System

Abstract

An object of a system according to an embodiment is to quickly and accurately determine an allergic component contained in a foodstuff and provide the allergic component to a user.SOLUTION: A system includes an image analysis unit, an allergy determination unit, and an information providing unit. The image analysis unit analyzes the photographic image of the food ingredient. The allergy determination unit determines an allergy component based on the information of the food analyzed by the image analysis unit. The information providing unit provides a user with information on the allergy component determined by the allergy determination unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult to quickly and accurately identify allergens contained in food ingredients.

[0005] The system according to the embodiment aims to quickly and accurately determine allergens contained in food ingredients and provide the results to the user. [Means for solving the problem]

[0006] The system according to the embodiment includes an image analysis unit, an allergy determination unit, and an information provision unit. The image analysis unit analyzes photographic images of ingredients. The allergy determination unit determines allergens based on the information about the ingredients analyzed by the image analysis unit. The information provision unit provides the user with information about the allergens determined by the allergy determination unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and accurately determine allergens contained in food ingredients and provide the information to the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The allergy management system according to an embodiment of the present invention is a system that uses photographic images of ingredients to identify allergenic ingredients and provides the information to users, thereby enabling people with allergies to enjoy meals safely.

[0029] The allergy management system according to the embodiment includes an image analysis unit, an allergy determination unit, and an information provision unit. The image analysis unit analyzes a photographic image of an ingredient. For example, the generation AI identifies the type of ingredient using deep learning technology. The generation AI can also analyze the ingredients of an ingredient using computer vision technology. The generation AI can also determine whether an ingredient contains an allergen based on the photographic image of the ingredient. For example, the generation AI receives an input photographic image of an ingredient and analyzes whether the ingredient contains an allergen. The allergy determination unit determines the allergen based on the information about the ingredient analyzed by the image analysis unit. For example, the generation AI determines the allergen of an ingredient based on a pre-trained allergen database. The generation AI can also determine the allergen based on ingredient information about the ingredient. The generation AI can also determine the allergen based on the type and ingredients of the ingredient. For example, the generation AI receives ingredient information about an ingredient as input and determines the allergen. The information provision unit provides the user with information about the allergen determined by the allergy determination unit. For example, the generation AI provides the user with information about the allergen as a text message. The generation AI can also provide the user with information about the allergen as an in-app notification. The generation AI can also provide information about allergens to users via email. For example, the generation AI can generate a warning message based on the information about allergens and provide it to the user. This allows the allergy management system according to the embodiment to determine whether a food contains an allergen and provide the message to the user, allowing people with allergies to enjoy their meals safely.

[0030] The image analysis unit can also simultaneously evaluate the freshness or quality of ingredients and provide the results to the user. For example, when the generation AI analyzes a photograph of an ingredient, the image analysis unit introduces an algorithm to evaluate the freshness of the ingredient. For example, it analyzes changes in the color and shape of vegetables and quantifies freshness. In addition, to evaluate the quality of ingredients, the generation AI performs image analysis to detect quality deterioration such as scratches and discoloration. For example, it analyzes the presence or absence of scratches or mold on the surface of fruit. The image analysis unit also provides the user with the results of the generation AI's evaluation of the freshness and quality of the ingredients. For example, it displays a message such as "This tomato is fresh" or "This apple has a blemish." This allows the freshness and quality of ingredients to be evaluated and provided to the user, helping them select ingredients.

[0031] The image analysis unit can also identify the origin and producer information of ingredients and provide the results to the user. For example, the image analysis unit uses an algorithm in which the generation AI analyzes photographs of ingredients and identifies the origin of the ingredients. For example, it identifies distinctive ingredients produced in a specific region. In addition, to identify the producer information of ingredients, the generation AI performs image analysis and reads information written on the package or label. For example, it analyzes the producer's name and farm information. The image analysis unit also provides the user with the origin and producer information of ingredients identified by the generation AI. For example, it displays a message such as "This vegetable is from Hokkaido" or "This fruit was produced at XX farm." This identifies the origin and producer information of ingredients and provides it to the user, helping them select ingredients.

[0032] The image analysis unit can analyze not only photos of ingredients but also videos to evaluate the cooking process and how ingredients are used, and provide the results to the user. For example, the image analysis unit uses an algorithm that allows the generative AI to analyze not only photos of ingredients but also videos. For example, it analyzes videos of cooking processes and evaluates how ingredients are used and the cooking method. The image analysis unit also uses video analysis to evaluate how ingredients are used and the cooking method, and provides the results to the user. For example, it displays messages such as "In this video, tomatoes are being sliced" or "This cooking method is suitable for stir-frying." The image analysis unit also analyzes videos using the generative AI, and provides the user with the results of its evaluation of the cooking process and how ingredients are used. For example, it displays messages such as "In this video, bell peppers are being finely chopped" or "This cooking method is suitable for stewing." In this way, by analyzing not only photos of ingredients but also videos, the cooking process and how ingredients are used can be evaluated, and provided to the user as a reference for cooking.

[0033] The image analysis unit can analyze multiple photos taken from different angles and provide three-dimensional information about ingredients. For example, the image analysis unit introduces an algorithm in which the generation AI analyzes multiple photos taken from different angles and generates three-dimensional information about ingredients. For example, it generates a 3D model. The image analysis unit also analyzes multiple photos and provides three-dimensional information about ingredients. For example, it displays a message such as, "This vegetable looks like this from this angle." The image analysis unit also analyzes multiple photos taken from different angles and provides three-dimensional information about ingredients to the user. For example, it displays a message such as, "This fruit looks like this from this angle." In this way, by analyzing multiple photos taken from different angles and providing three-dimensional information about ingredients, detailed information about ingredients can be provided to the user.

[0034] The generative AI can evaluate changes in allergenic components by taking into account the processing and cooking methods of ingredients. For example, when determining allergenic components, the generative AI introduces an algorithm that takes into account the processing method of ingredients. For example, it evaluates changes in allergenic components due to heating or freezing. The generative AI also analyzes information about the cooking process to evaluate changes in allergenic components by taking into account the cooking method. For example, it evaluates changes in allergenic components due to frying or stewing. The generative AI also evaluates changes in allergenic components by taking into account the processing and cooking methods of ingredients, and provides the results to the user. For example, it displays a message such as, "The allergenic components of this ingredient will decrease when heated." This allows for more accurate allergy information to be provided by taking into account the processing and cooking methods of ingredients and evaluating changes in allergenic components.

[0035] The generative AI can also evaluate the allergy risk of ingredient combinations and provide the results to the user. For example, when determining allergenic ingredients, the generative AI introduces an algorithm that evaluates the allergy risk of ingredient combinations. For example, it evaluates whether a specific ingredient combination will cause an allergic reaction. The generative AI also evaluates the allergy risk of ingredient combinations and provides the results to the user. For example, it displays a message such as, "Consuming this ingredient and this ingredient together increases the allergy risk." The generative AI also evaluates the allergy risk of ingredient combinations and provides the results to the user. For example, it displays a message such as, "This dish contains ingredients that pose an allergy risk." In this way, by evaluating the allergy risk of ingredient combinations and providing the results to the user, the allergy risk can be reduced.

[0036] The generative AI can simultaneously evaluate the nutritional value and health benefits of ingredients and provide the results to the user. For example, when determining whether an ingredient is allergenic, the generative AI introduces an algorithm to evaluate the nutritional value of ingredients. For example, it analyzes the vitamin and mineral content. The generative AI also analyzes ingredient information of ingredients to evaluate the health benefits of ingredients. For example, it evaluates antioxidant effects and immune-boosting effects. The generative AI also evaluates the nutritional value and health benefits of ingredients at the same time as determining whether an ingredient is allergenic, and provides the results to the user. For example, it displays a message such as "This ingredient is rich in vitamin C." This allows the generative AI to evaluate the nutritional value and health benefits of ingredients and provide the results to the user, helping them make better food selections.

[0037] The generation AI can evaluate changes in allergenic ingredients by taking into account the food storage method and expiration date. For example, the generation AI can incorporate an algorithm that takes into account the food storage method when determining allergenic ingredients. For example, it can evaluate changes in allergenic ingredients due to refrigerated or frozen storage. The generation AI can also analyze expiration date information to evaluate changes in allergenic ingredients by taking into account the food's expiration date. For example, it can evaluate the allergy risk of food ingredients that are close to their expiration date. The generation AI can also evaluate changes in allergenic ingredients by taking into account the food's storage method and expiration date, and provide the results to the user. For example, it can display a message such as, "The allergenic ingredients in this food will decrease if stored frozen." This allows the generation AI to provide more accurate allergy information by evaluating changes in allergenic ingredients by taking into account the food's storage method and expiration date.

[0038] When providing information about allergens, the generation AI can also provide detailed explanations of the allergens and countermeasures. For example, when providing information about allergens, the generation AI may provide a detailed explanation of the allergens. For example, it may display a message such as, "This ingredient contains peanuts. Peanuts may cause nut allergies." The generation AI may also analyze the allergen information to provide countermeasures. For example, it may display a message such as, "If you have a peanut allergy, avoid this ingredient or use an alternative." The generation AI may also provide a detailed explanation of the allergens and countermeasures, and provide the results to the user. For example, it may display a message such as, "This ingredient contains peanuts. Those with a peanut allergy should be careful." By providing detailed explanations of the allergens and countermeasures, the user can deepen their understanding of the allergens and take appropriate measures.

[0039] The generation AI can provide individually customized information by taking into account the user's past allergy history. For example, the generation AI can implement an algorithm that takes into account the user's past allergy history and provides individually customized information. For example, a user who has previously had a peanut allergy reaction can be warned to avoid foods containing peanuts. The generation AI can also provide individually customized information based on the user's allergy history. For example, it can display a message such as, "Because you have previously had a peanut allergy reaction, you should be careful with this food." The generation AI can also provide individually customized information by taking into account the user's past allergy history. For example, it can display a message such as, "This food contains peanuts. Please be careful as you have previously had a peanut allergy reaction." This allows the generation AI to provide individually customized information by taking into account the user's past allergy history.

[0040] When providing information about allergens, the generation AI can simultaneously suggest recipes and ingredients that do not contain allergens. For example, when providing information about allergens, the generation AI introduces an algorithm that suggests recipes that do not contain allergens. For example, it would suggest recipes that do not contain peanuts to a user with a peanut allergy. The generation AI also analyzes ingredient information to suggest ingredients that do not contain allergens. For example, it would suggest using almonds instead of peanuts. When providing information about allergens, the generation AI also suggests recipes and ingredients that do not contain allergens and provides the results to the user. For example, it displays a message such as, "This ingredient contains peanuts, but we recommend using almonds instead." This allows users to enjoy their meals safely by suggesting recipes and ingredients that do not contain allergens.

[0041] When providing information about allergens, the generation AI can also suggest restaurants that do not contain allergens. For example, when providing information about allergens, the generation AI introduces an algorithm that suggests restaurants that do not contain allergens. For example, for a user with a peanut allergy, the generation AI could suggest restaurants that do not contain peanuts. The generation AI also analyzes the menu information of the restaurant to suggest restaurants that do not contain allergens. For example, it could suggest restaurants that offer menus that do not contain peanuts. When providing information about allergens, the generation AI also suggests restaurants that do not contain allergens and provides the results to the user. For example, it could display a message such as, "This ingredient contains peanuts, but we recommend a restaurant that does not contain peanuts." This allows users to enjoy eating out safely by suggesting restaurants that do not contain allergens.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The allergy management system can further include a nutritional evaluation unit that evaluates the nutritional value of ingredients. The nutritional evaluation unit analyzes photographic images of ingredients to identify the vitamin and mineral content. For example, the generative AI estimates the nutritional value of ingredients from their color and shape and provides the result to the user. The nutritional evaluation unit also evaluates the nutritional value of ingredients and provides the results to the user. For example, it can display a message such as "This vegetable is rich in vitamin C." This allows users to understand the nutritional value of ingredients and make healthy dietary choices.

[0044] The allergy management system can further include a storage suggestion unit that suggests food storage methods. The storage suggestion unit analyzes photographic images of food ingredients and identifies the optimal storage method. For example, the generation AI recommends refrigerating or freezing food based on the type and condition of the food ingredient. The storage suggestion unit also suggests food storage methods and provides the results to the user. For example, it can display a message such as "This vegetable is best stored refrigerated." This allows the user to store food ingredients appropriately and maintain their freshness.

[0045] The allergy management system can further include a cooking suggestion unit that suggests cooking methods for ingredients. The cooking suggestion unit analyzes photographic images of ingredients and identifies the optimal cooking method. For example, the generative AI recommends stir-frying or stewing based on the type and condition of the ingredients. The cooking suggestion unit also suggests cooking methods for ingredients and provides the results to the user. For example, it can display a message such as "This vegetable is suitable for stir-frying." This allows the user to cook ingredients in the optimal way and create delicious dishes.

[0046] The allergy management system can further include a nutritional balance evaluation unit that evaluates the nutritional balance of a combination of ingredients. The nutritional balance evaluation unit analyzes photographic images of ingredients and evaluates the nutritional balance of multiple ingredients. For example, the generation AI analyzes the nutritional value of ingredients and suggests a balanced meal. The nutritional balance evaluation unit also evaluates the nutritional balance of a combination of ingredients and provides the results to the user. For example, it can display a message such as, "This meal contains a balanced amount of vitamins and minerals." This allows the user to select a nutritionally balanced meal.

[0047] The allergy management system can further include an alternative ingredient suggestion unit that suggests alternative ingredients that do not contain the allergenic ingredients of the ingredient. The alternative ingredient suggestion unit analyzes photographic images of ingredients and identifies alternative ingredients that do not contain allergenic ingredients. For example, the generation AI suggests almonds as an alternative ingredient to a user with a peanut allergy. The alternative ingredient suggestion unit also suggests alternative ingredients that do not contain allergenic ingredients and provides the results to the user. For example, it can display a message such as, "This ingredient contains peanuts, but we recommend using almonds instead." This allows users to enjoy their meal while avoiding the allergenic ingredients.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The image analysis unit analyzes the photo of the food. For example, the generative AI can use deep learning technology to identify the type of food and computer vision technology to analyze the ingredients. The generative AI can also determine whether the food contains allergens based on the photo of the food. Step 2: The allergy determination unit determines the allergens based on the information about the ingredients analyzed by the image analysis unit. For example, the generation AI determines the allergens of ingredients based on a database of allergens it has previously learned. It can also determine the allergens based on the ingredient information and type of ingredients. Step 3: The information provision unit provides the user with information about the allergens determined by the allergy determination unit. For example, the generation AI can provide the user with information about the allergens as a text message, an in-app notification, or an email. This allows the user to receive information about the allergens quickly.

[0050] (Example 2) The allergy management system according to an embodiment of the present invention is a system that uses photographic images of ingredients to identify allergenic ingredients and provides the information to users, thereby enabling people with allergies to enjoy meals safely.

[0051] The allergy management system according to the embodiment includes an image analysis unit, an allergy determination unit, and an information provision unit. The image analysis unit analyzes a photographic image of an ingredient. For example, the generation AI identifies the type of ingredient using deep learning technology. The generation AI can also analyze the ingredients of an ingredient using computer vision technology. The generation AI can also determine whether an ingredient contains an allergen based on the photographic image of the ingredient. For example, the generation AI receives an input photographic image of an ingredient and analyzes whether the ingredient contains an allergen. The allergy determination unit determines the allergen based on the information about the ingredient analyzed by the image analysis unit. For example, the generation AI determines the allergen of an ingredient based on a pre-trained allergen database. The generation AI can also determine the allergen based on ingredient information about the ingredient. The generation AI can also determine the allergen based on the type and ingredients of the ingredient. For example, the generation AI receives ingredient information about an ingredient as input and determines the allergen. The information provision unit provides the user with information about the allergen determined by the allergy determination unit. For example, the generation AI provides the user with information about the allergen as a text message. The generation AI can also provide the user with information about the allergen as an in-app notification. The generation AI can also provide information about allergens to users via email. For example, the generation AI can generate a warning message based on the information about allergens and provide it to the user. This allows the allergy management system according to the embodiment to determine whether a food contains an allergen and provide the message to the user, allowing people with allergies to enjoy their meals safely.

[0052] The image analysis unit can also simultaneously evaluate the freshness or quality of ingredients and provide the results to the user. For example, when the generation AI analyzes a photograph of an ingredient, the image analysis unit introduces an algorithm to evaluate the freshness of the ingredient. For example, it analyzes changes in the color and shape of vegetables and quantifies freshness. In addition, to evaluate the quality of ingredients, the generation AI performs image analysis to detect quality deterioration such as scratches and discoloration. For example, it analyzes the presence or absence of scratches or mold on the surface of fruit. The image analysis unit also provides the user with the results of the generation AI's evaluation of the freshness and quality of the ingredients. For example, it displays a message such as "This tomato is fresh" or "This apple has a blemish." This allows the freshness and quality of ingredients to be evaluated and provided to the user, helping them select ingredients.

[0053] The image analysis unit can also identify the origin and producer information of ingredients and provide the results to the user. For example, the image analysis unit uses an algorithm in which the generation AI analyzes photographs of ingredients and identifies the origin of the ingredients. For example, it identifies distinctive ingredients produced in a specific region. In addition, to identify the producer information of ingredients, the generation AI performs image analysis and reads information written on the package or label. For example, it analyzes the producer's name and farm information. The image analysis unit also provides the user with the origin and producer information of ingredients identified by the generation AI. For example, it displays a message such as "This vegetable is from Hokkaido" or "This fruit was produced at XX farm." This identifies the origin and producer information of ingredients and provides it to the user, helping them select ingredients.

[0054] The image analysis unit can use the emotion estimation function to analyze the emotions of users when they upload photos of ingredients and provide feedback to elicit positive emotions. For example, the image analysis unit uses the emotion estimation function to analyze facial expressions and voices when users upload photos of ingredients and estimate emotions. For example, it detects smiling faces and excited voices. The image analysis unit also provides feedback to elicit positive emotions based on the results of analyzing the user's emotions. For example, it displays messages such as "Great choice!" or "Looks delicious!" The image analysis unit also uses the emotion estimation function to analyze the emotions of users when they upload photos of ingredients in real time and makes suggestions to elicit positive emotions. For example, it makes suggestions such as "Why not try a new recipe with this ingredient?" In this way, the image analysis unit analyzes the user's emotions and provides feedback to elicit positive emotions, thereby improving user satisfaction.

[0055] The image analysis unit can analyze not only photos of ingredients but also videos to evaluate the cooking process and how ingredients are used, and provide the results to the user. For example, the image analysis unit uses an algorithm that allows the generative AI to analyze not only photos of ingredients but also videos. For example, it analyzes videos of cooking processes and evaluates how ingredients are used and the cooking method. The image analysis unit also uses video analysis to evaluate how ingredients are used and the cooking method, and provides the results to the user. For example, it displays messages such as "In this video, tomatoes are being sliced" or "This cooking method is suitable for stir-frying." The image analysis unit also analyzes videos using the generative AI, and provides the user with the results of its evaluation of the cooking process and how ingredients are used. For example, it displays messages such as "In this video, bell peppers are being finely chopped" or "This cooking method is suitable for stewing." In this way, by analyzing not only photos of ingredients but also videos, the cooking process and how ingredients are used can be evaluated, and provided to the user as a reference for cooking.

[0056] The image analysis unit can analyze multiple photos taken from different angles and provide three-dimensional information about ingredients. For example, the image analysis unit introduces an algorithm in which the generation AI analyzes multiple photos taken from different angles and generates three-dimensional information about ingredients. For example, it generates a 3D model. The image analysis unit also analyzes multiple photos and provides three-dimensional information about ingredients. For example, it displays a message such as, "This vegetable looks like this from this angle." The image analysis unit also analyzes multiple photos taken from different angles and provides three-dimensional information about ingredients to the user. For example, it displays a message such as, "This fruit looks like this from this angle." In this way, by analyzing multiple photos taken from different angles and providing three-dimensional information about ingredients, detailed information about ingredients can be provided to the user.

[0057] The image analysis unit can use the emotion estimation function to analyze the emotion a user expresses when uploading photos of ingredients and make suggestions to reduce negative emotions. For example, the image analysis unit uses the emotion estimation function to analyze the user's facial expression and voice when uploading photos of ingredients and estimate negative emotions. For example, it detects sad expressions and depressed voices. The image analysis unit also makes suggestions to reduce negative emotions based on the results of analyzing the user's emotions. For example, it displays messages such as "Let's make a delicious dish with these ingredients!" or "Why not try a new recipe?" The image analysis unit also uses the emotion estimation function to analyze the user's emotions in real time when uploading photos of ingredients and makes suggestions to reduce negative emotions. For example, it makes a suggestion such as "Let's have a fun cooking experience with these ingredients!" In this way, by analyzing the user's emotions and making suggestions to reduce negative emotions, user satisfaction is improved.

[0058] The generative AI can evaluate changes in allergenic components by taking into account the processing and cooking methods of ingredients. For example, when determining allergenic components, the generative AI introduces an algorithm that takes into account the processing method of ingredients. For example, it evaluates changes in allergenic components due to heating or freezing. The generative AI also analyzes information about the cooking process to evaluate changes in allergenic components by taking into account the cooking method. For example, it evaluates changes in allergenic components due to frying or stewing. The generative AI also evaluates changes in allergenic components by taking into account the processing and cooking methods of ingredients, and provides the results to the user. For example, it displays a message such as, "The allergenic components of this ingredient will decrease when heated." This allows for more accurate allergy information to be provided by taking into account the processing and cooking methods of ingredients and evaluating changes in allergenic components.

[0059] The generative AI can also evaluate the allergy risk of ingredient combinations and provide the results to the user. For example, when determining allergenic ingredients, the generative AI introduces an algorithm that evaluates the allergy risk of ingredient combinations. For example, it evaluates whether a specific ingredient combination will cause an allergic reaction. The generative AI also evaluates the allergy risk of ingredient combinations and provides the results to the user. For example, it displays a message such as, "Consuming this ingredient and this ingredient together increases the allergy risk." The generative AI also evaluates the allergy risk of ingredient combinations and provides the results to the user. For example, it displays a message such as, "This dish contains ingredients that pose an allergy risk." In this way, by evaluating the allergy risk of ingredient combinations and providing the results to the user, the allergy risk can be reduced.

[0060] The generation AI can use its emotion estimation function to analyze the emotions of the user when receiving the allergy ingredient determination results and provide feedback to provide a sense of security. For example, the generation AI uses its emotion estimation function to analyze the facial expressions and voice of the user when receiving the allergy ingredient determination results and estimate the emotions. For example, it can detect anxious facial expressions and voice. The generation AI then provides feedback to provide a sense of security based on the results of analyzing the user's emotions. For example, it can display a message such as, "Don't worry, this ingredient is safe." The generation AI can also use its emotion estimation function to analyze the emotions of the user when receiving the allergy ingredient determination results in real time and provide feedback to provide a sense of security. For example, it can display a message such as, "This ingredient does not contain any allergens, so please enjoy it with peace of mind." In this way, the generation AI can analyze the user's emotions and provide feedback to provide a sense of security, thereby reducing the user's anxiety.

[0061] The generative AI can simultaneously evaluate the nutritional value and health benefits of ingredients and provide the results to the user. For example, when determining whether an ingredient is allergenic, the generative AI introduces an algorithm to evaluate the nutritional value of ingredients. For example, it analyzes the vitamin and mineral content. The generative AI also analyzes ingredient information of ingredients to evaluate the health benefits of ingredients. For example, it evaluates antioxidant effects and immune-boosting effects. The generative AI also evaluates the nutritional value and health benefits of ingredients at the same time as determining whether an ingredient is allergenic, and provides the results to the user. For example, it displays a message such as "This ingredient is rich in vitamin C." This allows the generative AI to evaluate the nutritional value and health benefits of ingredients and provide the results to the user, helping them make better food selections.

[0062] The generation AI can evaluate changes in allergenic ingredients by taking into account the food storage method and expiration date. For example, the generation AI can incorporate an algorithm that takes into account the food storage method when determining allergenic ingredients. For example, it can evaluate changes in allergenic ingredients due to refrigerated or frozen storage. The generation AI can also analyze expiration date information to evaluate changes in allergenic ingredients by taking into account the food's expiration date. For example, it can evaluate the allergy risk of food ingredients that are close to their expiration date. The generation AI can also evaluate changes in allergenic ingredients by taking into account the food's storage method and expiration date, and provide the results to the user. For example, it can display a message such as, "The allergenic ingredients in this food will decrease if stored frozen." This allows the generation AI to provide more accurate allergy information by evaluating changes in allergenic ingredients by taking into account the food's storage method and expiration date.

[0063] The generation AI can use its emotion estimation function to analyze the emotions of users when they receive the allergy ingredient detection results and make suggestions to elicit positive emotions. For example, the generation AI uses its emotion estimation function to analyze the facial expressions and voice of users when they receive the allergy ingredient detection results and infer their emotions. For example, it can detect expressions and voices of joy or relief. Based on the results of analyzing the user's emotions, the generation AI can make suggestions to elicit positive emotions. For example, it can display a message such as, "This ingredient is safe, so please enjoy it with peace of mind." The generation AI can also use its emotion estimation function to analyze the emotions of users when they receive the allergy ingredient detection results in real time and make suggestions to elicit positive emotions. For example, it can display a message such as, "This ingredient does not contain any allergens, so please enjoy it with peace of mind." This analysis of the user's emotions and suggestions to elicit positive emotions improves user satisfaction.

[0064] When providing information about allergens, the generation AI can also provide detailed explanations of the allergens and countermeasures. For example, when providing information about allergens, the generation AI may provide a detailed explanation of the allergens. For example, it may display a message such as, "This ingredient contains peanuts. Peanuts may cause nut allergies." The generation AI may also analyze the allergen information to provide countermeasures. For example, it may display a message such as, "If you have a peanut allergy, avoid this ingredient or use an alternative." The generation AI may also provide a detailed explanation of the allergens and countermeasures, and provide the results to the user. For example, it may display a message such as, "This ingredient contains peanuts. Those with a peanut allergy should be careful." By providing detailed explanations of the allergens and countermeasures, the user can deepen their understanding of the allergens and take appropriate measures.

[0065] The generation AI can provide individually customized information by taking into account the user's past allergy history. For example, the generation AI can implement an algorithm that takes into account the user's past allergy history and provides individually customized information. For example, a user who has previously had a peanut allergy reaction can be warned to avoid foods containing peanuts. The generation AI can also provide individually customized information based on the user's allergy history. For example, it can display a message such as, "Because you have previously had a peanut allergy reaction, you should be careful with this food." The generation AI can also provide individually customized information by taking into account the user's past allergy history. For example, it can display a message such as, "This food contains peanuts. Please be careful as you have previously had a peanut allergy reaction." This allows the generation AI to provide individually customized information by taking into account the user's past allergy history.

[0066] The generation AI can use its emotion estimation function to analyze the emotions of users when they receive information about allergens and provide feedback to provide a sense of security. For example, the generation AI uses its emotion estimation function to analyze the facial expressions and voice of users when they receive information about allergens and estimate their emotions. For example, it can detect anxious facial expressions and voices. The generation AI then provides feedback to provide a sense of security based on the results of analyzing the user's emotions. For example, it can display a message such as, "Don't worry, this ingredient is safe." The generation AI can also use its emotion estimation function to analyze the emotions of users when they receive information about allergens in real time and provide feedback to provide a sense of security. For example, it can display a message such as, "This ingredient does not contain any allergens, so please enjoy it with peace of mind." In this way, the generation AI can analyze the user's emotions and provide feedback to provide a sense of security, thereby reducing the user's anxiety.

[0067] When providing information about allergens, the generation AI can simultaneously suggest recipes and ingredients that do not contain allergens. For example, when providing information about allergens, the generation AI introduces an algorithm that suggests recipes that do not contain allergens. For example, it would suggest recipes that do not contain peanuts to a user with a peanut allergy. The generation AI also analyzes ingredient information to suggest ingredients that do not contain allergens. For example, it would suggest using almonds instead of peanuts. When providing information about allergens, the generation AI also suggests recipes and ingredients that do not contain allergens and provides the results to the user. For example, it displays a message such as, "This ingredient contains peanuts, but we recommend using almonds instead." This allows users to enjoy their meals safely by suggesting recipes and ingredients that do not contain allergens.

[0068] When providing information about allergens, the generation AI can also suggest restaurants that do not contain allergens. For example, when providing information about allergens, the generation AI introduces an algorithm that suggests restaurants that do not contain allergens. For example, for a user with a peanut allergy, the generation AI could suggest restaurants that do not contain peanuts. The generation AI also analyzes the menu information of the restaurant to suggest restaurants that do not contain allergens. For example, it could suggest restaurants that offer menus that do not contain peanuts. When providing information about allergens, the generation AI also suggests restaurants that do not contain allergens and provides the results to the user. For example, it could display a message such as, "This ingredient contains peanuts, but we recommend a restaurant that does not contain peanuts." This allows users to enjoy eating out safely by suggesting restaurants that do not contain allergens.

[0069] The generation AI can use its emotion estimation function to analyze the emotions of users when they receive information about allergens and make suggestions to elicit positive emotions. For example, the generation AI uses its emotion estimation function to analyze the facial expressions and voice of users when they receive information about allergens and estimate their emotions. For example, it can detect expressions and voices of joy or relief. Based on the results of analyzing the user's emotions, the generation AI can make suggestions to elicit positive emotions. For example, it can display a message such as, "This ingredient is safe, so please enjoy it with peace of mind." The generation AI can also use its emotion estimation function to analyze the emotions of users when they receive information about allergens in real time and make suggestions to elicit positive emotions. For example, it can display a message such as, "This ingredient does not contain any allergens, so please enjoy it with peace of mind." This analysis of the user's emotions and suggestions to elicit positive emotions improves user satisfaction.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The allergy management system can further include a nutritional evaluation unit that evaluates the nutritional value of ingredients. The nutritional evaluation unit analyzes photographic images of ingredients to identify the vitamin and mineral content. For example, the generative AI estimates the nutritional value of ingredients from their color and shape and provides the result to the user. The nutritional evaluation unit also evaluates the nutritional value of ingredients and provides the results to the user. For example, it can display a message such as "This vegetable is rich in vitamin C." This allows users to understand the nutritional value of ingredients and make healthy dietary choices.

[0072] The allergy management system can further include a storage suggestion unit that suggests food storage methods. The storage suggestion unit analyzes photographic images of food ingredients and identifies the optimal storage method. For example, the generation AI recommends refrigerating or freezing food based on the type and condition of the food ingredient. The storage suggestion unit also suggests food storage methods and provides the results to the user. For example, it can display a message such as "This vegetable is best stored refrigerated." This allows the user to store food ingredients appropriately and maintain their freshness.

[0073] The allergy management system can further include a cooking suggestion unit that suggests cooking methods for ingredients. The cooking suggestion unit analyzes photographic images of ingredients and identifies the optimal cooking method. For example, the generative AI recommends stir-frying or stewing based on the type and condition of the ingredients. The cooking suggestion unit also suggests cooking methods for ingredients and provides the results to the user. For example, it can display a message such as "This vegetable is suitable for stir-frying." This allows the user to cook ingredients in the optimal way and create delicious dishes.

[0074] The allergy management system can further include a nutritional balance evaluation unit that evaluates the nutritional balance of a combination of ingredients. The nutritional balance evaluation unit analyzes photographic images of ingredients and evaluates the nutritional balance of multiple ingredients. For example, the generation AI analyzes the nutritional value of ingredients and suggests a balanced meal. The nutritional balance evaluation unit also evaluates the nutritional balance of a combination of ingredients and provides the results to the user. For example, it can display a message such as, "This meal contains a balanced amount of vitamins and minerals." This allows the user to select a nutritionally balanced meal.

[0075] The allergy management system can further include an alternative ingredient suggestion unit that suggests alternative ingredients that do not contain the allergenic ingredients of the ingredient. The alternative ingredient suggestion unit analyzes photographic images of ingredients and identifies alternative ingredients that do not contain allergenic ingredients. For example, the generation AI suggests almonds as an alternative ingredient to a user with a peanut allergy. The alternative ingredient suggestion unit also suggests alternative ingredients that do not contain allergenic ingredients and provides the results to the user. For example, it can display a message such as, "This ingredient contains peanuts, but we recommend using almonds instead." This allows users to enjoy their meal while avoiding the allergenic ingredients.

[0076] The allergy management system may further include an emotion feedback unit that analyzes the user's emotions and provides feedback to elicit positive emotions. The emotion feedback unit analyzes the user's facial expressions and voice when uploading photos of ingredients to estimate the user's emotions. For example, it detects smiling faces and excited voices. The emotion feedback unit also provides feedback to elicit positive emotions based on the results of analyzing the user's emotions. For example, it may display messages such as "Great choice!" or "Looks delicious!" This can improve user satisfaction.

[0077] The allergy management system may further include an emotion reduction suggestion unit that analyzes the user's emotions and makes suggestions to reduce negative emotions. The emotion reduction suggestion unit analyzes the user's facial expressions and voice when uploading photos of ingredients to estimate negative emotions. For example, it may detect a sad facial expression or a depressed voice. The emotion reduction suggestion unit also makes suggestions to reduce negative emotions based on the results of analyzing the user's emotions. For example, it may display messages such as "Let's make a delicious dish with these ingredients!" or "Why not try a new recipe?" This can improve user satisfaction.

[0078] The allergy management system may further include an emotion reassurance feedback unit that analyzes the user's emotions and provides feedback to provide a sense of security. The emotion reassurance feedback unit analyzes the user's facial expressions and voice when receiving the allergy ingredient determination results to estimate the user's emotions. For example, it detects anxious facial expressions and voice. The emotion reassurance feedback unit also provides feedback to provide a sense of security based on the results of analyzing the user's emotions. For example, it may display a message such as "Don't worry, this ingredient is safe." This reduces the user's anxiety and allows them to enjoy their meal with peace of mind.

[0079] The allergy management system may further include an emotion positive suggestion unit that analyzes the user's emotions and makes suggestions to elicit positive emotions. The emotion positive suggestion unit analyzes the user's facial expressions and voice when receiving the allergy ingredient determination results to estimate the user's emotions. For example, it detects facial expressions and voices of joy or relief. The emotion positive suggestion unit also makes suggestions to elicit positive emotions based on the results of the analysis of the user's emotions. For example, it may display a message such as, "This ingredient is safe, so please eat it with peace of mind." This can improve user satisfaction.

[0080] The allergy management system may further include an emotion positive suggestion unit that analyzes the user's emotions and makes suggestions to elicit positive emotions. The emotion positive suggestion unit analyzes the user's facial expressions and voice when receiving information about allergens to estimate the user's emotions. For example, it detects expressions and voices of joy or relief. The emotion positive suggestion unit also makes suggestions to elicit positive emotions based on the results of analyzing the user's emotions. For example, it may display a message such as, "This ingredient is safe, so please eat it with confidence." This can improve user satisfaction.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The image analysis unit analyzes the photo of the food. For example, the generative AI can use deep learning technology to identify the type of food and computer vision technology to analyze the ingredients. The generative AI can also determine whether the food contains allergens based on the photo of the food. Step 2: The allergy determination unit determines the allergens based on the information about the ingredients analyzed by the image analysis unit. For example, the generation AI determines the allergens of ingredients based on a database of allergens it has previously learned. It can also determine the allergens based on the ingredient information and type of ingredients. Step 3: The information provision unit provides the user with information about the allergens determined by the allergy determination unit. For example, the generation AI can provide the user with information about the allergens as a text message, an in-app notification, or an email. This allows the user to receive information about the allergens quickly.

[0083] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0089] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0090] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0091] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0092] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0093] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0094] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0097] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0124] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0133] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0134] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0135] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0136] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0137] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0138] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0139] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0140] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0141] 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.

[0142] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0143] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0144] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0145] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0146] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0147] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an image analysis unit that analyzes photographic images of ingredients; an allergy determination unit that determines allergens based on the information on ingredients analyzed by the image analysis unit; an information providing unit that provides a user with information about the allergy component determined by the allergy determining unit; A system characterized by:

2. The image analysis unit The freshness or quality of the food material is also evaluated at the same time, and the result is provided to the user.

2. The system of claim 1.

3. The image analysis unit The origin and producer information of the ingredients are also identified, and the results are provided to the user.

2. The system of claim 1.

4. The image analysis unit Analyze the emotions of users when they upload photos of ingredients and provide feedback to elicit positive emotions.

2. The system of claim 1.

5. The image analysis unit By analyzing not only the photographic images of the ingredients but also the videos, the cooking process and how the ingredients are used are evaluated, and the results are provided to the user.

2. The system of claim 1.

Images