make more appropriate suggestions by prioritizing suggestions based on when the meal was photographed.

[0044] The suggestion function can adjust the order of suggestions according to the relevance of the meal. For example, the suggestion function will prioritize suggestions related to the user's current meal. The suggestion function can also provide highly relevant suggestions based on the user's past meal history. Furthermore, the suggestion function can adjust the order of suggestions based on the relevance of the meal. This allows the suggestion function to provide more appropriate suggestions by adjusting the order of suggestions based on the relevance of the meal.

[0045] The instruction unit can select the most appropriate explanation method based on the user's past cooking experience when explaining cooking procedures. For example, the instruction unit can refer to the user's past cooking experience and select the most appropriate explanation method based on that information. The instruction unit can select the most appropriate explanation method based on the user's past cooking experience. In addition, the instruction unit can provide detailed explanations according to the user's cooking experience. Furthermore, the instruction unit can refer to the user's past cooking experience and adjust the difficulty level of the explanation. As a result, the instruction unit can provide more appropriate explanations by offering the most appropriate explanation method based on the user's past cooking experience.

[0046] The instruction unit can apply different explanation algorithms depending on the characteristics of the ingredients and cooking equipment when explaining cooking procedures. For example, when using a specific ingredient, the instruction unit will provide explanations tailored to its characteristics. The instruction unit can select the optimal explanation method depending on the cooking equipment. Furthermore, the instruction unit can apply different explanation algorithms based on the characteristics of the ingredients and cooking equipment. This allows the instruction unit to provide more appropriate explanations by applying the optimal explanation algorithm based on the characteristics of the ingredients and cooking equipment.

[0047] The instruction unit can select the optimal explanation method based on the user's device information when explaining cooking procedures. For example, the instruction unit can select the optimal explanation method considering the user's device information. If the user is using a smartphone, the instruction unit can provide a display method that is adapted to the screen size. If the user is using a tablet, the instruction unit can provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the instruction unit can provide a concise and highly visible display method. In this way, the instruction unit can provide more appropriate explanations by providing the optimal explanation method based on the user's device information.

[0048] The instruction team can suggest relevant cooking steps based on the user's social media activity when explaining cooking procedures. For example, the instruction team can analyze the user's social media activity and suggest relevant cooking steps based on that information. The instruction team can prioritize suggesting cooking steps that the user has shared on social media. Furthermore, the instruction team can suggest relevant cooking steps from the user's social media activity. In addition, the instruction team can analyze the content of the user's social media posts and suggest relevant cooking steps. This allows the instruction team to provide more appropriate explanations by suggesting relevant cooking steps based on the user's social media activity.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] When the reception desk reads a user's meal photos, it can refer to the user's past meal history and automatically tag similar meals. For example, it can compare a user's past meal photos with current photos and add information as a tag if the same ingredients or dishes are included. The reception desk can also prioritize suggesting recipes related to ingredients if the user frequently uses a particular ingredient. Furthermore, by analyzing the user's meal history and understanding seasonal eating patterns, the reception desk can suggest recipes appropriate for the season. This allows the reception desk to leverage the user's past meal history to provide more personalized recipe suggestions.

[0051] The analysis unit can consider the origin of ingredients when analyzing calories and nutritional components from food photos. For example, the analysis unit can more accurately calculate the nutritional value and calories of ingredients based on their origin. Furthermore, the analysis unit can consider differences in nutritional value depending on the origin of ingredients and suggest the best ingredient selection to the user. In addition, the analysis unit can suggest recipes using local ingredients based on the origin of the ingredients. This allows the analysis unit to utilize origin information to perform more accurate nutritional analysis and suggest recipes.

[0052] The suggestion unit can propose meal plans that take into account the user's activity level, based on the calorie and nutritional information obtained by the analysis unit. For example, the suggestion unit can consider the type and frequency of exercise the user performs daily and propose a corresponding calorie intake. It can also propose an optimal meal plan after the user has performed a specific type of exercise. Furthermore, the suggestion unit can propose meal plans that are effective for muscle building or body fat reduction, depending on the user's exercise goals. In this way, the suggestion unit can support a healthier lifestyle by proposing meal plans that take into account the user's activity level.

[0053] The instruction system can provide detailed cooking instructions via audio or video, tailoring the guidance to the user's cooking skill level. For example, it can explain basic cooking procedures in detail for beginners and suggest efficient cooking methods for advanced users. It can also refer to the user's past cooking experience and select the most appropriate instruction method based on that experience. Furthermore, it can suggest recipes of varying difficulty levels depending on the user's cooking skill level. This allows the instruction system to provide more effective cooking support by tailoring guidance to the user's skill level.

[0054] The reception desk can analyze the user's past meal photo history and consider the user's eating patterns when selecting the optimal loading method. For example, if the reception desk tends to eat at a specific time of day, it can suggest the most suitable loading method for that time slot. Similarly, if the reception desk tends to use a specific ingredient on a particular day of the week, it can suggest a loading method related to that ingredient. Furthermore, the reception desk can analyze the user's eating patterns and select the most efficient loading method. This allows the reception desk to provide an optimal loading method that takes the user's eating patterns into account, resulting in a smoother user experience.

[0055] The reception system can refer to the user's medical data when filtering food photos based on the user's current health status and dietary restrictions. For example, the reception system can filter food photos that correspond to specific allergies or diseases based on the user's medical data. It can also exclude food photos containing specific nutrients if the user needs to restrict those nutrients, based on the user's medical data. Furthermore, the reception system can refer to the user's medical data to select the most appropriate food photos based on their health condition. This allows the reception system to provide more appropriate food photos by utilizing the user's medical data.

[0056] When loading food photos, the reception system can consider the user's travel history when prioritizing the loading of highly relevant photos based on the user's geographical location. For example, the reception system can prioritize loading food photos related to ingredients and dishes from regions the user has visited in the past. Furthermore, if the user frequently visits a particular region, it can suggest recipes using ingredients from that region. In addition, the reception system can analyze the user's travel history and select the most suitable food photos based on their eating patterns during their travels. This allows the reception system to leverage the user's travel history to provide more personalized food photos.

[0057] The reception system can analyze the user's social media activity when loading food photos and consider the reactions of the user's followers when loading relevant photos. For example, the reception system can analyze the reactions of followers to food photos shared by the user on social media and prioritize loading popular food photos. It can also suggest food photos that the user's followers prefer. Furthermore, the reception system can analyze the user's social media activity and select the most suitable food photos based on follower reactions. This allows the reception system to leverage the user's social media activity and follower reactions to provide more relevant food photos.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The reception desk receives photos of the meal from the user. The user takes photos of their meal using a smartphone or digital camera and uploads the photos in JPEG or PNG format to the reception desk. Step 2: The analysis unit analyzes the photos read by the reception unit to identify the types of ingredients and dishes. The analysis unit uses image recognition algorithms and machine learning models to identify the types of ingredients and dishes contained in the photos. The analysis unit performs analysis based on a list of ingredients and the category of dishes. Step 3: The suggestion unit proposes the optimal recipe based on the information obtained by the analysis unit. The suggestion unit considers the user's preferences and nutritional balance, and provides suggestions including a list of ingredients and cooking procedures for the recipe. Step 4: The instruction team provides audio and video instructions for the specific cooking steps of the recipe proposed by the suggestion team. The instruction team uses audio guides and video tutorials to teach users the cooking steps.

[0060] (Example of form 2) The recipe suggestion system according to an embodiment of the present invention is a system in which the AI ​​suggests the optimal recipe and further analyzes the calories and nutritional components of a meal based on photos of the user's daily meals. When the user uploads a photo of a meal to the AI, the AI ​​analyzes the photo and identifies the ingredients and type of dish. Furthermore, the AI ​​analyzes the calories and nutritional components and suggests the optimal recipe or meal menu to the user based on the results. The suggested recipe or meal menu provides specific cooking instructions through voice and video, so the user can easily make delicious and nutritious meals. This provides an enjoyable recipe time even on busy days and helps maintain a healthy diet. For example, the AI ​​analyzes a photo of a meal taken by the user and calculates the calories and nutritional components of that meal. Based on the results, it suggests an appropriate meal menu to the user. This allows the user to maintain a healthy diet. Thus, the recipe suggestion system can analyze photos of the user's meals and suggest the optimal recipe or meal menu.

[0061] The recipe suggestion system according to this embodiment comprises a reception unit, an analysis unit, a suggestion unit, and a guidance unit. The reception unit receives a photo of a meal from the user. The user can use, for example, a smartphone or a digital camera to receive the photo. The reception unit can accept photos in JPEG or PNG format. The analysis unit analyzes the photo received by the reception unit to identify the ingredients and type of dish. The analysis unit uses, for example, an image recognition algorithm or a machine learning model to identify the ingredients and type of dish included in the photo. The analysis unit can perform analysis based on a list of ingredients or a category of dish. The suggestion unit proposes the optimal recipe based on the information obtained by the analysis unit. The suggestion unit can propose the optimal recipe considering, for example, the user's preferences and nutritional balance. The suggestion unit makes a proposal that includes a list of ingredients and cooking procedures for the recipe. The guidance unit teaches the specific cooking procedures for the recipe proposed by the suggestion unit using audio and video. The guidance unit can teach the user the cooking procedures using, for example, an audio guide or a video tutorial. Thus, the recipe suggestion system according to this embodiment can propose the optimal recipe and teach the specific cooking procedures when the user receives a photo of a meal.

[0062] The analysis unit can analyze calories or nutrients from a photograph of a meal. For example, the analysis unit can analyze a photograph of a meal and calculate calories and nutrients. The analysis unit can use image recognition technology to identify ingredients contained in a photograph and calculate the calories and nutrients of those ingredients. For example, the analysis unit can identify the types and quantities of ingredients contained in a photograph and calculate calories and nutrients based on that. The analysis unit can also refer to a nutritional database of ingredients to calculate the calories and nutrients of the identified ingredients. As a result, the analysis unit can analyze calories and nutrients from a photograph of a meal and suggest appropriate meal menus to the user.

[0063] The suggestion unit can propose meal menus based on calorie or nutritional information obtained by the analysis unit. For example, the suggestion unit can propose an appropriate meal menu to the user based on the calorie and nutritional information obtained by the analysis unit. The suggestion unit can propose the optimal meal menu considering the user's health condition and dietary preferences. For example, if the user is on a diet, the suggestion unit will propose a low-calorie meal menu. Also, if the user wants to consume more of a particular nutrient, the suggestion unit can propose a meal menu that contains a lot of that nutrient. In this way, the suggestion unit can support the user's healthy eating habits by proposing an appropriate meal menu based on calorie and nutritional information.

[0064] The instruction unit can provide specific cooking instructions via audio or video. For example, the instruction unit can teach the specific cooking steps of a recipe suggested by the suggestion unit using audio guides or video tutorials. The instruction unit can provide real-time audio guidance as the user cooks. For example, the instruction unit will guide the user through the next step by audio as they progress through the cooking process. The instruction unit can also visually demonstrate the cooking process using video tutorials. For example, the instruction unit can show each step of the cooking process in a video, allowing the user to follow along as they cook. In this way, the instruction unit makes it easy for users to cook by providing specific cooking instructions via audio and video.

[0065] The reception unit can estimate the user's emotions and adjust the timing of loading food photos based on those emotions. For example, the reception unit can estimate the user's emotions using facial recognition technology. If the user is feeling stressed, the reception unit can prompt them to load food photos during a time when they can relax. The reception unit can also provide an interface that allows the user to load food photos quickly if they are busy. Furthermore, if the user is relaxed, the reception unit can provide loading instructions that include detailed explanations. By adjusting the timing of loading food photos according to the user's emotions, the reception unit can load photos at a more appropriate time.

[0066] The reception desk can analyze the user's past meal photo history and select the optimal loading method. For example, the reception desk can analyze the user's past meal photo history and select the optimal loading method. The reception desk can prioritize suggesting loading methods that the user has frequently used in the past. Furthermore, the reception desk can suggest the optimal loading method for a specific time period based on the user's past meal photo history. In addition, the reception desk can analyze the user's past meal photo history and select the most efficient loading method. Thus, the reception desk can provide the optimal loading method by analyzing the user's past meal photo history.

[0067] The reception unit can filter food photos when they are loaded, based on the user's current health status and dietary restrictions. For example, the reception unit can refer to the user's health data and filter the food photos when they are loaded. If the user has specific dietary restrictions, the reception unit can filter the food photos based on those restrictions. The reception unit can also prioritize loading appropriate food photos based on the user's current health status. Furthermore, the reception unit can refer to the user's health data and select appropriate food photos. In this way, the reception unit can select appropriate food photos based on the user's health status and dietary restrictions.

[0068] The reception system can estimate the user's emotions and prioritize which food photos to load based on those emotions. For example, the reception system can estimate the user's emotions using facial recognition technology. If the user is stressed, the reception system can prioritize loading relaxing food photos. If the user is busy, the reception system can prioritize loading photos of easy-to-make meals. Furthermore, if the user is relaxed, the reception system can prioritize loading food photos that include detailed recipes. In this way, the reception system can prioritize more appropriate photos by prioritizing food photos according to the user's emotions.

[0069] The reception system can prioritize loading relevant food photos based on the user's geographical location when loading food photos. For example, the reception system can obtain the user's geographical location and filter food photos based on that information. If the user is in a specific region, the reception system can prioritize loading food photos that use ingredients from that region. The reception system can also suggest recipes using local ingredients based on the user's current location. Furthermore, the reception system can refer to the user's geographical location and prioritize loading relevant food photos. This allows the reception system to prioritize loading relevant food photos based on the user's geographical location.

[0070] The reception system can analyze the user's social media activity when loading food photos and load relevant photos. For example, the reception system can analyze the user's social media activity and filter food photos based on that information. The reception system can prioritize loading food photos that the user has shared on social media. The reception system can also suggest relevant food photos based on the user's social media activity. Furthermore, the reception system can analyze the content of the user's social media posts and load relevant food photos. In this way, the reception system can load relevant food photos based on the user's social media activity.

[0071] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, the analysis unit estimates the user's emotions using facial recognition technology. The analysis unit can perform a detailed analysis when the user is relaxed. It can also perform a simplified analysis when the user is in a hurry. Furthermore, if the user is stressed, the analysis unit can summarize the analysis results concisely. In this way, the analysis unit can provide more appropriate analysis results by adjusting the accuracy of the analysis according to the user's emotions.

[0072] The analysis unit can adjust the analysis results based on the freshness or quality of the ingredients. For example, the analysis unit adjusts the analysis results considering the freshness and quality of the ingredients. If the ingredients are very fresh, the analysis unit can rate their nutritional value higher. Conversely, if the ingredients are of poor quality, the analysis unit can rate their nutritional value lower. Furthermore, the analysis unit can adjust the analysis results based on the freshness and quality of the ingredients. As a result, the analysis unit can provide more accurate analysis results by adjusting the results based on the freshness and quality of the ingredients.

[0073] The analysis unit can apply different analysis algorithms depending on the type of meal or cooking method. For example, the analysis unit can apply different analysis algorithms depending on the type of meal and cooking method. In the case of raw food, the analysis unit can perform analysis that emphasizes specific nutritional components. Furthermore, the analysis unit can analyze different nutritional components depending on the cooking method. In addition, the analysis unit can apply the optimal analysis algorithm based on the type of meal. As a result, the accuracy of the analysis is improved by applying the optimal analysis algorithm according to the type of meal and cooking method.

[0074] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, the analysis unit estimates the user's emotions using facial expression recognition technology. If the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, the analysis unit can provide a more appropriate display by adjusting the display method of the analysis results according to the user's emotions.

[0075] The analysis unit can determine the priority of analysis based on when the meal was photographed. For example, the analysis unit can determine the priority of analysis based on when the meal was photographed. The analysis unit can prioritize the analysis of recently taken meal photos. It can also prioritize the analysis of meal photos taken during a specific time period. Furthermore, the analysis unit can determine the priority of analysis based on the user's eating patterns. As a result, the analysis unit can perform more appropriate analysis by determining the priority of analysis based on when the meal was photographed.

[0076] The analysis unit can improve the accuracy of its analysis based on dietary literature. For example, the analysis unit can refer to dietary literature and use that information to improve the accuracy of its analysis. The analysis unit can refer to dietary literature and supplement the analysis results. Furthermore, the analysis unit can improve the accuracy of its analysis based on the latest research findings. In addition, the analysis unit can analyze dietary literature and reflect that information in its analysis results. Thus, the analysis unit improves the accuracy of its analysis by referring to dietary literature.

[0077] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, it can use facial recognition technology to estimate the user's emotions. When the user is relaxed, the suggestion unit can provide detailed suggestions. When the user is in a hurry, it can provide concise suggestions. Furthermore, when the user is stressed, it can provide visually appealing suggestions. In this way, the suggestion unit can provide more appropriate suggestions by adjusting the way it presents its suggestions according to the user's emotions.

[0078] The suggestion function can adjust the level of detail of its suggestions based on the calories or nutritional content of a meal. For example, the suggestion function can adjust the level of detail of its suggestions based on the calories or nutritional content of a meal. In the case of a high-calorie meal, the suggestion function can provide detailed nutritional information. Conversely, in the case of a low-calorie meal, the suggestion function can provide concise nutritional information. Furthermore, the suggestion function can adjust the level of detail of its suggestions based on the nutritional content. This allows the suggestion function to provide more appropriate suggestions by adjusting the level of detail of its suggestions based on the calories and nutritional content of a meal.

[0079] The suggestion unit can apply different suggestion algorithms based on the user's past eating history. For example, the suggestion unit can analyze the user's past eating history and apply different suggestion algorithms based on that information. The suggestion unit can analyze the user's past eating history and make optimal suggestions. Furthermore, the suggestion unit can apply different suggestion algorithms based on the user's eating patterns. In addition, the suggestion unit can refer to the user's past eating history to improve the accuracy of its suggestions. As a result, the suggestion unit can make optimal suggestions based on the user's past eating history.

[0080] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on those emotions. For example, the suggestion unit can estimate the user's emotions using facial recognition technology. If the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. Furthermore, if the user is stressed, the suggestion unit can provide visually appealing suggestions. In this way, the suggestion unit can provide more appropriate suggestions by adjusting the length of the suggestion according to the user's emotions.

[0081] The suggestion function can prioritize suggestions based on when the meal was photographed. For example, the suggestion function can prioritize suggestions based on when the meal was photographed. The suggestion function can make suggestions based on recently taken meal photos. It can also make suggestions based on meal photos taken during a specific time period. Furthermore, the suggestion function can prioritize suggestions based on the user's eating patterns. This allows the suggestion function to make more appropriate suggestions by prioritizing suggestions based on when the meal was photographed.

[0082] The suggestion function can adjust the order of suggestions according to the relevance of the meal. For example, the suggestion function will prioritize suggestions related to the user's current meal. The suggestion function can also provide highly relevant suggestions based on the user's past meal history. Furthermore, the suggestion function can adjust the order of suggestions based on the relevance of the meal. This allows the suggestion function to provide more appropriate suggestions by adjusting the order of suggestions based on the relevance of the meal.

[0083] The instruction unit can estimate the user's emotions and adjust the way it explains cooking procedures based on those emotions. For example, the instruction unit can estimate the user's emotions using facial recognition technology. If the user is nervous, the instruction unit can explain in a calm voice. If the user is relaxed, the instruction unit can explain in a cheerful voice. Furthermore, if the user is in a hurry, the instruction unit can provide quick and concise explanations. In this way, the instruction unit can provide more appropriate explanations by adjusting the way it explains cooking procedures according to the user's emotions.

[0084] The instruction unit can select the most appropriate explanation method based on the user's past cooking experience when explaining cooking procedures. For example, the instruction unit can refer to the user's past cooking experience and select the most appropriate explanation method based on that information. The instruction unit can select the most appropriate explanation method based on the user's past cooking experience. In addition, the instruction unit can provide detailed explanations according to the user's cooking experience. Furthermore, the instruction unit can refer to the user's past cooking experience and adjust the difficulty level of the explanation. As a result, the instruction unit can provide more appropriate explanations by offering the most appropriate explanation method based on the user's past cooking experience.

[0085] The instruction unit can apply different explanation algorithms depending on the characteristics of the ingredients and cooking equipment when explaining cooking procedures. For example, when using a specific ingredient, the instruction unit will provide explanations tailored to its characteristics. The instruction unit can select the optimal explanation method depending on the cooking equipment. Furthermore, the instruction unit can apply different explanation algorithms based on the characteristics of the ingredients and cooking equipment. This allows the instruction unit to provide more appropriate explanations by applying the optimal explanation algorithm based on the characteristics of the ingredients and cooking equipment.

[0086] The instruction unit can estimate the user's emotions and adjust the way the cooking instructions are displayed based on those emotions. For example, the instruction unit can estimate the user's emotions using facial recognition technology. If the user is nervous, the instruction unit can provide a simple and highly visible display method. If the user is relaxed, the instruction unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the instruction unit can provide a display method that gets straight to the point. In this way, the instruction unit can provide a more appropriate display by adjusting the way the cooking instructions are displayed according to the user's emotions.

[0087] The instruction unit can select the optimal explanation method based on the user's device information when explaining cooking procedures. For example, the instruction unit can select the optimal explanation method considering the user's device information. If the user is using a smartphone, the instruction unit can provide a display method that is adapted to the screen size. If the user is using a tablet, the instruction unit can provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the instruction unit can provide a concise and highly visible display method. In this way, the instruction unit can provide more appropriate explanations by providing the optimal explanation method based on the user's device information.

[0088] The instruction team can suggest relevant cooking steps based on the user's social media activity when explaining cooking procedures. For example, the instruction team can analyze the user's social media activity and suggest relevant cooking steps based on that information. The instruction team can prioritize suggesting cooking steps that the user has shared on social media. Furthermore, the instruction team can suggest relevant cooking steps from the user's social media activity. In addition, the instruction team can analyze the content of the user's social media posts and suggest relevant cooking steps. This allows the instruction team to provide more appropriate explanations by suggesting relevant cooking steps based on the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, analysis unit, suggestion unit, and instruction unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit allows the user to upload a photo of a meal using the camera 42 of the smart device 14. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies the ingredients and type of dish using an image recognition algorithm or machine learning model. The suggestion unit is implemented by the identification processing unit 290 of the data processing unit 12 and suggests an optimal recipe considering the user's preferences and nutritional balance. The instruction unit uses the output device 40 of the smart device 14 to teach specific cooking procedures using voice and video. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, analysis unit, suggestion unit, and instruction unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit allows the user to read a photo of a meal using the camera 42 of the smart glasses 214. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies the ingredients and type of dish using an image recognition algorithm or machine learning model. The suggestion unit is implemented by the identification processing unit 290 of the data processing unit 12 and suggests an optimal recipe considering the user's preferences and nutritional balance. The instruction unit provides specific cooking instructions via voice using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, analysis unit, suggestion unit, and instruction unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit allows the user to upload a photo of a meal using the camera 42 of the headset terminal 314. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which identifies ingredients and types of dishes using image recognition algorithms and machine learning models. The suggestion unit is implemented by the identification processing unit 290 of the data processing unit 12, which suggests an optimal recipe considering the user's preferences and nutritional balance. The instruction unit provides specific cooking instructions via voice using the speaker 240 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, analysis unit, suggestion unit, and instruction unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit allows the user to read a photo of a meal using the camera 42 of the robot 414. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies the ingredients and type of dish using an image recognition algorithm or machine learning model. The suggestion unit is implemented by the identification processing unit 290 of the data processing unit 12 and suggests an optimal recipe considering the user's preferences and nutritional balance. The instruction unit provides specific cooking instructions by voice using the speaker 240 of the robot 414.

[0089] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0090] When the reception desk reads a user's meal photos, it can refer to the user's past meal history and automatically tag similar meals. For example, it can compare a user's past meal photos with current photos and add information as a tag if the same ingredients or dishes are included. The reception desk can also prioritize suggesting recipes related to ingredients if the user frequently uses a particular ingredient. Furthermore, by analyzing the user's meal history and understanding seasonal eating patterns, the reception desk can suggest recipes appropriate for the season. This allows the reception desk to leverage the user's past meal history to provide more personalized recipe suggestions.

[0091] The analysis unit can consider the origin of ingredients when analyzing calories and nutritional components from food photos. For example, the analysis unit can more accurately calculate the nutritional value and calories of ingredients based on their origin. Furthermore, the analysis unit can consider differences in nutritional value depending on the origin of ingredients and suggest the best ingredient selection to the user. In addition, the analysis unit can suggest recipes using local ingredients based on the origin of the ingredients. This allows the analysis unit to utilize origin information to perform more accurate nutritional analysis and suggest recipes.

[0092] The suggestion unit can propose meal plans that take into account the user's activity level, based on the calorie and nutritional information obtained by the analysis unit. For example, the suggestion unit can consider the type and frequency of exercise the user performs daily and propose a corresponding calorie intake. It can also propose an optimal meal plan after the user has performed a specific type of exercise. Furthermore, the suggestion unit can propose meal plans that are effective for muscle building or body fat reduction, depending on the user's exercise goals. In this way, the suggestion unit can support a healthier lifestyle by proposing meal plans that take into account the user's activity level.

[0093] The instruction system can provide detailed cooking instructions via audio or video, tailoring the guidance to the user's cooking skill level. For example, it can explain basic cooking procedures in detail for beginners and suggest efficient cooking methods for advanced users. It can also refer to the user's past cooking experience and select the most appropriate instruction method based on that experience. Furthermore, it can suggest recipes of varying difficulty levels depending on the user's cooking skill level. This allows the instruction system to provide more effective cooking support by tailoring guidance to the user's skill level.

[0094] The reception desk can estimate the user's emotions and customize the food photo loading interface based on those emotions. For example, if the user is stressed, it can provide a simple and intuitive interface. If the user is relaxed, it can provide an interface with more detailed options. Furthermore, if the user is busy, it can provide a quick and easy interface. In this way, the reception desk can provide a more comfortable user experience by customizing the interface according to the user's emotions.

[0095] The reception desk can analyze the user's past meal photo history and consider the user's eating patterns when selecting the optimal loading method. For example, if the reception desk tends to eat at a specific time of day, it can suggest the most suitable loading method for that time slot. Similarly, if the reception desk tends to use a specific ingredient on a particular day of the week, it can suggest a loading method related to that ingredient. Furthermore, the reception desk can analyze the user's eating patterns and select the most efficient loading method. This allows the reception desk to provide an optimal loading method that takes the user's eating patterns into account, resulting in a smoother user experience.

[0096] The reception system can refer to the user's medical data when filtering food photos based on the user's current health status and dietary restrictions. For example, the reception system can filter food photos that correspond to specific allergies or diseases based on the user's medical data. It can also exclude food photos containing specific nutrients if the user needs to restrict those nutrients, based on the user's medical data. Furthermore, the reception system can refer to the user's medical data to select the most appropriate food photos based on their health condition. This allows the reception system to provide more appropriate food photos by utilizing the user's medical data.

[0097] The reception desk can estimate the user's emotions and, based on that estimated emotion, can refer to the user's past emotional data when determining the priority of food photos to load. For example, the reception desk can prioritize loading food photos that the user preferred when they were relaxed in the past. It can also exclude food photos that the user avoided when they were stressed in the past. Furthermore, the reception desk can analyze the user's past emotional data and select food photos that are best suited to their current emotions. This allows the reception desk to leverage the user's past emotional data to provide more appropriate food photos.

[0098] When loading food photos, the reception system can consider the user's travel history when prioritizing the loading of highly relevant photos based on the user's geographical location. For example, the reception system can prioritize loading food photos related to ingredients and dishes from regions the user has visited in the past. Furthermore, if the user frequently visits a particular region, it can suggest recipes using ingredients from that region. In addition, the reception system can analyze the user's travel history and select the most suitable food photos based on their eating patterns during their travels. This allows the reception system to leverage the user's travel history to provide more personalized food photos.

[0099] The reception system can analyze the user's social media activity when loading food photos and consider the reactions of the user's followers when loading relevant photos. For example, the reception system can analyze the reactions of followers to food photos shared by the user on social media and prioritize loading popular food photos. It can also suggest food photos that the user's followers prefer. Furthermore, the reception system can analyze the user's social media activity and select the most suitable food photos based on follower reactions. This allows the reception system to leverage the user's social media activity and follower reactions to provide more relevant food photos.

[0100] The following briefly describes the processing flow for example form 2.

[0101] Step 1: The reception desk receives photos of the meal from the user. The user takes photos of their meal using a smartphone or digital camera and uploads the photos in JPEG or PNG format to the reception desk. Step 2: The analysis unit analyzes the photos read by the reception unit to identify the types of ingredients and dishes. The analysis unit uses image recognition algorithms and machine learning models to identify the types of ingredients and dishes contained in the photos. The analysis unit performs analysis based on a list of ingredients and the category of dishes. Step 3: The suggestion unit proposes the optimal recipe based on the information obtained by the analysis unit. The suggestion unit considers the user's preferences and nutritional balance, and provides suggestions including a list of ingredients and cooking procedures for the recipe. Step 4: The instruction team provides audio and video instructions for the specific cooking steps of the recipe proposed by the suggestion team. The instruction team uses audio guides and video tutorials to teach users the cooking steps.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0104] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0105] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0106] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0107] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0112] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0113] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0114] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0115] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0116] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0121] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0122] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0123] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0125] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0126] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0128] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0129] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0130] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0131] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0132] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0137] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0138] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0139] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0140] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0141] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0142] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0144] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0145] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0146] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0147] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0148] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0149] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0151] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0153] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0155] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0156] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0157] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0158] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0159] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0160] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0161] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0162] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0163] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0164] 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.

[0165] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0166] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0167] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0168] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0169] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0170] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0171] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0172] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0173] [Explanation of symbols]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk scans the photos of the meals, An analysis unit analyzes the photograph read by the aforementioned reception unit to identify the type of ingredients or dish, Based on the information obtained by the analysis unit, a proposal unit proposes a recipe, The instruction unit provides audio or video instructions for the specific cooking procedures of the recipe proposed by the aforementioned proposal unit. Equipped with A system characterized by the following features.

2. The aforementioned analysis unit, Analyze calories or nutrients from a photo of a meal. The system according to feature 1.

3. The aforementioned proposal section is, Based on the calorie or nutritional information obtained by the analysis unit, a meal menu is proposed. The system according to feature 1.

4. The aforementioned leadership, Instructions for specific cooking steps are provided via audio or video. The system according to feature 1.

5. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of loading food photos based on those emotions. The system according to feature 1.

6. The aforementioned reception unit is The system analyzes the user's past meal photo history and selects the appropriate loading method. The system according to feature 1.

7. The aforementioned reception unit is When loading food photos, the system filters them based on the user's current health status or dietary restrictions. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and determines the priority of food photos to load based on the estimated user emotions. The system according to feature 1.

Images