system

Abstract

The system according to the embodiment aims to grasp food allergy information in advance and reduce the burden on people with food allergies. [Solution] The system according to this embodiment comprises a reception unit, an acquisition unit, an analysis unit, and a provision unit. The reception unit receives food allergy information. The acquisition unit acquires restaurant menu information. The analysis unit analyzes the food allergy information received by the reception unit and the menu information acquired by the acquisition unit. The provision unit provides the results analyzed by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology made it difficult to obtain information about food allergies in advance when dining out, which presented a significant burden for people with food allergies.

[0005] The system according to the embodiment aims to grasp food allergy information in advance and reduce the burden on people with food allergies. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an acquisition unit, an analysis unit, and a provision unit. The reception unit inputs food allergy information. The acquisition unit acquires restaurant menu information. The analysis unit analyzes the food allergy information input by the reception unit and the menu information acquired by the acquisition unit. The provision unit provides the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can obtain information about food allergies in advance and reduce the burden on people with food allergies. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A food allergy reduction system according to an embodiment of the present invention reduces the burden on people with food allergies by using AI to easily display food allergy menus on their smartphones, which are usually only available once they enter a restaurant. The system begins with a user accessing a smartphone app and inputting their food allergy information. Next, the smartphone app retrieves menu information for the restaurant the user visited. Restaurant menu information can be obtained by scanning a two-dimensional code (e.g., a QR code (registered trademark)) or from the restaurant's website. AI analyzes the user's food allergy information and the restaurant's menu information to identify menu items containing allergens. The analysis results are displayed on the smartphone app, allowing the user to avoid menu items containing allergens. This system reduces the burden on people with food allergies and allows them to enjoy dining out with peace of mind. For example, a user accesses a smartphone app and inputs food allergy information. The user enters information such as a peanut allergy or dairy allergy. This information is then input into the AI. Next, the smartphone app retrieves menu information for the restaurant the user visited. Restaurant menu information can be obtained by scanning a two-dimensional code or from the restaurant's website. This menu information is then input into the AI. The AI ​​analyzes the user's food allergy information and restaurant menu information to identify menu items that contain allergenic ingredients. For example, if a user with a peanut allergy visits a restaurant and the menu contains peanuts, the system will identify those items. The analysis results are displayed on the smartphone app. The user can then avoid menu items that contain allergenic ingredients. For example, a user with a peanut allergy can avoid menu items that contain peanuts. This system reduces the burden on people with food allergies, allowing them to enjoy eating out with peace of mind. In this way, the food allergy reduction system reduces the burden on people with food allergies, allowing them to enjoy eating out with peace of mind.

[0029] The food allergy reduction system according to the embodiment comprises a reception unit, an acquisition unit, an analysis unit, and a provision unit. The reception unit receives food allergy information from the user. The food allergy information entered by the user includes, but is not limited to, peanut allergies or dairy allergies. The reception unit allows, for example, the user to enter food allergy information via a smartphone app. The reception unit can also save and reuse allergy information previously entered by the user. The acquisition unit obtains restaurant menu information. The acquisition unit includes, for example, a method of scanning a 2D code or a method of obtaining information from the restaurant's website. In the method of scanning a 2D code, the user scans the 2D code using a smartphone camera to obtain menu information. In the method of obtaining information from a website, the acquisition unit accesses the restaurant's website and obtains menu information. The analysis unit analyzes the food allergy information entered by the reception unit and the menu information obtained by the acquisition unit. The analysis unit performs analysis using, for example, an algorithm to identify allergy components. The algorithm to identify allergy components includes machine learning algorithms and rule-based analysis. The analysis unit compares the user's food allergy information with menu information to identify menu items containing allergens. The provision unit provides the results analyzed by the analysis unit. The provision unit displays the analysis results, for example, on a smartphone app. The provision unit displays the analysis results in a visually easy-to-understand format so that the user can intuitively understand them. For example, menu items containing allergens are displayed in red, and menu items that do not contain allergens are displayed in green. As a result, the food allergy reduction system according to this embodiment reduces the burden on people with food allergies, allowing them to enjoy eating out with peace of mind.

[0030] The acquisition unit may include a method for scanning a two-dimensional code and a method for acquiring information from a restaurant's website. The acquisition unit may acquire menu information, for example, by scanning a two-dimensional code. For example, a user may scan a two-dimensional code using a smartphone camera to acquire menu information. The acquisition unit may also acquire information from a restaurant's website. For example, the acquisition unit may access the restaurant's website to acquire menu information. This allows the acquisition unit to acquire menu information in a variety of ways. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input the menu information acquired by scanning a two-dimensional code into a generation AI, and have the generation AI analyze the menu information.

[0031] The analysis unit can perform analysis using an algorithm for identifying allergens. The analysis unit performs analysis using, for example, an algorithm for identifying allergens. For example, the analysis unit identifies allergens using a machine learning algorithm. The analysis unit can also identify allergens using rule-based analysis. For example, the analysis unit identifies allergens based on predefined rules. This allows the analysis unit to improve the accuracy of identifying allergens. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's food allergy information and menu information into the generation AI and cause the generation AI to identify allergens.

[0032] The service provider can display the analysis results on a smartphone app. For example, the service provider can display the analysis results on a smartphone app. For example, the service provider can display the analysis results as a notification. The service provider can also display the analysis results on a dashboard. For example, the service provider can display the analysis results in a visually easy-to-understand format. This allows the service provider to intuitively confirm the analysis results. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the analysis results into a generating AI and have the generating AI display the analysis results.

[0033] The reception desk allows users to input information about peanut allergies or dairy allergies. For example, the reception desk allows users to input information about peanut allergies via a smartphone app. The reception desk also allows users to input information about dairy allergies via a smartphone app. This allows the reception desk to receive specific allergy information from users. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the allergy information entered by the user into a generating AI and have the generating AI perform an analysis of the allergy information.

[0034] The reception unit can analyze the user's past allergy information input history and select the optimal input method. For example, the reception unit can automatically display allergy information that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest allergy information to be used during a specific time period based on the user's past input history. This allows the reception unit to provide an efficient input method based on the past history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data into a generation AI and have the generation AI select the optimal input method.

[0035] When inputting allergy information, the reception unit can filter the allergy information based on the user's current health condition and dietary history. For example, if the user inputs the results of a recent health check, the reception unit filters the allergy information based on that information. Furthermore, if the user inputs their past dietary history, the reception unit can also filter the allergy information based on that information. Furthermore, if the user inputs their current health condition, the reception unit can also filter the allergy information based on that information. This allows the reception unit to provide appropriate allergy information based on the user's health condition and dietary history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's health condition and dietary history data to the generation AI and have the generation AI perform filtering.

[0036] The reception system can prioritize inputting highly relevant allergy information when users enter allergy information, taking into account their geographical location. For example, if a user is in a specific region, the reception system can prioritize inputting allergy information common in that region. Furthermore, if a user is traveling, the reception system can prioritize inputting allergy information common in their travel destination. Additionally, if a user is in a specific restaurant, the reception system can prioritize inputting allergy information related to the menu offered at that restaurant. This allows the reception system to provide highly relevant allergy information based on geographical location. Some or all of the above processing in the reception system may be performed using AI, or not. For example, the reception system can input the user's geographical location information into a generating AI and have the AI ​​prioritize highly relevant allergy information.

[0037] The reception desk can analyze the user's social media activity and input relevant allergy information when allergy information is entered. For example, the reception desk can input relevant allergy information based on the food information the user has shared on social media. It can also input relevant allergy information based on the accounts the user follows on social media. Furthermore, it can input relevant allergy information based on the groups the user participates in on social media. This allows the reception desk to provide relevant allergy information based on social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI input the relevant allergy information.

[0038] The acquisition unit can analyze the user's past menu information acquisition history and select the optimal acquisition method. For example, the acquisition unit can prioritize acquiring menu information from restaurants the user has frequently visited in the past. The acquisition unit can also prioritize suggesting acquisition methods the user has used in the past (such as QR codes or websites). Furthermore, the acquisition unit can predict and suggest menu information that the user will use at a specific time of day based on their past acquisition history. This allows the acquisition unit to provide an efficient menu information acquisition method based on past history. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's past acquisition history data into a generating AI and have the generating AI select the optimal acquisition method.

[0039] The acquisition unit can filter menu information based on the user's current dietary preferences and allergy information when acquiring it. For example, if the user inputs their recent dietary preferences, the acquisition unit can filter the menu information based on that information. The acquisition unit can also filter menu information based on past allergy information if the user inputs that information. Furthermore, if the user inputs their current dietary preferences, the acquisition unit can also filter menu information based on that information. This allows the acquisition unit to provide appropriate menu information based on the user's dietary preferences and allergy information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's dietary preferences and allergy information data into a generating AI and have the generating AI perform the filtering.

[0040] The acquisition unit can prioritize the acquisition of highly relevant menu information by considering the user's geographical location when acquiring menu information. For example, if the user is in a specific region, the acquisition unit will prioritize acquiring menu information that is common in that region. Furthermore, if the user is traveling, the acquisition unit can prioritize acquiring menu information that is common in the destination region. Additionally, if the user is in a specific restaurant, the acquisition unit can prioritize acquiring menu information offered at that restaurant. This allows the acquisition unit to provide highly relevant menu information based on geographical location. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's geographical location information into a generating AI and have the generating AI prioritize highly relevant menu information.

[0041] The acquisition unit can analyze the user's social media activity and acquire relevant menu information when acquiring menu information. For example, the acquisition unit can acquire relevant menu information based on meal information shared by the user on social media. The acquisition unit can also acquire relevant menu information based on information about accounts that the user follows on social media. Furthermore, the acquisition unit can acquire relevant menu information based on information about groups that the user participates in on social media. In this way, the acquisition unit can provide relevant menu information based on social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's social media activity data into a generating AI and have the generating AI perform the acquisition of relevant menu information.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between allergens. For example, the analysis unit can identify menu items containing multiple allergens by taking into account the interrelationships between allergens. The analysis unit can also evaluate the risk of allergic reactions by taking into account the interrelationships between allergens. Furthermore, the analysis unit can create a list of menu items containing allergens by taking into account the interrelationships between allergens. In this way, the analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between allergens. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input interrelationship data between allergens into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0043] The analysis unit can perform the analysis while taking into account the user's past allergic reaction history. For example, the analysis unit identifies menu items containing allergenic ingredients based on the user's past allergic reaction history. The analysis unit can also evaluate the risk of an allergic reaction based on the user's past allergic reaction history. Furthermore, the analysis unit can create a list of menu items containing allergenic ingredients based on the user's past allergic reaction history. This allows the analysis unit to improve the accuracy of the analysis based on the past allergic reaction history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past allergic reaction history data into the generation AI and have the generation AI perform the analysis.

[0044] The analysis unit can perform the analysis while taking into account the geographical distribution of the menu items. For example, the analysis unit can identify allergens that are common in a specific region by taking into account the geographical distribution of the menu items. The analysis unit can also identify menu items that contain allergens that are common in a specific region by taking into account the geographical distribution of the menu items. Furthermore, the analysis unit can create a list of menu items that contain allergens that are common in a specific region by taking into account the geographical distribution of the menu items. This enables the analysis unit to perform analysis based on geographical distribution. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the geographical distribution data of the menu items into the generation AI and have the generation AI perform the analysis.

[0045] During analysis, the analysis unit can improve the accuracy of the analysis by referring to related literature. The analysis unit, for example, identifies allergenic ingredients by referring to related literature. The analysis unit can also evaluate the risk of allergic reactions by referring to related literature. Furthermore, the analysis unit can also create a list of menus containing allergenic ingredients by referring to related literature. This allows the analysis unit to improve the accuracy of the analysis by referring to related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0046] The providing unit can select the optimal display method by referring to the user's past allergic reaction history when providing the data. The providing unit, for example, highlights menu items containing allergenic ingredients based on the user's past allergic reaction history. The providing unit can also display menu items containing allergenic ingredients in different colors based on the user's past allergic reaction history. Furthermore, the providing unit can display menu items containing allergenic ingredients in list format based on the user's past allergic reaction history. This allows the providing unit to provide the optimal display method based on the past allergic reaction history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past allergic reaction history data into the generating AI and cause the generating AI to select the optimal display method.

[0047] The providing unit can customize the display of information based on the user's current health condition when providing the information. For example, when the user inputs their current health condition, the providing unit highlights menu items containing allergens based on the information. Furthermore, when the user inputs their current health condition, the providing unit can also display menu items containing allergens in a different color based on the information. Furthermore, when the user inputs their current health condition, the providing unit can also display menu items containing allergens in a list format based on the information. This enables the providing unit to display optimal information based on the current health condition. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current health condition data to the generating AI and cause the generating AI to display the information.

[0048] The service provider can provide appropriate information by considering the user's geographical location at the time of delivery. For example, if the user is in a specific region, the service provider can display menus that contain common allergens in that region. Furthermore, if the user is traveling, the service provider can display menus that contain common allergens in the destination region. Additionally, if the user is in a specific restaurant, the service provider can display allergy information related to the menu offered at that restaurant. This enables the service provider to provide optimal information based on geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location into a generating AI and have the generating AI provide appropriate information.

[0049] The service provider can analyze the user's social media activity and adjust how information is displayed at the time of delivery. For example, the service provider can display relevant allergy information based on the food information the user has shared on social media. It can also display relevant allergy information based on the accounts the user follows on social media. Furthermore, it can display relevant allergy information based on the groups the user participates in on social media. This enables the service provider to display information optimally based on social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI adjust how information is displayed.

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

[0051] When a user inputs food allergy information, the reception unit can assist the user in inputting the information by taking into account the user's past dietary history. For example, the reception unit can automatically suggest menus that do not contain allergenic ingredients based on the dietary history previously input by the user. The reception unit can also warn the user about menus that may contain allergenic ingredients based on menus that the user has avoided in the past. Furthermore, the reception unit can analyze the user's past dietary history and suggest new menus that do not contain allergenic ingredients. This allows the reception unit to assist the user in inputting more appropriate allergy information based on the user's past dietary history.

[0052] The acquisition unit can acquire menu information taking into consideration the user's current health condition. For example, if the user inputs the results of a health check, the acquisition unit can prioritize acquisition of menus that do not contain allergens based on that information. In addition, if the user inputs their current health condition, the acquisition unit can also filter menus that do not contain allergens based on that information. Furthermore, if the user has a specific health condition, the acquisition unit can acquire menu information appropriate for that condition. This allows the acquisition unit to provide appropriate menu information based on the user's health condition.

[0053] The acquisition unit can analyze the user's past menu information acquisition history and select the optimal acquisition method. For example, it can prioritize acquisition of menu information from restaurants that the user has frequently visited in the past. The acquisition unit can also prioritize suggesting acquisition methods (two-dimensional code, website, etc.) that the user has used in the past. Furthermore, the acquisition unit can predict and suggest menu information that will be used during a specific time period based on the user's past acquisition history. This allows the acquisition unit to provide an efficient menu information acquisition method based on the past history.

[0054] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between allergens during analysis. For example, a menu containing multiple allergens can be identified by taking into account the interrelationships between allergens. The risk of an allergic reaction can also be evaluated by taking into account the interrelationships between allergens. Furthermore, a list of menus containing allergens can be created by taking into account the interrelationships between allergens. In this way, the analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between allergens.

[0055] When providing the information, the providing unit can select the optimal display method by referring to the user's past allergic reaction history. For example, menu items containing allergenic ingredients can be highlighted based on the user's past allergic reaction history. Also, menu items containing allergenic ingredients can be displayed in different colors based on the user's past allergic reaction history. Furthermore, menu items containing allergenic ingredients can be displayed in list format based on the user's past allergic reaction history. This allows the providing unit to provide the optimal display method based on the user's past allergic reaction history.

[0056] When inputting allergy information, the reception unit can filter the allergy information based on the user's current health condition and dietary history. For example, if the user inputs the results of a recent health check, the allergy information can be filtered based on that information. Also, if the user inputs their past dietary history, the allergy information can be filtered based on that information. Furthermore, if the user inputs their current health condition, the allergy information can be filtered based on that information. This allows the reception unit to provide appropriate allergy information based on the user's health condition and dietary history.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The reception desk allows users to enter their food allergy information. This information may include, for example, peanut allergies or dairy allergies. The reception desk enables users to enter their food allergy information via a smartphone app and can also save and reuse previously entered allergy information. Step 2: The acquisition unit retrieves restaurant menu information. This includes methods such as scanning a QR code or retrieving information from the restaurant's website. In the QR code scanning method, the user scans the QR code using their smartphone camera to retrieve the menu information. In the website retrieval method, the acquisition unit accesses the restaurant's website and retrieves the menu information. Step 3: The analysis unit analyzes the food allergy information entered by the reception unit and the menu information acquired by the acquisition unit. The analysis unit uses an algorithm to identify allergy components, compares the user's food allergy information with the menu information, and identifies menu items that contain allergy components. Step 4: The service provider provides the results analyzed by the analysis unit. The service provider displays the analysis results on a smartphone app in a visually easy-to-understand format to allow the user to intuitively understand them. For example, menu items containing allergens are displayed in red, and menu items that do not contain allergens are displayed in green.

[0059] (Example 2) A food allergy reduction system according to an embodiment of the present invention reduces the burden on people with food allergies by using AI to easily display food allergy menus on their smartphones, which are usually only available once they enter a restaurant. The system begins with a user accessing a smartphone app and inputting their food allergy information. Next, the smartphone app retrieves menu information for the restaurant the user visited. Restaurant menu information can be obtained by scanning a two-dimensional code (e.g., a QR code) or from the restaurant's website. AI analyzes the user's food allergy information and the restaurant's menu information to identify menu items containing allergens. The analysis results are displayed on the smartphone app, allowing the user to avoid menu items containing allergens. This system reduces the burden on people with food allergies and allows them to enjoy dining out with peace of mind. For example, a user accesses a smartphone app and inputs food allergy information. The user enters information such as a peanut allergy or dairy allergy. This information is then input into the AI. Next, the smartphone app retrieves menu information for the restaurant the user visited. Restaurant menu information can be obtained by scanning a two-dimensional code or from the restaurant's website. This menu information is then input into the AI. The AI ​​analyzes the user's food allergy information and restaurant menu information to identify menu items that contain allergenic ingredients. For example, if a user with a peanut allergy visits a restaurant and the menu contains peanuts, the system will identify those items. The analysis results are displayed on the smartphone app. The user can then avoid menu items that contain allergenic ingredients. For example, a user with a peanut allergy can avoid menu items that contain peanuts. This system reduces the burden on people with food allergies, allowing them to enjoy eating out with peace of mind. In this way, the food allergy reduction system reduces the burden on people with food allergies, allowing them to enjoy eating out with peace of mind.

[0060] The food allergy reduction system according to the embodiment comprises a reception unit, an acquisition unit, an analysis unit, and a provision unit. The reception unit receives food allergy information from the user. The food allergy information entered by the user includes, but is not limited to, peanut allergies or dairy allergies. The reception unit allows, for example, the user to enter food allergy information via a smartphone app. The reception unit can also save and reuse allergy information previously entered by the user. The acquisition unit obtains restaurant menu information. The acquisition unit includes, for example, a method of scanning a 2D code or a method of obtaining information from the restaurant's website. In the method of scanning a 2D code, the user scans the 2D code using a smartphone camera to obtain menu information. In the method of obtaining information from a website, the acquisition unit accesses the restaurant's website and obtains menu information. The analysis unit analyzes the food allergy information entered by the reception unit and the menu information obtained by the acquisition unit. The analysis unit performs analysis using, for example, an algorithm to identify allergy components. The algorithm to identify allergy components includes machine learning algorithms and rule-based analysis. The analysis unit compares the user's food allergy information with menu information to identify menu items containing allergens. The provision unit provides the results analyzed by the analysis unit. The provision unit displays the analysis results, for example, on a smartphone app. The provision unit displays the analysis results in a visually easy-to-understand format so that the user can intuitively understand them. For example, menu items containing allergens are displayed in red, and menu items that do not contain allergens are displayed in green. As a result, the food allergy reduction system according to this embodiment reduces the burden on people with food allergies, allowing them to enjoy eating out with peace of mind.

[0061] The acquisition unit may include a method for scanning a two-dimensional code and a method for acquiring information from a restaurant's website. The acquisition unit may acquire menu information, for example, by scanning a two-dimensional code. For example, a user may scan a two-dimensional code using a smartphone camera to acquire menu information. The acquisition unit may also acquire information from a restaurant's website. For example, the acquisition unit may access the restaurant's website to acquire menu information. This allows the acquisition unit to acquire menu information in a variety of ways. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input the menu information acquired by scanning a two-dimensional code into a generation AI, and have the generation AI analyze the menu information.

[0062] The analysis unit can perform analysis using an algorithm for identifying allergens. The analysis unit performs analysis using, for example, an algorithm for identifying allergens. For example, the analysis unit identifies allergens using a machine learning algorithm. The analysis unit can also identify allergens using rule-based analysis. For example, the analysis unit identifies allergens based on predefined rules. This allows the analysis unit to improve the accuracy of identifying allergens. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's food allergy information and menu information into the generation AI and cause the generation AI to identify allergens.

[0063] The service provider can display the analysis results on a smartphone app. For example, the service provider can display the analysis results on a smartphone app. For example, the service provider can display the analysis results as a notification. The service provider can also display the analysis results on a dashboard. For example, the service provider can display the analysis results in a visually easy-to-understand format. This allows the service provider to intuitively confirm the analysis results. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the analysis results into a generating AI and have the generating AI display the analysis results.

[0064] The reception desk allows users to input information about peanut allergies or dairy allergies. For example, the reception desk allows users to input information about peanut allergies via a smartphone app. The reception desk also allows users to input information about dairy allergies via a smartphone app. This allows the reception desk to receive specific allergy information from users. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the allergy information entered by the user into a generating AI and have the generating AI perform an analysis of the allergy information.

[0065] The reception unit can estimate the user's emotions and adjust the input method for allergy information based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick input of allergy information. This allows the reception unit to provide the optimal input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, or without AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0066] The reception unit can analyze the user's past allergy information input history and select the optimal input method. For example, the reception unit can automatically display allergy information that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest allergy information to be used during a specific time period based on the user's past input history. This allows the reception unit to provide an efficient input method based on the past history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data into a generation AI and have the generation AI select the optimal input method.

[0067] When inputting allergy information, the reception unit can filter the allergy information based on the user's current health condition and dietary history. For example, if the user inputs the results of a recent health check, the reception unit filters the allergy information based on that information. Furthermore, if the user inputs their past dietary history, the reception unit can also filter the allergy information based on that information. Furthermore, if the user inputs their current health condition, the reception unit can also filter the allergy information based on that information. This allows the reception unit to provide appropriate allergy information based on the user's health condition and dietary history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's health condition and dietary history data to the generation AI and have the generation AI perform filtering.

[0068] The reception unit can estimate the user's emotions and determine the priority of the allergy information to be input based on the estimated user emotions. For example, when the user is nervous, the reception unit can prioritize inputting the most important allergy information. Furthermore, when the user is relaxed, the reception unit can also prioritize inputting detailed allergy information. Furthermore, when the user is in a hurry, the reception unit can prioritize inputting the most important allergy information. This allows the reception unit to input allergy information in a priority order according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or without an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0069] The reception system can prioritize inputting highly relevant allergy information when users enter allergy information, taking into account their geographical location. For example, if a user is in a specific region, the reception system can prioritize inputting allergy information common in that region. Furthermore, if a user is traveling, the reception system can prioritize inputting allergy information common in their travel destination. Additionally, if a user is in a specific restaurant, the reception system can prioritize inputting allergy information related to the menu offered at that restaurant. This allows the reception system to provide highly relevant allergy information based on geographical location. Some or all of the above processing in the reception system may be performed using AI, or not. For example, the reception system can input the user's geographical location information into a generating AI and have the AI ​​prioritize highly relevant allergy information.

[0070] The reception desk can analyze the user's social media activity and input relevant allergy information when allergy information is entered. For example, the reception desk can input relevant allergy information based on the food information the user has shared on social media. It can also input relevant allergy information based on the accounts the user follows on social media. Furthermore, it can input relevant allergy information based on the groups the user participates in on social media. This allows the reception desk to provide relevant allergy information based on social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI input the relevant allergy information.

[0071] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring menu information based on the estimated user's emotions. For example, when the user is relaxed, the acquisition unit acquires menu information at a slow pace. Furthermore, when the user is in a hurry, the acquisition unit can also acquire menu information quickly. Furthermore, when the user is excited, the acquisition unit can acquire menu information with a visually stimulating effect. This allows the acquisition unit to acquire menu information at an optimal timing according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0072] The acquisition unit can analyze the user's past menu information acquisition history and select the optimal acquisition method. For example, the acquisition unit can prioritize acquiring menu information from restaurants the user has frequently visited in the past. The acquisition unit can also prioritize suggesting acquisition methods the user has used in the past (such as QR codes or websites). Furthermore, the acquisition unit can predict and suggest menu information that the user will use at a specific time of day based on their past acquisition history. This allows the acquisition unit to provide an efficient menu information acquisition method based on past history. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's past acquisition history data into a generating AI and have the generating AI select the optimal acquisition method.

[0073] The acquisition unit can filter menu information based on the user's current dietary preferences and allergy information when acquiring it. For example, if the user inputs their recent dietary preferences, the acquisition unit can filter the menu information based on that information. The acquisition unit can also filter menu information based on past allergy information if the user inputs that information. Furthermore, if the user inputs their current dietary preferences, the acquisition unit can also filter menu information based on that information. This allows the acquisition unit to provide appropriate menu information based on the user's dietary preferences and allergy information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's dietary preferences and allergy information data into a generating AI and have the generating AI perform the filtering.

[0074] The acquisition unit can estimate the user's emotions and determine the priority of menu information to acquire based on the estimated user emotions. For example, if the user is nervous, the acquisition unit may prioritize acquiring the most important menu information. It may also prioritize acquiring detailed menu information if the user is relaxed. Furthermore, if the user is in a hurry, the acquisition unit may prioritize acquiring the most important menu information. This allows the acquisition unit to acquire menu information with priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, or not. For example, the acquisition unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0075] The acquisition unit can prioritize the acquisition of highly relevant menu information by considering the user's geographical location when acquiring menu information. For example, if the user is in a specific region, the acquisition unit will prioritize acquiring menu information that is common in that region. Furthermore, if the user is traveling, the acquisition unit can prioritize acquiring menu information that is common in the destination region. Additionally, if the user is in a specific restaurant, the acquisition unit can prioritize acquiring menu information offered at that restaurant. This allows the acquisition unit to provide highly relevant menu information based on geographical location. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's geographical location information into a generating AI and have the generating AI prioritize highly relevant menu information.

[0076] The acquisition unit can analyze the user's social media activity and acquire relevant menu information when acquiring menu information. For example, the acquisition unit can acquire relevant menu information based on meal information shared by the user on social media. The acquisition unit can also acquire relevant menu information based on information about accounts that the user follows on social media. Furthermore, the acquisition unit can acquire relevant menu information based on information about groups that the user participates in on social media. In this way, the acquisition unit can provide relevant menu information based on social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's social media activity data into a generating AI and have the generating AI perform the acquisition of relevant menu information.

[0077] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. For example, the analysis unit can perform a detailed analysis when the user is relaxed. The analysis unit can also perform a quick analysis when the user is in a hurry. Furthermore, the analysis unit can perform an analysis that adds visually stimulating effects when the user is excited. This allows the analysis unit to perform analysis using optimal criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0078] During analysis, the analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between allergens. For example, the analysis unit can identify menu items containing multiple allergens by taking into account the interrelationships between allergens. The analysis unit can also evaluate the risk of allergic reactions by taking into account the interrelationships between allergens. Furthermore, the analysis unit can create a list of menu items containing allergens by taking into account the interrelationships between allergens. In this way, the analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between allergens. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input interrelationship data between allergens into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0079] The analysis unit can perform the analysis while taking into account the user's past allergic reaction history. For example, the analysis unit identifies menu items containing allergenic ingredients based on the user's past allergic reaction history. The analysis unit can also evaluate the risk of an allergic reaction based on the user's past allergic reaction history. Furthermore, the analysis unit can create a list of menu items containing allergenic ingredients based on the user's past allergic reaction history. This allows the analysis unit to improve the accuracy of the analysis based on the past allergic reaction history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past allergic reaction history data into the generation AI and have the generation AI perform the analysis.

[0080] The analysis unit can estimate the user's emotions and adjust the display order of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can prioritize displaying the most important analysis results. Furthermore, 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 prioritize displaying the most important analysis results. This allows the analysis unit to display the analysis results in an optimal order according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0081] The analysis unit can perform the analysis while taking into account the geographical distribution of the menu items. For example, the analysis unit can identify allergens that are common in a specific region by taking into account the geographical distribution of the menu items. The analysis unit can also identify menu items that contain allergens that are common in a specific region by taking into account the geographical distribution of the menu items. Furthermore, the analysis unit can create a list of menu items that contain allergens that are common in a specific region by taking into account the geographical distribution of the menu items. This enables the analysis unit to perform analysis based on geographical distribution. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the geographical distribution data of the menu items into the generation AI and have the generation AI perform the analysis.

[0082] During analysis, the analysis unit can improve the accuracy of the analysis by referring to related literature. The analysis unit, for example, identifies allergenic ingredients by referring to related literature. The analysis unit can also evaluate the risk of allergic reactions by referring to related literature. Furthermore, the analysis unit can also create a list of menus containing allergenic ingredients by referring to related literature. This allows the analysis unit to improve the accuracy of the analysis by referring to related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0083] The service provider can estimate the user's emotions and adjust the way information is displayed based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the service provider can provide a concise display method. In this way, the service provider can provide the optimal display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0084] The providing unit can select the optimal display method by referring to the user's past allergic reaction history when providing the data. The providing unit, for example, highlights menu items containing allergenic ingredients based on the user's past allergic reaction history. The providing unit can also display menu items containing allergenic ingredients in different colors based on the user's past allergic reaction history. Furthermore, the providing unit can display menu items containing allergenic ingredients in list format based on the user's past allergic reaction history. This allows the providing unit to provide the optimal display method based on the past allergic reaction history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past allergic reaction history data into the generating AI and cause the generating AI to select the optimal display method.

[0085] The providing unit can customize the display of information based on the user's current health condition when providing the information. For example, when the user inputs their current health condition, the providing unit highlights menu items containing allergens based on the information. Furthermore, when the user inputs their current health condition, the providing unit can also display menu items containing allergens in a different color based on the information. Furthermore, when the user inputs their current health condition, the providing unit can also display menu items containing allergens in a list format based on the information. This enables the providing unit to display optimal information based on the current health condition. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current health condition data to the generating AI and cause the generating AI to display the information.

[0086] The information provider can estimate the user's emotions and determine the priority of the information to be provided based on the estimated emotions. For example, if the user is stressed, the information provider will prioritize displaying the most important information. If the user is relaxed, the information provider may also display more detailed information. Furthermore, if the user is in a hurry, the information provider may also prioritize displaying the most important information. This allows the information provider to provide information with priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0087] The service provider can provide appropriate information by considering the user's geographical location at the time of delivery. For example, if the user is in a specific region, the service provider can display menus that contain common allergens in that region. Furthermore, if the user is traveling, the service provider can display menus that contain common allergens in the destination region. Additionally, if the user is in a specific restaurant, the service provider can display allergy information related to the menu offered at that restaurant. This enables the service provider to provide optimal information based on geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location into a generating AI and have the generating AI provide appropriate information.

[0088] The service provider can analyze the user's social media activity and adjust how information is displayed at the time of delivery. For example, the service provider can display relevant allergy information based on the food information the user has shared on social media. It can also display relevant allergy information based on the accounts the user follows on social media. Furthermore, it can display relevant allergy information based on the groups the user participates in on social media. This enables the service provider to display information optimally based on social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI adjust how information is displayed. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, acquisition unit, analysis unit, and provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and allows a user to input food allergy information through a smartphone app. The acquisition unit includes a method of scanning a two-dimensional code using the camera 42 of the smart device 14 and a method of acquiring information from a restaurant's website using the communication I / F 26 of the data processing device 12. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and performs analysis using an algorithm that identifies allergens. The provision unit is realized by the control unit 46A of the smart device 14 and displays the analysis results on the smartphone app. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, acquisition unit, analysis unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and allows a user to input food allergy information through a smartphone app. The acquisition unit includes a method for scanning a two-dimensional code using the camera 42 of the smart glasses 214 and a method for acquiring information from a restaurant's website using the communication I / F 26 of the data processing device 12. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and performs analysis using an algorithm for identifying allergens. The provision unit is realized by the control unit 46A of the smart glasses 214 and displays the analysis results on the smartphone app. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, acquisition unit, analysis unit, and provision unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset terminal 314 and allows a user to input food allergy information through a smartphone app. The acquisition unit includes a method of scanning a two-dimensional code using the camera 42 of the headset terminal 314 and a method of acquiring information from a restaurant's website using the communication I / F 26 of the data processing device 12. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and performs analysis using an algorithm that identifies allergens. The provision unit is realized by the control unit 46A of the headset terminal 314 and displays the analysis results on the smartphone app. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, acquisition unit, analysis unit, and provision unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and allows the user to input food allergy information through a smartphone app. The acquisition unit includes a method of scanning a two-dimensional code using the camera 42 of the robot 414 and a method of acquiring information from a restaurant's website using the communication I / F 26 of the data processing device 12. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and performs analysis using an algorithm that identifies allergens. The provision unit is realized by the control unit 46A of the robot 414 and displays the analysis results on the smartphone app.

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

[0090] When a user inputs food allergy information, the reception unit can assist the user in inputting the information by taking into account the user's past dietary history. For example, the reception unit can automatically suggest menus that do not contain allergenic ingredients based on the dietary history previously input by the user. The reception unit can also warn the user about menus that may contain allergenic ingredients based on menus that the user has avoided in the past. Furthermore, the reception unit can analyze the user's past dietary history and suggest new menus that do not contain allergenic ingredients. This allows the reception unit to assist the user in inputting more appropriate allergy information based on the user's past dietary history.

[0091] The acquisition unit can acquire menu information taking into consideration the user's current health condition. For example, if the user inputs the results of a health check, the acquisition unit can prioritize acquisition of menus that do not contain allergens based on that information. In addition, if the user inputs their current health condition, the acquisition unit can also filter menus that do not contain allergens based on that information. Furthermore, if the user has a specific health condition, the acquisition unit can acquire menu information appropriate for that condition. This allows the acquisition unit to provide appropriate menu information based on the user's health condition.

[0092] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is relaxed, a detailed analysis can be performed. If the user is in a hurry, a rapid analysis can be performed. Furthermore, if the user is excited, an analysis with visually stimulating effects can be added. This allows the analysis unit to perform analysis using the optimal criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The providing unit can estimate the user's emotions and adjust the display method of the information to be provided based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This allows the providing unit to provide the optimal display method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0094] The reception unit can estimate the user's emotions and adjust the input method for allergy information based on the estimated user emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize input steps. Alternatively, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow the user to quickly input allergy information. This allows the reception unit to provide the optimal input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0095] The acquisition unit can analyze the user's past menu information acquisition history and select the optimal acquisition method. For example, it can prioritize acquisition of menu information from restaurants that the user has frequently visited in the past. The acquisition unit can also prioritize suggesting acquisition methods (two-dimensional code, website, etc.) that the user has used in the past. Furthermore, the acquisition unit can predict and suggest menu information that will be used during a specific time period based on the user's past acquisition history. This allows the acquisition unit to provide an efficient menu information acquisition method based on the past history.

[0096] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between allergens during analysis. For example, a menu containing multiple allergens can be identified by taking into account the interrelationships between allergens. The risk of an allergic reaction can also be evaluated by taking into account the interrelationships between allergens. Furthermore, a list of menus containing allergens can be created by taking into account the interrelationships between allergens. In this way, the analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between allergens.

[0097] When providing the information, the providing unit can select the optimal display method by referring to the user's past allergic reaction history. For example, menu items containing allergenic ingredients can be highlighted based on the user's past allergic reaction history. Also, menu items containing allergenic ingredients can be displayed in different colors based on the user's past allergic reaction history. Furthermore, menu items containing allergenic ingredients can be displayed in list format based on the user's past allergic reaction history. This allows the providing unit to provide the optimal display method based on the user's past allergic reaction history.

[0098] When inputting allergy information, the reception unit can filter the allergy information based on the user's current health condition and dietary history. For example, if the user inputs the results of a recent health check, the allergy information can be filtered based on that information. Also, if the user inputs their past dietary history, the allergy information can be filtered based on that information. Furthermore, if the user inputs their current health condition, the allergy information can be filtered based on that information. This allows the reception unit to provide appropriate allergy information based on the user's health condition and dietary history.

[0099] The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user's emotions. For example, if the user is nervous, the most important information can be displayed with priority. Also, if the user is relaxed, detailed information can be displayed. Furthermore, if the user is in a hurry, the most important information can be displayed with priority. This allows the providing unit to provide information with priority according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The reception desk allows users to enter their food allergy information. This information may include, for example, peanut allergies or dairy allergies. The reception desk enables users to enter their food allergy information via a smartphone app and can also save and reuse previously entered allergy information. Step 2: The acquisition unit retrieves restaurant menu information. This includes methods such as scanning a QR code or retrieving information from the restaurant's website. In the QR code scanning method, the user scans the QR code using their smartphone camera to retrieve the menu information. In the website retrieval method, the acquisition unit accesses the restaurant's website and retrieves the menu information. Step 3: The analysis unit analyzes the food allergy information entered by the reception unit and the menu information acquired by the acquisition unit. The analysis unit uses an algorithm to identify allergy components, compares the user's food allergy information with the menu information, and identifies menu items that contain allergy components. Step 4: The service provider provides the results analyzed by the analysis unit. The service provider displays the analysis results on a smartphone app in a visually easy-to-understand format to allow the user to intuitively understand them. For example, menu items containing allergens are displayed in red, and menu items that do not contain allergens are displayed in green.

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

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

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

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

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

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

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

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

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

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

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

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

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

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

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

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

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

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

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

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

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

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

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

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

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

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

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

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

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

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

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

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

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

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

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

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

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

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

[0146] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0149] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

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

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

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

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

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

[0156] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0157] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0158] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0159] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

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

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

[0162] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

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

[0164] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0165] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0166] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

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

[0168] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0169] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0170] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0171] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0172] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0173] [Explanation of symbols]

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

Claims

1. a reception desk for inputting food allergy information; an acquisition unit for acquiring restaurant menu information; an analysis unit that analyzes the food allergy information input by the reception unit and the menu information acquired by the acquisition unit; a providing unit that provides the results analyzed by the analyzing unit; Equipped with A system characterized by:

2. The acquisition unit Includes methods for scanning 2D codes and retrieving information from restaurant websites The system of claim 1 .

3. The analysis unit Analyze using an algorithm to identify allergens The system of claim 1 .

4. The providing unit Display analysis results on a smartphone app The system of claim 1 .

5. The reception unit The user enters peanut or dairy allergy information The system of claim 1 .

6. The reception unit Estimates the user's emotions and adjusts the input method for allergy information based on the estimated user emotions. The system of claim 1 .

7. The reception unit Analyze the user's past allergy information input history and select the appropriate input method The system of claim 1 .

8. The reception unit When entering allergy information, filtering is performed based on the user's current health condition and dietary history. The system of claim 1 .

9. The reception unit Estimate the user's emotions and prioritize the allergy information to be entered based on the estimated user emotions. The system of claim 1 .

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