system

Abstract

The system according to this embodiment aims to manage the user's allergy information and information on additives to be avoided, and to display appropriate warnings. [Solution] The system according to the embodiment comprises a registration unit, a provision unit, a matching unit, an analysis unit, and a warning unit. The registration unit registers the user's allergy information or information on additives to be avoided. The provision unit receives ingredient information from at least one food provider, such as a nursery school or a restaurant. The matching unit matches the information registered by the registration unit with the ingredient information provided by the provision unit. The analysis unit analyzes a photograph of the product taken by the user. The warning unit displays a warning based on the ingredient information analyzed by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, information management for avoiding specific allergies and additives is not sufficiently performed, and there is a possibility of oversight.

[0005] The system according to the embodiment aims to manage the allergy information of the user and the information of additives to be avoided, and display appropriate warnings.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a registration unit, a supply unit, a matching unit, an analysis unit, and a warning unit. The registration unit registers the user's allergy information or information on additives to be avoided. The supply unit receives ingredient information from at least one food provider, such as a nursery school or a restaurant. The matching unit matches the information registered by the registration unit with the ingredient information provided by the supply unit. The analysis unit analyzes a photograph of the product taken by the user. The warning unit displays a warning based on the ingredient information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can manage the user's allergy information and information on additives to be avoided, and display appropriate warnings. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The allergy and additive management system according to an embodiment of the present invention is a system that registers a user's allergy information and information on additives to be avoided, compares this information with ingredient information provided by food providers such as daycare centers and restaurants, and displays a warning. This system begins with the user registering their own or their child's allergy information and information on additives to be avoided. Next, it collaborates with food providers such as daycare centers and restaurants to share ingredient information of the meals they provide. Furthermore, when a user purchases a product at a store, they can take a picture of the product, which is then used to analyze the product's ingredient information and display a warning if it matches the user's NG list. This service allows users to avoid foods containing allergens or additives they or their children are allergic to, and to enjoy meals with peace of mind. Food providers such as daycare centers and restaurants can also understand the user's allergy information and provide appropriate meals. For example, when a user registers their own or their child's allergy information and information on additives to be avoided, they can use a dedicated application. This application is designed to allow users to easily input information, and registration is completed simply by selecting allergy information and a list of additives. Food providers such as daycare centers and restaurants can also input ingredient information of the meals they provide using a dedicated management system. This management system stores ingredient information in a database and has a function to match it with the user's allergy information. Furthermore, when a user purchases a product in a store, they can take a picture of the product using their smartphone camera. This picture is analyzed by the system, and the product's ingredient information is extracted. The system then matches the extracted ingredient information with the user's "NG list" (things to avoid) and displays a warning if it contains allergens or additives that the user wants to avoid. For example, if a user wants to avoid a specific preservative, the system can detect products containing that preservative and display a warning. This allows users to choose products with peace of mind. In this way, the allergy and additive management system can prevent allergies and additives from being overlooked by registering the user's allergy information and information on additives to avoid, matching it with the provided ingredient information, and displaying warnings.

[0029] The allergy and additive management system according to this embodiment comprises a registration unit, a provision unit, a matching unit, an analysis unit, and a warning unit. The registration unit registers the user's allergy information and information on additives to be avoided. The user's allergy information includes, but is not limited to, food allergies and drug allergies. The registration unit allows, for example, the user to input allergy information using a dedicated application. The registration unit also allows the user to input information on additives to be avoided. This includes, for example, additives such as preservatives and colorings. The provision unit inputs ingredient information of meals provided by food providers such as daycare centers and restaurants. The ingredient information includes, for example, a list of ingredients and their content. The provision unit allows, for example, the food provider to input ingredient information using a dedicated management system. The matching unit matches the information registered by the registration unit with the ingredient information provided by the provision unit. The matching is performed by, for example, an exact match or a partial match, but is not limited to such a method. The matching unit can, for example, store the user's allergy information and ingredient information in a database and perform the matching. The analysis unit analyzes product photos taken by the user and extracts ingredient information. The analysis is performed using, for example, an image analysis algorithm, but is not limited to such an example. The analysis unit can, for example, analyze product photos taken with a smartphone camera and extract ingredient information. The warning unit displays a warning based on the ingredient information analyzed by the analysis unit if there are any items that match the user's NG list. The warning is given by, for example, text display or audio alert, but is not limited to such an example. The warning unit can, for example, display a warning on the user's smartphone. As a result, the allergy and additive management system according to the embodiment can prevent overlooking allergies and additives by registering the user's allergy information and information on additives to be avoided, comparing it with the provided ingredient information, and displaying a warning.

[0030] The registration unit can analyze the user's past allergy reaction history and suggest appropriate input fields. For example, the registration unit can automatically display allergens the user has reacted to in the past as candidates. It can also suggest relevant additives based on the user's past allergy reaction history. Furthermore, the registration unit can analyze the user's past allergy reaction history and suggest input fields appropriate for specific seasons or situations. This allows users to efficiently input information by suggesting optimal input fields based on their past allergy reaction history. Allergy reaction history is collected based on data such as past medical records and self-reports. Medical records are records of allergy tests and treatments the user has received in the past, while self-reports are data in which the user reports their own allergy reactions. This allows the registration unit to understand the user's past allergy reaction history in detail and suggest appropriate input fields. Furthermore, the registration unit can analyze the user's allergy reaction history using machine learning algorithms. For example, it can cluster the user's allergy reaction history using algorithms such as K-means or hierarchical clustering to identify relevant input fields. This allows the registration unit to suggest optimal input fields based on the user's allergy reaction history.

[0031] The registration unit can filter information based on the user's current health status when they input allergy information or additives they wish to avoid. For example, if a user is currently unwell, the registration unit will prompt them to prioritize inputting specific allergy items. Furthermore, if the user inputs the results of a health checkup, the registration unit can suggest additives to avoid based on those results. In addition, the registration unit can input information about medications the user is currently taking and filter additives that may interact with those medications. This allows for more appropriate information input by filtering according to the user's health status. Current health status is assessed based on data such as body temperature, blood pressure, and the presence or absence of allergy symptoms. Body temperature can be measured by the user using a thermometer and entered into the registration unit. Blood pressure can be measured by the user using a blood pressure monitor and entered into the registration unit. The presence or absence of allergy symptoms is data reported by the user about their own symptoms. This allows the registration unit to gain a detailed understanding of the user's current health status and select appropriate information. Furthermore, the registration unit can extract frequently occurring keywords and prioritize the display of information related to the user's health status. For example, based on frequently occurring keywords, relevant allergy and additive information can be extracted and presented to the user. This allows the registration unit to select appropriate information based on the user's health status and support data entry.

[0032] The registration system can prioritize inputting highly relevant information when users enter allergy information or additives they wish to avoid, taking into account their geographical location. For example, if a user is in a specific region, the system can prompt them to prioritize inputting common allergens in that region. It can also prompt users to input common additive information in their travel destination if they are traveling. Furthermore, if a user is in a specific store, the system can prompt them to prioritize inputting additive information likely to be used in that store. This allows the system to prioritize inputting highly relevant information by considering geographical location. Geographical location information is obtained, for example, from GPS data or address information. GPS data is location information obtained from the user's smartphone or other devices, while address information is address data entered by the user. This allows the registration system to understand the user's geographical location in detail and provide appropriate information. Additionally, the registration system can extract common elements across different clusters and prioritize the display of highly relevant information. For example, it can extract and present relevant allergy and additive information based on high-frequency elements or common concepts. This allows the registration unit to select and support the input of appropriate information based on the user's geographical location.

[0033] The registration section can analyze a user's social media activity and input relevant information when they enter allergy information or additives they wish to avoid. For example, the registration section can analyze photos of meals shared by the user on social media and prompt them to enter allergy information. It can also suggest additives to avoid based on information from health-related accounts the user follows on social media. Furthermore, the registration section can prompt users to enter relevant allergy information based on information from groups they participate in on social media. This allows for efficient input of relevant information by analyzing social media activity. Social media activity is analyzed based on, for example, the content of posts, the number of likes, and follower reactions. The content of posts is data such as text, images, and videos posted by the user on social media, and the number of likes is data showing the reactions of other users to the user's posts. Follower reactions are data such as comments and shares on the user's posts. This allows the registration section to understand the user's social media activity in detail and provide relevant information. Furthermore, the registration section can identify the inventor's areas of interest and prioritize the display of relevant allergy and additive information. For example, based on survey results and past research themes, the system identifies the inventor's areas of interest and extracts relevant information. This allows the registration unit to select appropriate information based on the user's social media activity and support input.

[0034] The information provider can select an appropriate method of providing ingredient information based on past provision history. For example, the provider can prioritize the method of providing ingredient information that the user has preferred in the past. The provider can also select the most effective method based on the user's past provision history. Furthermore, the provider can analyze the user's past provision history and select a method appropriate to specific situations. This allows users to efficiently receive information by selecting the optimal method based on past provision history. Provision history is collected based on data such as past provision dates and content. The provision date is a record of when the ingredient information was provided, and the content is the details of the provided ingredient information. This allows the provider to understand the user's past provision history in detail and select an appropriate method. Furthermore, the provider can analyze metadata to optimize the method of providing ingredient information. For example, it can adjust the method of providing ingredient information based on metadata such as tag information and attribute data. This allows the provider to provide appropriate ingredient information based on the user's past provision history and metadata.

[0035] The information provider can select information based on the food provider's current menu when providing ingredient information. For example, the provider can prioritize providing relevant ingredient information based on the food provider's current menu. The provider can also update ingredient information in real time in response to changes in the food provider's menu. Furthermore, the provider can filter and provide ingredient information related to specific menu items of the food provider. This allows for the provision of highly relevant ingredient information by filtering based on the current menu. The current menu is obtained from data such as menu lists and ingredient tables. A menu list is a list of dishes and drinks offered by the food provider, and an ingredient table is a detailed list of the ingredients contained in each dish and drink. This allows the provider to have a detailed understanding of the food provider's current menu and provide appropriate ingredient information. Furthermore, the provider can extract highly innovative elements and optimize the method of providing ingredient information. For example, it can identify highly innovative elements based on patent search results and technology evaluations, and adjust the method of providing ingredient information. This allows the provider to provide appropriate ingredient information based on the food provider's current menu.

[0036] The data provider can prioritize providing highly relevant information based on the food provider's geographical location when providing ingredient information. For example, if a food provider is in a specific region, the data provider will prioritize providing ingredient information common in that region. Furthermore, if a food provider is traveling, the data provider can provide ingredient information common in their destination region. Additionally, if a food provider is at a specific store, the data provider can prioritize providing ingredient information likely to be used at that store. This allows the data provider to prioritize providing highly relevant ingredient information by considering geographical location. Geographical location information is obtained, for example, from GPS data or address information. GPS data is location information obtained from the food provider's device, and address information is address data entered by the food provider. This allows the data provider to have a detailed understanding of the food provider's geographical location and provide appropriate ingredient information. Furthermore, the data provider can optimize the method of providing ingredient information using supervised learning. For example, it can adjust the method of providing ingredient information using labeled datasets or training processes. This allows the data provider to provide appropriate ingredient information based on the food provider's geographical location.

[0037] The information provider can analyze the social media activities of food providers and provide relevant information when providing ingredient information. For example, the provider can analyze photos of menus shared by food providers on social media and provide ingredient information. The provider can also provide ingredient information based on information about health-related accounts that food providers follow on social media. Furthermore, the provider can provide relevant ingredient information based on information about groups that food providers participate in on social media. This allows for the efficient provision of relevant ingredient information by analyzing social media activities. Social media activities are analyzed based on, for example, the content of posts, the number of likes, and follower reactions. The content of posts is data such as text, images, and videos posted by food providers on social media, and the number of likes is data showing the reactions of other users to the food provider's posts. Follower reactions are data such as comments and shares on the food provider's posts. This allows the provider to understand the food provider's social media activities in detail and provide relevant information. Furthermore, the provider can analyze evolutionary patterns and optimize the method of providing ingredient information. For example, the method of providing ingredient information will be adjusted based on changes over time and the stage of technological advancement. This will allow the service provider to provide appropriate ingredient information based on the social media activities of food providers.

[0038] The matching unit can improve the accuracy of the matching process based on the interrelationship between allergy information and ingredient information. For example, the matching unit analyzes the interrelationship between allergy information and ingredient information to improve matching accuracy. The matching unit can also prioritize matching specific allergy items by considering the interrelationship between allergy information and ingredient information. Furthermore, the matching unit can adjust the matching accuracy in real time based on the interrelationship between allergy information and ingredient information. This improves matching accuracy by considering the interrelationship between allergy information and ingredient information. The interrelationship is evaluated based on, for example, the interaction of allergy components and the combination of components. The interaction of allergy components is data that shows how a specific allergy component reacts with other components, and the combination of components is data that shows the effect when multiple components are used together. This allows the matching unit to grasp the interrelationship between allergy information and ingredient information in detail and perform appropriate matching. Furthermore, the matching unit can also evaluate the quality of the summary and optimize the matching accuracy. For example, it adjusts the matching accuracy based on the accuracy of the summary and the comprehensiveness of the information. This allows the matching unit to perform appropriate matching based on the interrelationship between allergy information and ingredient information.

[0039] The matching unit can perform matching based on the user's past allergy reaction history. For example, the matching unit can improve the accuracy of matching based on the user's past allergy reaction history. The matching unit can also prioritize matching specific allergy items by considering the user's past allergy reaction history. Furthermore, the matching unit can analyze the user's past allergy reaction history and adjust the accuracy of matching in real time. This improves the accuracy of matching by considering past allergy reaction history. Allergy reaction history is collected based on data such as past medical records and self-reports. Medical records are records of allergy tests and treatments the user has received in the past, and self-reports are data in which the user reports their own allergy reactions. This allows the matching unit to have a detailed understanding of the user's past allergy reaction history and perform appropriate matching. Furthermore, the matching unit can clarify the specific methods and criteria for matching and optimize the accuracy of matching. For example, it can adjust the accuracy of matching based on methods such as exact match or partial match. This allows the matching unit to perform appropriate matching based on the user's past allergy reaction history.

[0040] The matching unit can perform matching based on the geographical distribution of component information during the matching process. For example, the matching unit can improve the accuracy of matching based on the geographical distribution of component information. The matching unit can also prioritize matching component information that is common in a particular region, taking into account the geographical distribution of component information. Furthermore, the matching unit can adjust the accuracy of matching in real time based on the geographical distribution of component information. This improves the accuracy of matching by considering the geographical distribution of component information. Geographical distribution is evaluated based on, for example, the distribution of components by region and the geographical bias of components. The distribution of components by region is a list of components commonly used in a particular region, and the geographical bias of components is data on components that are particularly frequently used in a particular region. This allows the matching unit to grasp the geographical distribution of component information in detail and perform appropriate matching. Furthermore, the matching unit can clarify the specific methods and criteria for matching and optimize the accuracy of matching. For example, it can adjust the accuracy of matching based on methods such as exact match or partial match. This allows the matching unit to perform appropriate matching based on the geographical distribution of component information.

[0041] The matching unit can improve the accuracy of the matching by referring to relevant literature on ingredient information during the matching process. For example, the matching unit improves the accuracy of the matching based on relevant literature on ingredient information. The matching unit can also prioritize matching specific allergy items by referring to relevant literature on ingredient information. Furthermore, the matching unit can adjust the accuracy of the matching in real time based on relevant literature on ingredient information. This improves the accuracy of the matching by referring to relevant literature. Relevant literature is evaluated based on, for example, academic papers and patent documents. Academic papers are documents that show the latest research results on ingredient information, and patent documents are documents that show technical details on ingredient information. This allows the matching unit to have a detailed understanding of relevant literature on ingredient information and perform appropriate matching. Furthermore, the matching unit can also clarify the specific methods and criteria for matching and optimize the accuracy of the matching. For example, it adjusts the accuracy of the matching based on methods such as algorithm improvements and data accuracy improvements. This allows the matching unit to perform appropriate matching based on relevant literature on ingredient information.

[0042] The analysis unit can change the level of detail of the analysis based on the importance of the product's ingredient information during photo analysis. For example, the analysis unit can perform a detailed analysis based on the importance of the product's ingredient information. The analysis unit can also prioritize the analysis of specific ingredient information, taking into account its importance. Furthermore, the analysis unit can adjust the accuracy of the analysis in real time based on the importance of the product's ingredient information. This allows for the prioritization of important information by adjusting the level of detail of the analysis based on the importance of the ingredient information. The importance of ingredient information is evaluated based on factors such as the presence or absence of allergens and the amount of each ingredient. The presence or absence of allergens is data indicating whether a specific allergen is present, and the amount of each ingredient is data indicating the amount of each ingredient. This allows the analysis unit to grasp the importance of the product's ingredient information in detail and perform an appropriate analysis. Furthermore, the analysis unit can optimize the accuracy of the analysis by clarifying specific methods and criteria for changing the level of detail of the analysis. For example, the level of detail of the analysis can be adjusted based on methods such as the depth of the analysis and the number of analysis items. This allows the analysis unit to perform an appropriate analysis based on the importance of the product's ingredient information.

[0043] The analysis unit can use different analysis algorithms depending on the product category when analyzing photographs. For example, the analysis unit can apply different analysis algorithms depending on the food category. It can also apply different analysis algorithms depending on the beverage category. Furthermore, it can apply different analysis algorithms depending on the seasoning category. This improves the accuracy of the analysis by applying the most suitable analysis algorithm for each product category. Product categories are evaluated based on classification methods such as food, beverages, and pharmaceuticals. The food category is a list of food-related products, the beverage category is a list of beverage-related products, and the pharmaceutical category is a list of medicine-related products. This allows the analysis unit to understand the product category in detail and apply an appropriate analysis algorithm. Furthermore, the analysis unit can also optimize the accuracy of the analysis by clarifying the specific type and implementation method of the analysis algorithm. For example, it can select an analysis algorithm based on methods such as image recognition algorithms and text analysis algorithms. This allows the analysis unit to perform appropriate analysis based on the product category.

[0044] The analysis unit can determine the order of analysis based on the product submission date during photo analysis. For example, the analysis unit can determine the priority of analysis based on the product submission date. The analysis unit can also prioritize the analysis of specific products, taking into account the product submission date. Furthermore, the analysis unit can adjust the analysis priority in real time based on the product submission date. This allows for the priority analysis of important information by determining the analysis priority based on the product submission date. The submission date is evaluated based on factors such as the submission date and time and the elapsed time since submission. The submission date and time is a record of the date and time the product was submitted, and the elapsed time since submission is data indicating the time since the product was submitted. This allows the analysis unit to grasp the product submission date in detail and perform appropriate analysis. Furthermore, the analysis unit can clarify specific methods and criteria for determining the order of analysis and optimize the accuracy of the analysis. For example, it can adjust the order of analysis based on methods such as priority based on submission date or priority based on urgency. This allows the analysis unit to perform appropriate analysis based on the product submission date.

[0045] The analysis unit can change the order of analysis based on the relevance of products during photo analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of products. The analysis unit can also prioritize the analysis of specific products, taking relevance into consideration. Furthermore, the analysis unit can adjust the order of analysis in real time based on product relevance. This allows for the prioritization of analysis of important information by adjusting the order of analysis based on product relevance. Product relevance is evaluated based on factors such as the commonality of ingredients and the similarity of uses. Commonality of ingredients is a list of ingredients that are commonly included in multiple products, and similarity of uses is data indicating whether multiple products are used for the same purpose. This allows the analysis unit to grasp the relevance of products in detail and perform appropriate analysis. Furthermore, the analysis unit can clarify specific methods and criteria for changing the order of analysis and optimize the accuracy of the analysis. For example, it can adjust the order of analysis based on methods such as sorting based on relevance or sorting based on importance. This allows the analysis unit to perform appropriate analysis based on product relevance.

[0046] The warning unit can change the level of detail of a warning based on the importance of the allergy information when displaying a warning. For example, the warning unit can display a detailed warning based on the importance of the allergy information. The warning unit can also prioritize the display of warnings for specific allergens, taking into account the importance of the allergy information. Furthermore, the warning unit can adjust the level of detail of the warning in real time based on the importance of the allergy information. This allows important information to be warned about preferentially by adjusting the level of detail of the warning based on the importance of the allergy information. The importance of allergy information is evaluated based on factors such as the severity of the allergic reaction and the amount of each component. The severity of the allergic reaction is data indicating the strength of the reaction to a specific allergen, and the amount of each component is data indicating the amount of each component. This allows the warning unit to grasp the importance of allergy information in detail and provide appropriate warnings. Furthermore, the warning unit can also optimize the accuracy of warnings by clarifying specific methods and criteria for changing the level of detail of the warning. For example, the level of detail of the warning can be adjusted based on methods such as the depth of the warning and the number of warning items. This allows the warning unit to provide appropriate warnings based on the importance of the allergy information.

[0047] The warning unit can use different warning algorithms depending on the product category when displaying a warning. For example, the warning unit can apply different warning algorithms depending on the food category. It can also apply different warning algorithms depending on the beverage category. Furthermore, it can apply different warning algorithms depending on the condiment category. This improves the accuracy of warnings by applying the most appropriate warning algorithm for each product category. Product categories are evaluated based on classification methods such as food, beverages, and pharmaceuticals. The food category is a list of food-related products, the beverage category is a list of beverage-related products, and the pharmaceutical category is a list of medicine-related products. This allows the warning unit to have a detailed understanding of product categories and apply appropriate warning algorithms. Furthermore, the warning unit can also optimize the accuracy of warnings by clarifying the specific types and implementation methods of warning algorithms. For example, warning algorithms can be selected based on methods such as risk assessment algorithms and alert generation algorithms. This allows the warning unit to provide appropriate warnings based on the product category.

[0048] The warning unit can determine the order of warnings based on the product submission date when displaying a warning. For example, the warning unit can determine the priority of warnings based on the product submission date. The warning unit can also prioritize the display of warnings for specific products, taking into account the product submission date. Furthermore, the warning unit can adjust the priority of warnings in real time based on the product submission date. This allows important information to be warned about preferentially by determining the priority of warnings based on the product submission date. The submission date is evaluated based on, for example, the submission date and time or the time elapsed since submission. The submission date and time is a record of the date and time the product was submitted, and the time elapsed since submission is data indicating the time since the product was submitted. This allows the warning unit to have a detailed understanding of the product submission date and issue appropriate warnings. Furthermore, the warning unit can clarify specific methods and criteria for determining the order of warnings and optimize the accuracy of warnings. For example, the order of warnings can be adjusted based on methods such as priority based on submission date or priority based on urgency. This allows the warning unit to issue appropriate warnings based on the product submission date.

[0049] The warning unit can change the order of warnings based on the relevance of the products when displaying a warning. For example, the warning unit adjusts the order of warnings based on the relevance of the products. The warning unit can also prioritize the display of warnings for specific products, taking into account their relevance. Furthermore, the warning unit can adjust the order of warnings in real time based on product relevance. This allows important information to be warned about preferentially by adjusting the order of warnings based on product relevance. Product relevance is evaluated based on factors such as the commonality of ingredients or the similarity of uses. Commonality of ingredients is a list of ingredients that are commonly included in multiple products, and similarity of uses is data indicating whether multiple products are used for the same purpose. This allows the warning unit to understand the relevance of products in detail and provide appropriate warnings. Furthermore, the warning unit can clarify specific methods and criteria for changing the order of warnings and optimize the accuracy of warnings. For example, it can adjust the order of warnings based on methods such as relevance-based sorting or importance-based sorting. This allows the warning unit to provide appropriate warnings based on product relevance.

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

[0051] The registration function can analyze a user's past eating history when they register allergy information and information about additives they wish to avoid, and automatically suggest relevant allergy and additive information. For example, based on the ingredient information of foods the user has eaten in the past, it can identify ingredients that may cause allergic reactions and display a warning during registration. It can also automatically list foods containing similar additives based on information about additives the user has avoided in the past, and prompt the user to register them. Furthermore, it can analyze seasonal allergy trends from the user's eating history and suggest foods and ingredients to avoid during specific seasons. This allows users to register more accurate allergy and additive information based on their past eating history.

[0052] The matching unit can improve the accuracy of matching based on the user's past allergy reaction history. For example, it can prioritize matching allergens that the user has reacted to in the past and display a warning. It can also analyze the user's past allergy reaction history and automatically filter foods containing specific ingredients. Furthermore, it can improve accuracy by setting matching criteria based on the user's allergy reaction history, taking past allergy reaction history into consideration. As a result, the accuracy of matching is improved, allowing users to choose foods with confidence.

[0053] The service provider can select ingredient information based on the current menu of the food provider. For example, it can prioritize providing relevant ingredient information based on the food provider's current menu. It can also update ingredient information in real time in response to changes in the food provider's menu. Furthermore, it can filter and provide ingredient information related to specific menu items of the food provider. This allows for the provision of highly relevant ingredient information by filtering based on the current menu. The current menu is obtained from data such as menu lists and ingredient tables. A menu list is a list of dishes and drinks offered by the food provider, and an ingredient table is a detailed list of the ingredients contained in each dish and drink. This allows the service provider to have a detailed understanding of the food provider's current menu and provide appropriate ingredient information.

[0054] The matching unit can perform matching based on the geographical distribution of component information. For example, it can improve the accuracy of matching based on the geographical distribution of component information. It can also prioritize matching component information that is common in a particular region, taking into account the geographical distribution of component information. Furthermore, it can adjust the accuracy of matching in real time based on the geographical distribution of component information. This improves the accuracy of matching by considering the geographical distribution of component information. Geographical distribution is evaluated based on, for example, the distribution of components by region and the geographical bias of components. The distribution of components by region is a list of components commonly used in a particular region, and the geographical bias of components is data on components that are used particularly often in a particular region. This allows the matching unit to grasp the geographical distribution of component information in detail and perform appropriate matching.

[0055] The analysis unit can change the level of detail of the analysis based on the importance of the product's ingredient information during photo analysis. For example, it can perform a detailed analysis based on the importance of the product's ingredient information. It can also prioritize the analysis of specific ingredient information, taking into account its importance. Furthermore, it can adjust the accuracy of the analysis in real time based on the importance of the product's ingredient information. This allows for the prioritization of important information by adjusting the level of detail of the analysis based on the importance of the ingredient information. The importance of ingredient information is evaluated based on factors such as the presence or absence of allergens and the amount of each ingredient. The presence or absence of allergens is data indicating whether a specific allergen is present, and the amount of each ingredient is data indicating the quantity of each ingredient. This allows the analysis unit to grasp the importance of the product's ingredient information in detail and perform an appropriate analysis.

[0056] The warning unit can change the level of detail of a warning based on the importance of the allergy information when a warning is displayed. For example, it can display a detailed warning based on the importance of the allergy information. It can also prioritize the display of warnings for specific allergens, taking into account the importance of the allergy information. Furthermore, it can adjust the level of detail of the warning in real time based on the importance of the allergy information. This allows important information to be warned about preferentially by adjusting the level of detail of the warning based on the importance of the allergy information. The importance of allergy information is evaluated based on factors such as the severity of the allergic reaction and the amount of each component. The severity of the allergic reaction is data indicating the strength of the reaction to a specific allergenic component, and the component amount is data indicating the amount of each component. This allows the warning unit to grasp the importance of allergy information in detail and provide appropriate warnings.

[0057] The service provider can analyze users' social media activity and provide relevant ingredient information. For example, it can analyze photos of meals shared by users on social media and prompt them to input allergy information. It can also suggest additives to avoid based on information from health-related accounts that users follow on social media. Furthermore, it can prompt users to input relevant allergy information based on information from groups they participate in on social media. This allows the service provider to efficiently provide relevant ingredient information by analyzing social media activity. Social media activity is analyzed based on, for example, the content of posts, the number of likes, and follower reactions. Post content refers to data such as text, images, and videos posted by users on social media, while the number of likes indicates data showing how other users react to a user's post. Follower reactions refer to data such as comments and shares on a user's post. This allows the service provider to understand users' social media activity in detail and provide relevant information.

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

[0059] Step 1: The registration section allows users to register their allergy information and information about additives they wish to avoid. Users can use a dedicated application to input information about food allergies, drug allergies, and additives such as preservatives and colorings. Step 2: The supply department inputs the ingredient information of meals provided by food service providers such as daycare centers and restaurants. This ingredient information includes a list of ingredients and their quantities. Food service providers can input this ingredient information using a dedicated management system. Step 3: The matching unit compares the information registered by the registration unit with the ingredient information provided by the provision unit. The matching is performed using methods such as exact match or partial match. The matching unit stores the user's allergy information and ingredient information in a database and performs the matching. Step 4: The analysis unit analyzes the product photos taken by the user and extracts the ingredient information. The analysis is performed using an image analysis algorithm. The analysis unit analyzes the product photos taken with the smartphone camera and extracts the ingredient information. Step 5: The warning unit displays a warning if any of the ingredients, based on the analysis information performed by the analysis unit, match the user's NG list. The warning is delivered via methods such as text display or audio alert. The warning unit displays the warning on the user's smartphone.

[0060] (Example of form 2) The allergy and additive management system according to an embodiment of the present invention is a system that registers a user's allergy information and information on additives to be avoided, compares this information with ingredient information provided by food providers such as daycare centers and restaurants, and displays a warning. This system begins with the user registering their own or their child's allergy information and information on additives to be avoided. Next, it collaborates with food providers such as daycare centers and restaurants to share ingredient information of the meals they provide. Furthermore, when a user purchases a product at a store, they can take a picture of the product, which is then used to analyze the product's ingredient information and display a warning if it matches the user's NG list. This service allows users to avoid foods containing allergens or additives they or their children are allergic to, and to enjoy meals with peace of mind. Food providers such as daycare centers and restaurants can also understand the user's allergy information and provide appropriate meals. For example, when a user registers their own or their child's allergy information and information on additives to be avoided, they can use a dedicated application. This application is designed to allow users to easily input information, and registration is completed simply by selecting allergy information and a list of additives. Food providers such as daycare centers and restaurants can also input ingredient information of the meals they provide using a dedicated management system. This management system stores ingredient information in a database and has a function to match it with the user's allergy information. Furthermore, when a user purchases a product in a store, they can take a picture of the product using their smartphone camera. This picture is analyzed by the system, and the product's ingredient information is extracted. The system then matches the extracted ingredient information with the user's "NG list" (things to avoid) and displays a warning if it contains allergens or additives that the user wants to avoid. For example, if a user wants to avoid a specific preservative, the system can detect products containing that preservative and display a warning. This allows users to choose products with peace of mind. In this way, the allergy and additive management system can prevent allergies and additives from being overlooked by registering the user's allergy information and information on additives to avoid, matching it with the provided ingredient information, and displaying warnings.

[0061] The allergy and additive management system according to this embodiment comprises a registration unit, a provision unit, a matching unit, an analysis unit, and a warning unit. The registration unit registers the user's allergy information and information on additives to be avoided. The user's allergy information includes, but is not limited to, food allergies and drug allergies. The registration unit allows, for example, the user to input allergy information using a dedicated application. The registration unit also allows the user to input information on additives to be avoided. This includes, for example, additives such as preservatives and colorings. The provision unit inputs ingredient information of meals provided by food providers such as daycare centers and restaurants. The ingredient information includes, for example, a list of ingredients and their content. The provision unit allows, for example, the food provider to input ingredient information using a dedicated management system. The matching unit matches the information registered by the registration unit with the ingredient information provided by the provision unit. The matching is performed by, for example, an exact match or a partial match, but is not limited to such a method. The matching unit can, for example, store the user's allergy information and ingredient information in a database and perform the matching. The analysis unit analyzes product photos taken by the user and extracts ingredient information. The analysis is performed using, for example, an image analysis algorithm, but is not limited to such an example. The analysis unit can, for example, analyze product photos taken with a smartphone camera and extract ingredient information. The warning unit displays a warning based on the ingredient information analyzed by the analysis unit if there are any items that match the user's NG list. The warning is given by, for example, text display or audio alert, but is not limited to such an example. The warning unit can, for example, display a warning on the user's smartphone. As a result, the allergy and additive management system according to the embodiment can prevent overlooking allergies and additives by registering the user's allergy information and information on additives to be avoided, comparing it with the provided ingredient information, and displaying a warning.

[0062] The registration unit can estimate the user's emotions and adjust the input method for allergy information or additives to avoid based on the estimated emotions. For example, if the user is stressed, the registration unit can provide a simple interface and minimize the input steps. If the user is relaxed, the registration unit can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the registration unit can prioritize voice input, allowing for quick input of allergy information or additives to avoid. This allows users to input information without stress by adjusting the input method according to their emotions. Emotion estimation is performed using technologies such as facial recognition and voice analysis. Facial recognition is a technology that analyzes the user's facial expressions captured by a camera and estimates their emotions. Voice analysis is a technology that analyzes the tone and speed of the user's voice and estimates their emotions. This allows the registration unit to grasp the user's emotions in real time and provide appropriate input methods. Furthermore, the registration unit can accumulate user emotion data and analyze long-term emotional trends. For example, the registration unit can analyze user emotion data over time to identify patterns of emotional change. This allows for a detailed understanding of user emotions and the optimization of input methods.

[0063] The registration unit can analyze the user's past allergy reaction history and suggest appropriate input fields. For example, the registration unit can automatically display allergens the user has reacted to in the past as candidates. It can also suggest relevant additives based on the user's past allergy reaction history. Furthermore, the registration unit can analyze the user's past allergy reaction history and suggest input fields appropriate for specific seasons or situations. This allows users to efficiently input information by suggesting optimal input fields based on their past allergy reaction history. Allergy reaction history is collected based on data such as past medical records and self-reports. Medical records are records of allergy tests and treatments the user has received in the past, while self-reports are data in which the user reports their own allergy reactions. This allows the registration unit to understand the user's past allergy reaction history in detail and suggest appropriate input fields. Furthermore, the registration unit can analyze the user's allergy reaction history using machine learning algorithms. For example, it can cluster the user's allergy reaction history using algorithms such as K-means or hierarchical clustering to identify relevant input fields. This allows the registration unit to suggest optimal input fields based on the user's allergy reaction history.

[0064] The registration unit can filter information based on the user's current health status when they input allergy information or additives they wish to avoid. For example, if a user is currently unwell, the registration unit will prompt them to prioritize inputting specific allergy items. Furthermore, if the user inputs the results of a health checkup, the registration unit can suggest additives to avoid based on those results. In addition, the registration unit can input information about medications the user is currently taking and filter additives that may interact with those medications. This allows for more appropriate information input by filtering according to the user's health status. Current health status is assessed based on data such as body temperature, blood pressure, and the presence or absence of allergy symptoms. Body temperature can be measured by the user using a thermometer and entered into the registration unit. Blood pressure can be measured by the user using a blood pressure monitor and entered into the registration unit. The presence or absence of allergy symptoms is data reported by the user about their own symptoms. This allows the registration unit to gain a detailed understanding of the user's current health status and select appropriate information. Furthermore, the registration unit can extract frequently occurring keywords and prioritize the display of information related to the user's health status. For example, based on frequently occurring keywords, relevant allergy and additive information can be extracted and presented to the user. This allows the registration unit to select appropriate information based on the user's health status and support data entry.

[0065] The registration unit can estimate the user's emotions and, based on those emotions, determine the priority of allergy information or additives to be entered. For example, if the user is feeling stressed, the registration unit may prompt them to prioritize entering important allergy information. It can also prompt the user to enter detailed additive information if they are relaxed. Furthermore, if the user is in a hurry, the registration unit may prompt them to enter only the most important allergy information. This allows important information to be prioritized by determining input priorities according to the user's emotions. Emotion estimation is performed using technologies such as facial recognition and voice analysis. Facial recognition is a technology that analyzes the user's facial expressions captured by a camera to estimate emotions. Voice analysis is a technology that analyzes the tone and speed of the user's voice to estimate emotions. This allows the registration unit to grasp the user's emotions in real time and provide appropriate input priorities. Furthermore, the registration unit can accumulate user emotion data and analyze long-term emotional trends. For example, the registration unit can analyze user emotion data over time to identify patterns of emotional change. This allows for a detailed understanding of user emotions and the optimization of input priorities.

[0066] The registration system can prioritize inputting highly relevant information when users enter allergy information or additives they wish to avoid, taking into account their geographical location. For example, if a user is in a specific region, the system can prompt them to prioritize inputting common allergens in that region. It can also prompt users to input common additive information in their travel destination if they are traveling. Furthermore, if a user is in a specific store, the system can prompt them to prioritize inputting additive information likely to be used in that store. This allows the system to prioritize inputting highly relevant information by considering geographical location. Geographical location information is obtained, for example, from GPS data or address information. GPS data is location information obtained from the user's smartphone or other devices, while address information is address data entered by the user. This allows the registration system to understand the user's geographical location in detail and provide appropriate information. Additionally, the registration system can extract common elements across different clusters and prioritize the display of highly relevant information. For example, it can extract and present relevant allergy and additive information based on high-frequency elements or common concepts. This allows the registration unit to select and support the input of appropriate information based on the user's geographical location.

[0067] The registration section can analyze a user's social media activity and input relevant information when they enter allergy information or additives they wish to avoid. For example, the registration section can analyze photos of meals shared by the user on social media and prompt them to enter allergy information. It can also suggest additives to avoid based on information from health-related accounts the user follows on social media. Furthermore, the registration section can prompt users to enter relevant allergy information based on information from groups they participate in on social media. This allows for efficient input of relevant information by analyzing social media activity. Social media activity is analyzed based on, for example, the content of posts, the number of likes, and follower reactions. The content of posts is data such as text, images, and videos posted by the user on social media, and the number of likes is data showing the reactions of other users to the user's posts. Follower reactions are data such as comments and shares on the user's posts. This allows the registration section to understand the user's social media activity in detail and provide relevant information. Furthermore, the registration section can identify the inventor's areas of interest and prioritize the display of relevant allergy and additive information. For example, based on survey results and past research themes, the system identifies the inventor's areas of interest and extracts relevant information. This allows the registration unit to select appropriate information based on the user's social media activity and support input.

[0068] The information provider can estimate the user's emotions and change the way it provides ingredient information based on those estimates. For example, if the user is stressed, the provider can provide simple and highly visible ingredient information. If the user is relaxed, the provider can also provide detailed ingredient information. Furthermore, if the user is in a hurry, the provider can provide only the essential ingredient information. By adjusting the way ingredient information is provided according to the user's emotions, the user can receive information without feeling stressed. Emotion estimation is performed using technologies such as facial recognition and voice analysis. Facial recognition is a technology that analyzes the user's facial expressions captured by a camera and estimates their emotions. Voice analysis is a technology that analyzes the tone and speed of the user's voice and estimates their emotions. This allows the provider to grasp the user's emotions in real time and provide appropriate ingredient information. Furthermore, the provider can also analyze contextual information and optimize the way ingredient information is provided. For example, it can adjust the way ingredient information is provided based on contextual information such as surrounding sentences and related topics. This allows the provider to provide appropriate ingredient information based on the user's emotions and contextual information.

[0069] The information provider can select an appropriate method of providing ingredient information based on past provision history. For example, the provider can prioritize the method of providing ingredient information that the user has preferred in the past. The provider can also select the most effective method based on the user's past provision history. Furthermore, the provider can analyze the user's past provision history and select a method appropriate to specific situations. This allows users to efficiently receive information by selecting the optimal method based on past provision history. Provision history is collected based on data such as past provision dates and content. The provision date is a record of when the ingredient information was provided, and the content is the details of the provided ingredient information. This allows the provider to understand the user's past provision history in detail and select an appropriate method. Furthermore, the provider can analyze metadata to optimize the method of providing ingredient information. For example, it can adjust the method of providing ingredient information based on metadata such as tag information and attribute data. This allows the provider to provide appropriate ingredient information based on the user's past provision history and metadata.

[0070] The information provider can select information based on the food provider's current menu when providing ingredient information. For example, the provider can prioritize providing relevant ingredient information based on the food provider's current menu. The provider can also update ingredient information in real time in response to changes in the food provider's menu. Furthermore, the provider can filter and provide ingredient information related to specific menu items of the food provider. This allows for the provision of highly relevant ingredient information by filtering based on the current menu. The current menu is obtained from data such as menu lists and ingredient tables. A menu list is a list of dishes and drinks offered by the food provider, and an ingredient table is a detailed list of the ingredients contained in each dish and drink. This allows the provider to have a detailed understanding of the food provider's current menu and provide appropriate ingredient information. Furthermore, the provider can extract highly innovative elements and optimize the method of providing ingredient information. For example, it can identify highly innovative elements based on patent search results and technology evaluations, and adjust the method of providing ingredient information. This allows the provider to provide appropriate ingredient information based on the food provider's current menu.

[0071] The information provider can estimate the user's emotions and determine the order in which to provide component information based on the estimated emotions. For example, if the user is stressed, the provider will prioritize providing important component information. If the user is relaxed, the provider can also provide detailed component information. Furthermore, if the user is in a hurry, the provider can provide only the most important component information. This allows the provider to prioritize important information by determining the priority of component information according to the user's emotions. Emotion estimation is performed using technologies such as facial recognition and voice analysis. Facial recognition is a technology that analyzes the user's facial expressions captured by a camera and estimates their emotions. Voice analysis is a technology that analyzes the tone and speed of the user's voice and estimates their emotions. This allows the provider to grasp the user's emotions in real time and provide the appropriate order of component information. Furthermore, the provider can optimize the method of providing component information using natural language generation technology. For example, it can adjust the method of providing component information using technologies such as text generation algorithms and template-based generation. This allows the provider to provide appropriate component information based on the user's emotions and natural language generation technology.

[0072] The data provider can prioritize providing highly relevant information based on the food provider's geographical location when providing ingredient information. For example, if a food provider is in a specific region, the data provider will prioritize providing ingredient information common in that region. Furthermore, if a food provider is traveling, the data provider can provide ingredient information common in their destination region. Additionally, if a food provider is at a specific store, the data provider can prioritize providing ingredient information likely to be used at that store. This allows the data provider to prioritize providing highly relevant ingredient information by considering geographical location. Geographical location information is obtained, for example, from GPS data or address information. GPS data is location information obtained from the food provider's device, and address information is address data entered by the food provider. This allows the data provider to have a detailed understanding of the food provider's geographical location and provide appropriate ingredient information. Furthermore, the data provider can optimize the method of providing ingredient information using supervised learning. For example, it can adjust the method of providing ingredient information using labeled datasets or training processes. This allows the data provider to provide appropriate ingredient information based on the food provider's geographical location.

[0073] The information provider can analyze the social media activities of food providers and provide relevant information when providing ingredient information. For example, the provider can analyze photos of menus shared by food providers on social media and provide ingredient information. The provider can also provide ingredient information based on information about health-related accounts that food providers follow on social media. Furthermore, the provider can provide relevant ingredient information based on information about groups that food providers participate in on social media. This allows for the efficient provision of relevant ingredient information by analyzing social media activities. Social media activities are analyzed based on, for example, the content of posts, the number of likes, and follower reactions. The content of posts is data such as text, images, and videos posted by food providers on social media, and the number of likes is data showing the reactions of other users to the food provider's posts. Follower reactions are data such as comments and shares on the food provider's posts. This allows the provider to understand the food provider's social media activities in detail and provide relevant information. Furthermore, the provider can analyze evolutionary patterns and optimize the method of providing ingredient information. For example, the method of providing ingredient information will be adjusted based on changes over time and the stage of technological advancement. This will allow the service provider to provide appropriate ingredient information based on the social media activities of food providers.

[0074] The matching unit can estimate the user's emotions and modify the matching criteria based on the estimated emotions. For example, if the user is stressed, the matching unit can provide simple and easily understandable matching criteria. If the user is relaxed, it can also provide detailed matching criteria. Furthermore, if the user is in a hurry, it can provide only the essential matching criteria. This allows the user to match information without feeling stressed by adjusting the matching criteria according to their emotions. Emotion estimation is performed using technologies such as facial recognition and voice analysis. Facial recognition is a technology that analyzes the user's facial expressions captured by a camera and estimates their emotions. Voice analysis is a technology that analyzes the tone and speed of the user's voice and estimates their emotions. This allows the matching unit to grasp the user's emotions in real time and provide appropriate matching criteria. Furthermore, the matching unit can also evaluate the effectiveness of elements and optimize the matching criteria. For example, it can adjust the matching criteria based on experimental results or feedback evaluations. This allows the matching unit to provide appropriate matching criteria based on the user's emotions and the effectiveness of the elements.

[0075] The matching unit can improve the accuracy of the matching process based on the interrelationship between allergy information and ingredient information. For example, the matching unit analyzes the interrelationship between allergy information and ingredient information to improve matching accuracy. The matching unit can also prioritize matching specific allergy items by considering the interrelationship between allergy information and ingredient information. Furthermore, the matching unit can adjust the matching accuracy in real time based on the interrelationship between allergy information and ingredient information. This improves matching accuracy by considering the interrelationship between allergy information and ingredient information. The interrelationship is evaluated based on, for example, the interaction of allergy components and the combination of components. The interaction of allergy components is data that shows how a specific allergy component reacts with other components, and the combination of components is data that shows the effect when multiple components are used together. This allows the matching unit to grasp the interrelationship between allergy information and ingredient information in detail and perform appropriate matching. Furthermore, the matching unit can also evaluate the quality of the summary and optimize the matching accuracy. For example, it adjusts the matching accuracy based on the accuracy of the summary and the comprehensiveness of the information. This allows the matching unit to perform appropriate matching based on the interrelationship between allergy information and ingredient information.

[0076] The matching unit can perform matching based on the user's past allergy reaction history. For example, the matching unit can improve the accuracy of matching based on the user's past allergy reaction history. The matching unit can also prioritize matching specific allergy items by considering the user's past allergy reaction history. Furthermore, the matching unit can analyze the user's past allergy reaction history and adjust the accuracy of matching in real time. This improves the accuracy of matching by considering past allergy reaction history. Allergy reaction history is collected based on data such as past medical records and self-reports. Medical records are records of allergy tests and treatments the user has received in the past, and self-reports are data in which the user reports their own allergy reactions. This allows the matching unit to have a detailed understanding of the user's past allergy reaction history and perform appropriate matching. Furthermore, the matching unit can clarify the specific methods and criteria for matching and optimize the accuracy of matching. For example, it can adjust the accuracy of matching based on methods such as exact match or partial match. This allows the matching unit to perform appropriate matching based on the user's past allergy reaction history.

[0077] The matching unit can estimate the user's emotions and change the display order of matching results based on the estimated emotions. For example, if the user is feeling stressed, the matching unit will prioritize displaying important matching results. It can also display detailed matching results if the user is relaxed. Furthermore, if the user is in a hurry, the matching unit can display only the most important matching results. This allows the system to prioritize the display of important information by adjusting the display order of matching results according to the user's emotions. Emotion estimation is performed using technologies such as facial recognition and voice analysis. Facial recognition is a technology that analyzes the user's facial expressions captured by a camera to estimate emotions. Voice analysis is a technology that analyzes the tone and speed of the user's voice to estimate emotions. This allows the matching unit to grasp the user's emotions in real time and provide an appropriate display order for matching results. Furthermore, the matching unit can also optimize the display order of matching results by clarifying specific methods and criteria for changing the display order. For example, it can adjust the display order of matching results based on methods such as sorting by importance or urgency. This allows the matching unit to provide appropriate matching results based on the user's emotions.

[0078] The matching unit can perform matching based on the geographical distribution of component information during the matching process. For example, the matching unit can improve the accuracy of matching based on the geographical distribution of component information. The matching unit can also prioritize matching component information that is common in a particular region, taking into account the geographical distribution of component information. Furthermore, the matching unit can adjust the accuracy of matching in real time based on the geographical distribution of component information. This improves the accuracy of matching by considering the geographical distribution of component information. Geographical distribution is evaluated based on, for example, the distribution of components by region and the geographical bias of components. The distribution of components by region is a list of components commonly used in a particular region, and the geographical bias of components is data on components that are particularly frequently used in a particular region. This allows the matching unit to grasp the geographical distribution of component information in detail and perform appropriate matching. Furthermore, the matching unit can clarify the specific methods and criteria for matching and optimize the accuracy of matching. For example, it can adjust the accuracy of matching based on methods such as exact match or partial match. This allows the matching unit to perform appropriate matching based on the geographical distribution of component information.

[0079] The matching unit can improve the accuracy of the matching by referring to relevant literature on ingredient information during the matching process. For example, the matching unit improves the accuracy of the matching based on relevant literature on ingredient information. The matching unit can also prioritize matching specific allergy items by referring to relevant literature on ingredient information. Furthermore, the matching unit can adjust the accuracy of the matching in real time based on relevant literature on ingredient information. This improves the accuracy of the matching by referring to relevant literature. Relevant literature is evaluated based on, for example, academic papers and patent documents. Academic papers are documents that show the latest research results on ingredient information, and patent documents are documents that show technical details on ingredient information. This allows the matching unit to have a detailed understanding of relevant literature on ingredient information and perform appropriate matching. Furthermore, the matching unit can also clarify the specific methods and criteria for matching and optimize the accuracy of the matching. For example, it adjusts the accuracy of the matching based on methods such as algorithm improvements and data accuracy improvements. This allows the matching unit to perform appropriate matching based on relevant literature on ingredient information.

[0080] The analysis unit can estimate the user's emotions and modify the photo analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visual photo analysis method. It can also provide a more detailed photo analysis method if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can analyze only the essential component information. This allows the user to analyze information without feeling stressed by adjusting the photo analysis method according to their emotions. Emotion estimation is performed using technologies such as facial recognition and voice analysis. Facial recognition is a technology that analyzes the user's facial expressions captured by the camera and estimates their emotions. Voice analysis is a technology that analyzes the tone and speed of the user's voice and estimates their emotions. This allows the analysis unit to grasp the user's emotions in real time and provide an appropriate photo analysis method. Furthermore, the analysis unit can also optimize the accuracy of photo analysis by clarifying specific methods and criteria for modifying the photo analysis method. For example, it can adjust the photo analysis method based on methods such as selecting an analysis algorithm and adjusting the analysis accuracy. This allows the analysis unit to perform appropriate photo analysis based on the user's emotions.

[0081] The analysis unit can change the level of detail of the analysis based on the importance of the product's ingredient information during photo analysis. For example, the analysis unit can perform a detailed analysis based on the importance of the product's ingredient information. The analysis unit can also prioritize the analysis of specific ingredient information, taking into account its importance. Furthermore, the analysis unit can adjust the accuracy of the analysis in real time based on the importance of the product's ingredient information. This allows for the prioritization of important information by adjusting the level of detail of the analysis based on the importance of the ingredient information. The importance of ingredient information is evaluated based on factors such as the presence or absence of allergens and the amount of each ingredient. The presence or absence of allergens is data indicating whether a specific allergen is present, and the amount of each ingredient is data indicating the amount of each ingredient. This allows the analysis unit to grasp the importance of the product's ingredient information in detail and perform an appropriate analysis. Furthermore, the analysis unit can optimize the accuracy of the analysis by clarifying specific methods and criteria for changing the level of detail of the analysis. For example, the level of detail of the analysis can be adjusted based on methods such as the depth of the analysis and the number of analysis items. This allows the analysis unit to perform an appropriate analysis based on the importance of the product's ingredient information.

[0082] The analysis unit can use different analysis algorithms depending on the product category when analyzing photographs. For example, the analysis unit can apply different analysis algorithms depending on the food category. It can also apply different analysis algorithms depending on the beverage category. Furthermore, it can apply different analysis algorithms depending on the seasoning category. This improves the accuracy of the analysis by applying the most suitable analysis algorithm for each product category. Product categories are evaluated based on classification methods such as food, beverages, and pharmaceuticals. The food category is a list of food-related products, the beverage category is a list of beverage-related products, and the pharmaceutical category is a list of medicine-related products. This allows the analysis unit to understand the product category in detail and apply an appropriate analysis algorithm. Furthermore, the analysis unit can also optimize the accuracy of the analysis by clarifying the specific type and implementation method of the analysis algorithm. For example, it can select an analysis algorithm based on methods such as image recognition algorithms and text analysis algorithms. This allows the analysis unit to perform appropriate analysis based on the product category.

[0083] The analysis unit can estimate the user's emotions and change how the analysis results are displayed based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also display detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can display only the most important analysis results. This allows the user to receive information without feeling stressed by adjusting the display method according to their emotions. Emotion estimation is performed using technologies such as facial recognition and voice analysis. Facial recognition is a technology that analyzes the user's facial expressions captured by a camera to estimate emotions. Voice analysis is a technology that analyzes the tone and speed of the user's voice to estimate emotions. This allows the analysis unit to grasp the user's emotions in real time and provide an appropriate display method for the analysis results. Furthermore, the analysis unit can clarify specific methods and criteria for changing the display method and optimize the display method of the analysis results. For example, it can adjust the display method based on methods such as the information display format and notification method. This allows the analysis unit to provide appropriate analysis results based on the user's emotions.

[0084] The analysis unit can determine the order of analysis based on the product submission date during photo analysis. For example, the analysis unit can determine the priority of analysis based on the product submission date. The analysis unit can also prioritize the analysis of specific products, taking into account the product submission date. Furthermore, the analysis unit can adjust the analysis priority in real time based on the product submission date. This allows for the priority analysis of important information by determining the analysis priority based on the product submission date. The submission date is evaluated based on factors such as the submission date and time and the elapsed time since submission. The submission date and time is a record of the date and time the product was submitted, and the elapsed time since submission is data indicating the time since the product was submitted. This allows the analysis unit to grasp the product submission date in detail and perform appropriate analysis. Furthermore, the analysis unit can clarify specific methods and criteria for determining the order of analysis and optimize the accuracy of the analysis. For example, it can adjust the order of analysis based on methods such as priority based on submission date or priority based on urgency. This allows the analysis unit to perform appropriate analysis based on the product submission date.

[0085] The analysis unit can change the order of analysis based on the relevance of products during photo analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of products. The analysis unit can also prioritize the analysis of specific products, taking relevance into consideration. Furthermore, the analysis unit can adjust the order of analysis in real time based on product relevance. This allows for the prioritization of analysis of important information by adjusting the order of analysis based on product relevance. Product relevance is evaluated based on factors such as the commonality of ingredients and the similarity of uses. Commonality of ingredients is a list of ingredients that are commonly included in multiple products, and similarity of uses is data indicating whether multiple products are used for the same purpose. This allows the analysis unit to grasp the relevance of products in detail and perform appropriate analysis. Furthermore, the analysis unit can clarify specific methods and criteria for changing the order of analysis and optimize the accuracy of the analysis. For example, it can adjust the order of analysis based on methods such as sorting based on relevance or sorting based on importance. This allows the analysis unit to perform appropriate analysis based on product relevance.

[0086] The warning unit can estimate the user's emotions and change how warnings are displayed based on those emotions. For example, if the user is stressed, the warning unit can provide a simple and highly visible warning. If the user is relaxed, it can also provide a more detailed warning. Furthermore, if the user is in a hurry, it can display only the most important warnings. This allows the warning unit to adjust the display method according to the user's emotions, ensuring that users receive warnings without feeling stressed. Emotion estimation is performed using technologies such as facial recognition and voice analysis. Facial recognition is a technology that analyzes the user's facial expressions captured by a camera to estimate emotions. Voice analysis is a technology that analyzes the tone and speed of the user's voice to estimate emotions. This allows the warning unit to grasp the user's emotions in real time and provide an appropriate warning display method. Furthermore, the warning unit can also clarify specific methods and criteria for changing the warning display method, optimizing the accuracy of warnings. For example, it can adjust the warning display method based on methods such as text display and voice alerts. This allows the warning unit to provide appropriate warnings based on the user's emotions.

[0087] The warning unit can change the level of detail of a warning based on the importance of the allergy information when displaying a warning. For example, the warning unit can display a detailed warning based on the importance of the allergy information. The warning unit can also prioritize the display of warnings for specific allergens, taking into account the importance of the allergy information. Furthermore, the warning unit can adjust the level of detail of the warning in real time based on the importance of the allergy information. This allows important information to be warned about preferentially by adjusting the level of detail of the warning based on the importance of the allergy information. The importance of allergy information is evaluated based on factors such as the severity of the allergic reaction and the amount of each component. The severity of the allergic reaction is data indicating the strength of the reaction to a specific allergen, and the amount of each component is data indicating the amount of each component. This allows the warning unit to grasp the importance of allergy information in detail and provide appropriate warnings. Furthermore, the warning unit can also optimize the accuracy of warnings by clarifying specific methods and criteria for changing the level of detail of the warning. For example, the level of detail of the warning can be adjusted based on methods such as the depth of the warning and the number of warning items. This allows the warning unit to provide appropriate warnings based on the importance of the allergy information.

[0088] The warning unit can use different warning algorithms depending on the product category when displaying a warning. For example, the warning unit can apply different warning algorithms depending on the food category. It can also apply different warning algorithms depending on the beverage category. Furthermore, it can apply different warning algorithms depending on the condiment category. This improves the accuracy of warnings by applying the most appropriate warning algorithm for each product category. Product categories are evaluated based on classification methods such as food, beverages, and pharmaceuticals. The food category is a list of food-related products, the beverage category is a list of beverage-related products, and the pharmaceutical category is a list of medicine-related products. This allows the warning unit to have a detailed understanding of product categories and apply appropriate warning algorithms. Furthermore, the warning unit can also optimize the accuracy of warnings by clarifying the specific types and implementation methods of warning algorithms. For example, warning algorithms can be selected based on methods such as risk assessment algorithms and alert generation algorithms. This allows the warning unit to provide appropriate warnings based on the product category.

[0089] The warning unit can estimate the user's emotions and determine the order of warnings based on those emotions. For example, if the user is stressed, the warning unit will prioritize displaying important warnings. It can also display detailed warnings if the user is relaxed. Furthermore, if the user is in a hurry, it can display only the most important warnings. This allows the warning unit to prioritize important information by determining the priority of warnings according to the user's emotions. Emotion estimation is performed using technologies such as facial recognition and voice analysis. Facial recognition is a technology that analyzes the user's facial expressions captured by a camera to estimate emotions. Voice analysis is a technology that analyzes the tone and speed of the user's voice to estimate emotions. This allows the warning unit to grasp the user's emotions in real time and provide an appropriate order of warnings. Furthermore, the warning unit can clarify specific methods and criteria for determining the order of warnings and optimize the accuracy of the warnings. For example, it can adjust the order of warnings based on methods such as sorting by importance or urgency. This allows the warning unit to provide appropriate warnings based on the user's emotions.

[0090] The warning unit can determine the order of warnings based on the product submission date when displaying a warning. For example, the warning unit can determine the priority of warnings based on the product submission date. The warning unit can also prioritize the display of warnings for specific products, taking into account the product submission date. Furthermore, the warning unit can adjust the priority of warnings in real time based on the product submission date. This allows important information to be warned about preferentially by determining the priority of warnings based on the product submission date. The submission date is evaluated based on, for example, the submission date and time or the time elapsed since submission. The submission date and time is a record of the date and time the product was submitted, and the time elapsed since submission is data indicating the time since the product was submitted. This allows the warning unit to have a detailed understanding of the product submission date and issue appropriate warnings. Furthermore, the warning unit can clarify specific methods and criteria for determining the order of warnings and optimize the accuracy of warnings. For example, the order of warnings can be adjusted based on methods such as priority based on submission date or priority based on urgency. This allows the warning unit to issue appropriate warnings based on the product submission date.

[0091] The warning unit can change the order of warnings based on the relevance of the products when displaying a warning. For example, the warning unit adjusts the order of warnings based on the relevance of the products. The warning unit can also prioritize the display of warnings for specific products, taking into account their relevance. Furthermore, the warning unit can adjust the order of warnings in real time based on product relevance. This allows important information to be warned about preferentially by adjusting the order of warnings based on product relevance. Product relevance is evaluated based on factors such as the commonality of ingredients or the similarity of uses. Commonality of ingredients is a list of ingredients that are commonly included in multiple products, and similarity of uses is data indicating whether multiple products are used for the same purpose. This allows the warning unit to understand the relevance of products in detail and provide appropriate warnings. Furthermore, the warning unit can clarify specific methods and criteria for changing the order of warnings and optimize the accuracy of warnings. For example, it can adjust the order of warnings based on methods such as relevance-based sorting or importance-based sorting. This allows the warning unit to provide appropriate warnings based on product relevance. === Hard Collateral 1-1 === Each of the multiple elements described above, including the registration unit, provision unit, verification unit, analysis unit, and warning unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the smart device 14, allowing users to input allergy information and information on additives to be avoided using a dedicated application. The provision unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, allowing food providers to input ingredient information using a dedicated management system. The verification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and verifies the registered information with the provided ingredient information. The analysis unit is implemented, for example, by the control unit 46A of the smart device 14, and can analyze a photograph of the product taken with a smartphone camera and extract ingredient information. The warning unit is implemented, for example, by the control unit 46A of the smart device 14, and displays a warning based on the analyzed ingredient information. === Hard Collateral 1-2 === Each of the multiple elements described above, including the registration unit, provision unit, verification unit, analysis unit, and warning unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the smart glasses 214, allowing users to input allergy information and information on additives they wish to avoid using a dedicated application. The provision unit is implemented by the identification processing unit 290 of the data processing unit 12, allowing food providers to input ingredient information using a dedicated management system. The verification unit is implemented by the identification processing unit 290 of the data processing unit 12, for example, to verify the registered information with the provided ingredient information. The analysis unit is implemented by the control unit 46A of the smart glasses 214, for example, to analyze a photograph of a product taken with a smartphone camera and extract ingredient information. The warning unit is implemented by the control unit 46A of the smart glasses 214, for example, to display a warning based on the analyzed ingredient information. === Hard Collateral 1-3 === Each of the multiple elements described above, including the registration unit, provision unit, verification unit, analysis unit, and warning unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the headset terminal 314, allowing users to input allergy information and information on additives to be avoided using a dedicated application. The provision unit is implemented by the identification processing unit 290 of the data processing unit 12, allowing food providers to input ingredient information using a dedicated management system. The verification unit is implemented by the identification processing unit 290 of the data processing unit 12, for example, to verify the registered information with the provided ingredient information. The analysis unit is implemented by the control unit 46A of the headset terminal 314, for example, to analyze a photograph of the product taken with a smartphone camera and extract ingredient information. The warning unit is implemented by the control unit 46A of the headset terminal 314, for example, to display a warning based on the analyzed ingredient information. === Hard Collateral 1-4 === Each of the multiple elements described above, including the registration unit, provision unit, verification unit, analysis unit, and warning unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the robot 414, allowing users to input allergy information and information on additives to be avoided using a dedicated application. The provision unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, allowing food providers to input ingredient information using a dedicated management system. The verification unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, and verifies the registered information with the provided ingredient information. The analysis unit is implemented by, for example, the control unit 46A of the robot 414, and can analyze a photograph of a product taken with a smartphone camera and extract ingredient information. The warning unit is implemented by, for example, the control unit 46A of the robot 414, and displays a warning based on the analyzed ingredient information.

[0092] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0093] The registration function can analyze a user's past eating history when they register allergy information and information about additives they wish to avoid, and automatically suggest relevant allergy and additive information. For example, based on the ingredient information of foods the user has eaten in the past, it can identify ingredients that may cause allergic reactions and display a warning during registration. It can also automatically list foods containing similar additives based on information about additives the user has avoided in the past, and prompt the user to register them. Furthermore, it can analyze seasonal allergy trends from the user's eating history and suggest foods and ingredients to avoid during specific seasons. This allows users to register more accurate allergy and additive information based on their past eating history.

[0094] The information provider can estimate the user's emotions and change the way ingredient information is provided based on those estimates. For example, if the user is stressed, it can provide simple and easily visible ingredient information. If the user is relaxed, it can provide detailed ingredient information. Furthermore, if the user is in a hurry, it can provide only the most important ingredient information. By adjusting the way ingredient information is provided according to the user's emotions, the user can receive the information without feeling stressed. Emotion estimation is performed using technologies such as facial recognition and voice analysis. Facial recognition is a technology that analyzes the user's facial expressions captured by a camera and estimates their emotions. Voice analysis is a technology that analyzes the tone and speed of the user's voice and estimates their emotions. This allows the information provider to grasp the user's emotions in real time and provide appropriate ingredient information.

[0095] The matching unit can improve the accuracy of matching based on the user's past allergy reaction history. For example, it can prioritize matching allergens that the user has reacted to in the past and display a warning. It can also analyze the user's past allergy reaction history and automatically filter foods containing specific ingredients. Furthermore, it can improve accuracy by setting matching criteria based on the user's allergy reaction history, taking past allergy reaction history into consideration. As a result, the accuracy of matching is improved, allowing users to choose foods with confidence.

[0096] The analysis unit can estimate the user's emotions and modify the photo analysis method based on the estimated emotions. For example, if the user is stressed, it can provide a simple and highly visual photo analysis method. If the user is relaxed, it can provide a more detailed photo analysis method. Furthermore, if the user is in a hurry, it can analyze only the essential component information. This allows the user to analyze information without feeling stressed by adjusting the photo analysis method according to their emotions. Emotion estimation is performed using technologies such as facial recognition and voice analysis. Facial recognition is a technology that analyzes the user's facial expressions captured by the camera and estimates their emotions. Voice analysis is a technology that analyzes the tone and speed of the user's voice and estimates their emotions. This allows the analysis unit to grasp the user's emotions in real time and provide an appropriate photo analysis method.

[0097] The warning unit can estimate the user's emotions and change how warnings are displayed based on those estimates. For example, if the user is stressed, it can provide a simple and highly visible warning. If the user is relaxed, it can provide a more detailed warning. Furthermore, if the user is in a hurry, it can display only the most important warnings. This allows the warning system to adjust the display method according to the user's emotions, ensuring that users receive warnings without feeling stressed. Emotion estimation is performed using technologies such as facial recognition and voice analysis. Facial recognition is a technology that analyzes the user's facial expressions captured by a camera to estimate emotions. Voice analysis is a technology that analyzes the tone and speed of the user's voice to estimate emotions. This allows the warning unit to grasp the user's emotions in real time and provide appropriate warning displays.

[0098] The service provider can select ingredient information based on the current menu of the food provider. For example, it can prioritize providing relevant ingredient information based on the food provider's current menu. It can also update ingredient information in real time in response to changes in the food provider's menu. Furthermore, it can filter and provide ingredient information related to specific menu items of the food provider. This allows for the provision of highly relevant ingredient information by filtering based on the current menu. The current menu is obtained from data such as menu lists and ingredient tables. A menu list is a list of dishes and drinks offered by the food provider, and an ingredient table is a detailed list of the ingredients contained in each dish and drink. This allows the service provider to have a detailed understanding of the food provider's current menu and provide appropriate ingredient information.

[0099] The matching unit can perform matching based on the geographical distribution of component information. For example, it can improve the accuracy of matching based on the geographical distribution of component information. It can also prioritize matching component information that is common in a particular region, taking into account the geographical distribution of component information. Furthermore, it can adjust the accuracy of matching in real time based on the geographical distribution of component information. This improves the accuracy of matching by considering the geographical distribution of component information. Geographical distribution is evaluated based on, for example, the distribution of components by region and the geographical bias of components. The distribution of components by region is a list of components commonly used in a particular region, and the geographical bias of components is data on components that are used particularly often in a particular region. This allows the matching unit to grasp the geographical distribution of component information in detail and perform appropriate matching.

[0100] The analysis unit can change the level of detail of the analysis based on the importance of the product's ingredient information during photo analysis. For example, it can perform a detailed analysis based on the importance of the product's ingredient information. It can also prioritize the analysis of specific ingredient information, taking into account its importance. Furthermore, it can adjust the accuracy of the analysis in real time based on the importance of the product's ingredient information. This allows for the prioritization of important information by adjusting the level of detail of the analysis based on the importance of the ingredient information. The importance of ingredient information is evaluated based on factors such as the presence or absence of allergens and the amount of each ingredient. The presence or absence of allergens is data indicating whether a specific allergen is present, and the amount of each ingredient is data indicating the quantity of each ingredient. This allows the analysis unit to grasp the importance of the product's ingredient information in detail and perform an appropriate analysis.

[0101] The warning unit can change the level of detail of a warning based on the importance of the allergy information when a warning is displayed. For example, it can display a detailed warning based on the importance of the allergy information. It can also prioritize the display of warnings for specific allergens, taking into account the importance of the allergy information. Furthermore, it can adjust the level of detail of the warning in real time based on the importance of the allergy information. This allows important information to be warned about preferentially by adjusting the level of detail of the warning based on the importance of the allergy information. The importance of allergy information is evaluated based on factors such as the severity of the allergic reaction and the amount of each component. The severity of the allergic reaction is data indicating the strength of the reaction to a specific allergenic component, and the component amount is data indicating the amount of each component. This allows the warning unit to grasp the importance of allergy information in detail and provide appropriate warnings.

[0102] The service provider can analyze users' social media activity and provide relevant ingredient information. For example, it can analyze photos of meals shared by users on social media and prompt them to input allergy information. It can also suggest additives to avoid based on information from health-related accounts that users follow on social media. Furthermore, it can prompt users to input relevant allergy information based on information from groups they participate in on social media. This allows the service provider to efficiently provide relevant ingredient information by analyzing social media activity. Social media activity is analyzed based on, for example, the content of posts, the number of likes, and follower reactions. Post content refers to data such as text, images, and videos posted by users on social media, while the number of likes indicates data showing how other users react to a user's post. Follower reactions refer to data such as comments and shares on a user's post. This allows the service provider to understand users' social media activity in detail and provide relevant information.

[0103] The following briefly describes the processing flow for example form 2.

[0104] Step 1: The registration section allows users to register their allergy information and information about additives they wish to avoid. Users can use a dedicated application to input information about food allergies, drug allergies, and additives such as preservatives and colorings. Step 2: The supply department inputs the ingredient information of meals provided by food service providers such as daycare centers and restaurants. This ingredient information includes a list of ingredients and their quantities. Food service providers can input this ingredient information using a dedicated management system. Step 3: The matching unit compares the information registered by the registration unit with the ingredient information provided by the provision unit. The matching is performed using methods such as exact match or partial match. The matching unit stores the user's allergy information and ingredient information in a database and performs the matching. Step 4: The analysis unit analyzes the product photos taken by the user and extracts the ingredient information. The analysis is performed using an image analysis algorithm. The analysis unit analyzes the product photos taken with the smartphone camera and extracts the ingredient information. Step 5: The warning unit displays a warning if any of the ingredients, based on the analysis information performed by the analysis unit, match the user's NG list. The warning is delivered via methods such as text display or audio alert. The warning unit displays the warning on the user's smartphone.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0107] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0109] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0110] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0112] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0113] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0115] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0116] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0117] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0118] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0119] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0123] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0125] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0126] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0141] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0142] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0149] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0158] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0160] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0161] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0162] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0165] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0166] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0167] 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.

[0168] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0169] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0170] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0171] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0173] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0174] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0175] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0176] [Explanation of Symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A registration unit for registering the user's allergy information or information on additives they wish to avoid, A food service provider, either a nursery school or a restaurant, provides ingredient information to a service department, A matching unit that compares the information registered by the registration unit with the component information provided by the provision unit, An analysis unit that analyzes product photos taken by the user, The system includes a warning unit that displays a warning based on the component information analyzed by the analysis unit. A system characterized by the following features.

2. The aforementioned registration unit is The system estimates the user's emotions and adjusts how allergy information or additives to avoid are entered based on those estimated emotions. The system according to feature 1.

3. The aforementioned registration unit is It analyzes the user's past allergy reaction history and suggests appropriate input fields. The system according to feature 1.

4. The aforementioned registration unit is When users enter allergy information or additives they wish to avoid, the system filters the information based on their current health status. The system according to feature 1.

5. The aforementioned registration unit is The system estimates the user's emotions and, based on those emotions, determines the priority of allergy information or additives to be entered. The system according to feature 1.

6. The aforementioned registration unit is When users enter allergy information or additives they wish to avoid, the system prioritizes inputting more relevant information based on the user's geographical location. The system according to feature 1.

7. The aforementioned registration unit is When users enter allergy information or additives they wish to avoid, the system analyzes their social media activity and inputs relevant information. The system according to feature 1.

8. The aforementioned supply unit is, We estimate the user's emotions and change the way ingredient information is provided based on those estimated emotions. The system according to feature 1.

9. The aforementioned supply unit is, When providing ingredient information, the appropriate method of provision will be selected based on past provision history. The system according to feature 1.

10. The aforementioned supply unit is, When providing ingredient information, the information is selected based on the current menu of the food provider. The system according to feature 1.

Images