System

Abstract

An object of a system according to an embodiment is to propose a menu or a restaurant menu in consideration of nutritional balance based on a health condition or a desired condition of a user.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, a provision unit, a collection unit, and a restaurant analysis unit. The reception unit inputs a health condition and a desired condition of the user. The analysis unit analyzes the information input by the reception unit, and proposes a menu in consideration of nutritional balance. The providing unit provides the menu proposed by the analyzing unit to the user. The collection unit collects dining information. The eating-out analysis unit analyzes the eating-out information collected by the collection unit and proposes an appropriate menu.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately propose appropriate menus and dining out options based on the user's health condition and desired conditions, and there is room for improvement.

[0005] The system according to the embodiment aims to propose menus and dining out dishes that take into consideration nutritional balance based on the user's health condition and desired conditions. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a provision unit, a collection unit, and an eating out analysis unit. The reception unit inputs the user's health condition and desired conditions. The analysis unit analyzes the information input by the reception unit and proposes a menu that takes nutritional balance into consideration. The provision unit provides the user with the menu proposed by the analysis unit. The collection unit collects eating out information. The eating out analysis unit analyzes the eating out information collected by the collection unit and proposes an appropriate menu. [Effects of the Invention]

[0007] The system according to the embodiment can propose menus and dining out dishes that take into consideration nutritional balance based on the user's health condition and desired conditions. [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 menu suggestion system according to an embodiment of the present invention is a system that suggests a menu that takes nutritional balance into consideration by inputting a user's health condition and desired conditions. This menu suggestion system allows a user to input their health condition and desired conditions, and AI analyzes this information to suggest a menu that takes nutritional balance into consideration. It also provides advice on what to eat even when the user wishes to eat out or have a meal at home. For example, if a user inputs that they are on a diet or trying to save money, the AI ​​analyzes this information and selects the optimal ingredients and dishes. Furthermore, when eating out, the AI ​​suggests menus that take calories and nutritional balance into consideration, and when having a meal at home, it suggests healthy dishes that are easy to prepare. This allows the menu suggestion system to easily select the optimal meal based on the user's health condition and desired conditions. For example, a user on a diet can effectively progress with their diet by referring to low-calorie menus suggested by the AI. Furthermore, a user trying to save money can eat a nutritionally balanced meal while keeping food costs down by referring to cost-effective menus suggested by the AI.

[0029] A menu suggestion system according to an embodiment includes a reception unit, an analysis unit, a provision unit, a collection unit, and an eating out analysis unit. The reception unit inputs a user's health condition and desired conditions. For example, the user can input that they are on a diet or trying to save money. The reception unit can also input, for example, the user's allergy information and intake restrictions for specific nutrients. The analysis unit uses AI to analyze the information input by the reception unit and proposes a menu that takes nutritional balance into consideration. For example, the AI ​​selects optimal ingredients and dishes based on the user's health condition and desired conditions. For example, the AI ​​may propose dishes using low-calorie ingredients to a user on a diet, and dishes using cost-effective ingredients to a user trying to save money. The provision unit provides the user with the menu proposed by the analysis unit. For example, the provision unit provides the user with recipes and cooking methods for the proposed menu. The collection unit collects eating out information. For example, the collection unit collects information on nearby restaurants. The eating out analysis unit analyzes the eating out information collected by the collection unit and proposes optimal menus. For example, the eating out analysis unit proposes menus that take calories and nutritional balance into consideration. As a result, the menu suggestion system according to the embodiment can suggest menus and dining out menus that take nutritional balance into consideration based on the user's health condition and desired conditions.

[0030] The reception unit can input the user's allergy information and specific nutrient intake restrictions. Allergy information includes, for example, allergies to specific ingredients and the severity of the allergy, but is not limited to such examples. The reception unit can, for example, allow the user to input allergy information to specific ingredients. The reception unit can also allow the user to input intake restrictions for specific nutrients. For example, the reception unit can allow the user to input intake restrictions for salt, sugar, and fat. This makes it possible to propose menus that take into account the user's allergy information and intake restrictions for specific nutrients.

[0031] The providing unit can provide the user with a recipe and cooking method for the proposed menu. The recipe includes, for example, but is not limited to, the types and amounts of ingredients, cooking procedures, and cooking time. The providing unit can provide, for example, the types and amounts of ingredients for the proposed menu to the user. The providing unit can also provide the user with cooking procedures for the proposed menu. The providing unit can also provide the user with cooking times for the proposed menu. The cooking methods include, for example, cooking techniques and procedures such as baking, boiling, and steaming, but are not limited to, for example. The providing unit can provide the user with baking procedures for the proposed menu. The providing unit can also provide the user with boiling procedures for the proposed menu. The providing unit can also provide the user with steaming procedures for the proposed menu. This allows the user to obtain information for specifically cooking the proposed menu.

[0032] The collection unit can collect restaurant information within a specified range. The specified range includes, but is not limited to, a geographical range, a distance, or an area. For example, the collection unit collects restaurant information within a geographical range specified by a user. The collection unit can also collect restaurant information within a distance specified by a user. Furthermore, the collection unit can collect restaurant information within an area specified by a user. This allows a user to find dining out options based on nearby restaurant information.

[0033] The reception unit can analyze the user's past health data and suggest optimal input items. The reception unit can automatically suggest related input items based on, for example, the health condition and desired conditions previously input by the user. The reception unit can also analyze the user's past health data and, if the intake of a specific nutrient needs to be restricted, can preferentially display that input item. Furthermore, the reception unit can suggest input items related to a specific health condition from the user's past health data. This improves input efficiency by suggesting optimal input items based on past health data.

[0034] The reception unit can perform filtering based on the user's current lifestyle habits when inputting health conditions or desired conditions. For example, when the user inputs current lifestyle habits, the reception unit filters and displays related items based on past data. The reception unit can also preferentially display input items related to specific health conditions or desired conditions based on the user's lifestyle habits. Furthermore, the reception unit can analyze the user's lifestyle habit data and customize and display input items. This allows for more appropriate input by filtering input items based on the user's current lifestyle habits.

[0035] When inputting the health condition or desired conditions, the reception unit can select the optimal input means according to the user's input method. For example, if the user selects voice input, the reception unit inputs the health condition or desired conditions using voice recognition technology. Furthermore, if the user selects text input, the reception unit can provide a text box to allow for easy input. Furthermore, if the user selects image input, the reception unit can also input the health condition or desired conditions using image recognition technology. This improves input efficiency by selecting the optimal input means according to the user's input method.

[0036] When inputting health conditions and desired conditions, the reception unit can prioritize input of highly relevant information taking into account the user's geographical location information. For example, if the user lives in a specific area, the reception unit can prioritize displaying health conditions and desired conditions related to that area. In addition, if the user is traveling, the reception unit can also suggest related health conditions and desired conditions based on the user's current location. Furthermore, the reception unit can prioritize displaying region-specific health conditions and desired conditions based on the user's geographical location information. This improves input efficiency by preferentially inputting highly relevant information taking into account geographical location information.

[0037] The reception unit can analyze the user's social media activity and input related information when inputting a health condition or desired conditions. The reception unit can automatically suggest related input items based on, for example, the health condition or desired conditions shared by the user on social media. The reception unit can also analyze the user's social media activity and preferentially display input items related to a specific health condition or desired condition. Furthermore, the reception unit can also suggest related health conditions or desired conditions by referring to the activity of the user's friends on social media. This improves input efficiency by analyzing social media activity and inputting related information.

[0038] The reception unit can customize the input method by reflecting the user's past feedback when inputting the health condition or desired conditions. For example, the reception unit customizes and displays the input method based on feedback provided by the user in the past. The reception unit can also analyze the user's past feedback and preferentially display input items related to a specific health condition or desired conditions. Furthermore, the reception unit can also optimize the input procedure by referring to the user's past feedback. In this way, customizing the input method by reflecting past feedback improves input efficiency.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the health state and desired conditions. For example, if the user's health state is important, the analysis unit provides detailed analysis results. Furthermore, if the user's desired conditions are important, the analysis unit can also provide detailed analysis results related to the desired conditions. Furthermore, the analysis unit can adjust the level of detail of the analysis results based on the importance of the user's health state and desired conditions. In this way, adjusting the level of detail of the analysis based on the importance of the health state and desired conditions improves the accuracy of the analysis results.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the category of health status or desired conditions. For example, the analysis unit can apply an analysis algorithm specialized for calorie calculation to a user on a diet. The analysis unit can also apply an analysis algorithm that emphasizes cost performance to a user who is trying to save money. Furthermore, the analysis unit can apply an analysis algorithm specialized for a specific nutrient to a user who has intake restrictions for that nutrient. In this way, applying an analysis algorithm according to the category improves the accuracy of the analysis results.

[0041] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit adjusts the current analysis result based on the user's past analysis results. The analysis unit can also analyze the user's past analysis results and find specific patterns to improve the accuracy of the analysis. Furthermore, the analysis unit can also optimize the analysis algorithm by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the past analysis results.

[0042] During analysis, the analysis unit can determine the priority of analysis based on the user's health condition and the time of submission of the desired conditions. For example, if the user is in a hurry, the analysis unit determines the priority of analysis based on the time of submission. Furthermore, if the user is relaxed, the analysis unit can adjust the priority of analysis based on the time of submission. Furthermore, the analysis unit can determine the priority of analysis based on the user's health condition and the time of submission of the desired conditions. In this way, by determining the priority of analysis based on the time of submission, the efficiency of analysis is improved.

[0043] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the health state and desired conditions. For example, if the user's health state is important, the analysis unit adjusts the order of analysis based on the relevance. Furthermore, if the user's desired conditions are important, the analysis unit can also adjust the order of analysis based on the relevance. Furthermore, the analysis unit can also adjust the order of analysis based on the relevance of the user's health state and desired conditions. In this way, adjusting the order of analysis based on the relevance improves the efficiency of the analysis.

[0044] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide the analysis results using detailed technical terms. Alternatively, if the user does not have technical expertise, the analysis unit can provide the analysis results in simple terms. Furthermore, the analysis unit can adjust the use of technical terms in the analysis results according to the user's level of expertise. This allows the user to better understand the analysis results by adjusting the use of technical terms in the analysis according to the user's level of expertise.

[0045] The providing unit can adjust the level of detail of the provision based on the importance of the proposed menu when providing the menu. For example, if the proposed menu is important, the providing unit selects a provision method that includes detailed information. Also, if the proposed menu is not important, the providing unit can select a provision method that includes concise information. Furthermore, the providing unit can adjust the level of detail of the provision based on the importance of the proposed menu. In this way, adjusting the level of detail of the provision based on the importance of the proposed menu improves the accuracy of the provision results.

[0046] When providing the menu, the providing unit can apply different providing algorithms depending on the category of the proposed menu. For example, the providing unit applies an algorithm that provides a low-calorie menu to a user who is on a diet. The providing unit can also apply an algorithm that provides a cost-effective menu to a user who is trying to save money. Furthermore, the providing unit can also apply an algorithm that provides a menu specialized for a specific nutrient to a user who has intake restrictions for that nutrient. In this way, by applying a providing algorithm according to the category, the accuracy of the provided results is improved.

[0047] The providing unit can improve the accuracy of the provision by referring to the user's past provision results when providing the information. For example, the providing unit adjusts the current provision results based on the user's past provision results. The providing unit can also analyze the user's past provision results and find specific patterns to improve the accuracy of the provision. Furthermore, the providing unit can also optimize the provision algorithm by referring to the user's past provision results. In this way, the accuracy of the provision is improved by referring to the past provision results.

[0048] The providing unit can determine the priority of provision based on the time of submission of the proposed menu at the time of provision. For example, if the user is in a hurry, the providing unit determines the priority of provision based on the time of submission. In addition, if the user is relaxed, the providing unit can also adjust the priority of provision based on the time of submission. Furthermore, the providing unit can also determine the priority of provision based on the user's health condition or the time of submission of desired conditions. In this way, determining the priority of provision based on the time of submission improves the efficiency of provision.

[0049] The providing unit can adjust the order of serving based on the relevance of the proposed menu when serving. For example, if the user's health condition is important, the providing unit adjusts the order of serving based on the relevance. Furthermore, if the user's desired conditions are important, the providing unit can also adjust the order of serving based on the relevance. Furthermore, the providing unit can also adjust the order of serving based on the relevance of the user's health condition or desired conditions. In this way, adjusting the order of serving based on the relevance improves the efficiency of serving.

[0050] The providing unit may adjust the use of technical terms in the provided results according to the user's level of expertise when providing the results. For example, if the user has technical expertise, the providing unit may provide the results using detailed technical terms. Also, if the user does not have technical expertise, the providing unit may provide the results in simple language. Furthermore, the providing unit may adjust the use of technical terms in the provided results according to the user's level of expertise. This allows the user to better understand the provided results by adjusting the use of technical terms in the provided results according to the user's level of expertise.

[0051] The collection unit can adjust the level of detail of the collection based on the importance of the dining out information when collecting the information. For example, if the dining out information is important, the collection unit collects detailed information. Also, if the dining out information is not important, the collection unit can collect concise information. Furthermore, the collection unit can adjust the level of detail of the collection based on the importance of the dining out information. This improves the accuracy of the collection results by adjusting the level of detail of the collection based on the importance of the dining out information.

[0052] When collecting, the collection unit can apply different collection algorithms depending on the category of dining out information. For example, the collection unit can apply an algorithm that collects low-calorie dining out information to a user who is on a diet. The collection unit can also apply an algorithm that collects cost-effective dining out information to a user who is trying to save money. Furthermore, the collection unit can also apply an algorithm that collects dining out information specialized for a specific nutrient to a user who has intake restrictions for that nutrient. In this way, by applying a collection algorithm according to the category, the accuracy of the collection results is improved.

[0053] The collection unit can improve the accuracy of collection by referring to the user's past collection results when collecting data. For example, the collection unit adjusts the current collection results based on the user's past collection results. The collection unit can also analyze the user's past collection results and find specific patterns to improve the accuracy of collection. Furthermore, the collection unit can also optimize the collection algorithm by referring to the user's past collection results. In this way, the accuracy of collection is improved by referring to the past collection results.

[0054] The collection unit can determine the collection priority based on the time of submission of the dining out information at the time of collection. For example, if the user is in a hurry, the collection unit determines the collection priority based on the time of submission. The collection unit can also adjust the collection priority based on the time of submission if the user is relaxed. Furthermore, the collection unit can also determine the collection priority based on the user's health condition or the time of submission of the desired conditions. In this way, by determining the collection priority based on the time of submission, the efficiency of collection is improved.

[0055] The collection unit can adjust the order of collection based on the relevance of dining out information when collecting the information. For example, if the user's health condition is important, the collection unit adjusts the order of collection based on the relevance. Furthermore, if the user's desired conditions are important, the collection unit can also adjust the order of collection based on the relevance. Furthermore, the collection unit can also adjust the order of collection based on the relevance of the user's health condition or desired conditions. In this way, adjusting the order of collection based on the relevance improves collection efficiency.

[0056] The collection unit can adjust the use of technical terms in the collection according to the user's level of expertise during collection. For example, if the user has technical expertise, the collection unit provides the collection results using detailed technical terms. Also, if the user does not have technical expertise, the collection unit can provide the collection results in simple terms. Furthermore, the collection unit can adjust the use of technical terms in the collection results according to the user's level of expertise. This allows the user to better understand the collection results by adjusting the use of technical terms in the collection according to the user's level of expertise.

[0057] When analyzing eating out, the eating out analysis unit can adjust the level of detail of the analysis based on the importance of the eating out information. For example, if the eating out information is important, the eating out analysis unit provides detailed analysis results. Also, if the eating out information is not important, the eating out analysis unit can provide concise analysis results. Furthermore, the eating out analysis unit can adjust the level of detail of the analysis based on the importance of the eating out information. In this way, adjusting the level of detail of the analysis based on the importance of the eating out information improves the accuracy of the analysis results.

[0058] When analyzing eating out, the eating out analysis unit can apply different analysis algorithms depending on the category of eating out information. For example, the eating out analysis unit can apply an algorithm that analyzes low-calorie eating out information to a user who is on a diet. The eating out analysis unit can also apply an algorithm that analyzes cost-effective eating out information to a user who is trying to save money. Furthermore, the eating out analysis unit can also apply an algorithm that analyzes eating out information specialized for that nutrient to a user who has intake restrictions for a specific nutrient. In this way, by applying an analysis algorithm according to the category, the accuracy of the analysis results can be improved.

[0059] When analyzing eating out, the eating out analysis unit can improve the accuracy of the analysis by referring to the user's past eating out analysis results. For example, the eating out analysis unit adjusts the current analysis results based on the user's past eating out analysis results. The eating out analysis unit can also analyze the user's past eating out analysis results and find specific patterns to improve the accuracy of the analysis. Furthermore, the eating out analysis unit can also optimize the analysis algorithm by referring to the user's past eating out analysis results. In this way, the accuracy of the analysis is improved by referring to the past eating out analysis results.

[0060] When analyzing dining out, the dining out analysis unit can determine the analysis priority based on the time of submission of dining out information. For example, if the user is in a hurry, the dining out analysis unit determines the analysis priority based on the time of submission. The dining out analysis unit can also adjust the analysis priority based on the time of submission if the user is relaxed. Furthermore, the dining out analysis unit can also determine the analysis priority based on the user's health condition or the time of submission of desired conditions. In this way, determining the analysis priority based on the time of submission improves the efficiency of the analysis.

[0061] When analyzing eating out, the eating out analysis unit can adjust the order of analysis based on the relevance of the eating out information. For example, if the user's health condition is important, the eating out analysis unit adjusts the order of analysis based on that relevance. Furthermore, if the user's desired conditions are important, the eating out analysis unit can also adjust the order of analysis based on that relevance. Furthermore, the eating out analysis unit can also adjust the order of analysis based on the relevance of the user's health condition and desired conditions. In this way, adjusting the order of analysis based on relevance improves the efficiency of the analysis.

[0062] The eating out analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise when analyzing eating out. For example, if the user has technical expertise, the eating out analysis unit provides the analysis results using detailed technical terms. Also, if the user does not have technical expertise, the eating out analysis unit can provide the analysis results in simple language. Furthermore, the eating out analysis unit can adjust the use of technical terms in the analysis results according to the user's level of expertise. This allows the user to better understand the analysis results by adjusting the use of technical terms in the analysis according to the user's level of expertise.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The provision unit can also analyze the user's past selection history and propose optimal menus. For example, it can analyze the user's tendency in choosing menus in the past and propose new menus that match their preferences. Also, if the user has a preference for a particular ingredient, it can preferentially propose menus that use that ingredient. Furthermore, it can take into account ingredients that the user has avoided in the past and propose menus that do not include those ingredients. In this way, satisfaction is improved by proposing optimal menus based on the user's past selection history.

[0065] The analysis unit can also suggest food preservation and cooking methods based on the user's health condition and desired conditions. For example, if the user wants to store fresh food for a long period of time, the analysis unit can suggest an appropriate preservation method. Also, if the user prefers a specific cooking method, the analysis unit can suggest food ingredients suitable for that method. Furthermore, if the user wants to consume a specific nutrient, the analysis unit can suggest food ingredients containing that nutrient and how to cook them. In this way, suggesting food preservation and cooking methods based on the user's health condition and desired conditions improves the quality of meals.

[0066] The collection unit can also collect information about ingredients and dishes from a specific region based on the user's geographical location information. For example, if the user is traveling, information about local specialties and dishes from that region can be collected. If the user lives in a specific region, information about ingredients and dishes available in that region can also be collected. Furthermore, if the user wants to try dishes from a specific region, information about recipes and cooking methods for that region can also be collected. In this way, collecting information about ingredients and dishes from a specific region based on geographical location information enriches the user's dining experience.

[0067] The providing unit can also automatically generate an ingredient shopping list based on the user's meal history. For example, it can list the necessary ingredients based on the ingredients of recipes the user has previously selected. If the user frequently uses a particular ingredient, it can prioritize that ingredient and add it to the list. Furthermore, if the user wants to avoid a particular ingredient, it can exclude that ingredient from the list. In this way, automatically generating an ingredient shopping list based on the user's meal history improves shopping efficiency.

[0068] The analysis unit can also suggest food preservation and cooking methods based on the user's health condition and desired conditions. For example, if the user wants to store fresh food for a long period of time, the analysis unit can suggest an appropriate preservation method. Also, if the user prefers a specific cooking method, the analysis unit can suggest food ingredients suitable for that method. Furthermore, if the user wants to consume a specific nutrient, the analysis unit can suggest food ingredients containing that nutrient and how to cook them. In this way, suggesting food preservation and cooking methods based on the user's health condition and desired conditions improves the quality of meals.

[0069] The processing flow of the first embodiment will be briefly explained below.

[0070] Step 1: The reception unit inputs the user's health condition and desired conditions. For example, the user can input information such as whether they are on a diet or trying to save money, allergy information, or intake restrictions for specific nutrients. Step 2: The analysis unit uses AI to analyze the information entered by the reception unit and proposes menus that take nutritional balance into consideration. For example, it will propose dishes using low-calorie ingredients to a user on a diet, and dishes using cost-effective ingredients to a user trying to save money. Step 3: The providing unit provides the user with the menu proposed by the analysis unit, for example, the recipe and cooking method of the proposed menu. Step 4: The collection unit collects dining out information, for example, information about nearby restaurants. Step 5: The dining out analysis unit analyzes the dining out information collected by the collection unit and proposes optimal menus, for example, menus that take into consideration calories and nutritional balance.

[0071] (Example 2) A menu suggestion system according to an embodiment of the present invention is a system that suggests a menu that takes nutritional balance into consideration by inputting a user's health condition and desired conditions. This menu suggestion system allows a user to input their health condition and desired conditions, and AI analyzes this information to suggest a menu that takes nutritional balance into consideration. It also provides advice on what to eat even when the user wishes to eat out or have a meal at home. For example, if a user inputs that they are on a diet or trying to save money, the AI ​​analyzes this information and selects the optimal ingredients and dishes. Furthermore, when eating out, the AI ​​suggests menus that take calories and nutritional balance into consideration, and when having a meal at home, it suggests healthy dishes that are easy to prepare. This allows the menu suggestion system to easily select the optimal meal based on the user's health condition and desired conditions. For example, a user on a diet can effectively progress with their diet by referring to low-calorie menus suggested by the AI. Furthermore, a user trying to save money can eat a nutritionally balanced meal while keeping food costs down by referring to cost-effective menus suggested by the AI.

[0072] A menu suggestion system according to an embodiment includes a reception unit, an analysis unit, a provision unit, a collection unit, and an eating out analysis unit. The reception unit inputs a user's health condition and desired conditions. For example, the user can input that they are on a diet or trying to save money. The reception unit can also input, for example, the user's allergy information and intake restrictions for specific nutrients. The analysis unit uses AI to analyze the information input by the reception unit and proposes a menu that takes nutritional balance into consideration. For example, the AI ​​selects optimal ingredients and dishes based on the user's health condition and desired conditions. For example, the AI ​​may propose dishes using low-calorie ingredients to a user on a diet, and dishes using cost-effective ingredients to a user trying to save money. The provision unit provides the user with the menu proposed by the analysis unit. For example, the provision unit provides the user with recipes and cooking methods for the proposed menu. The collection unit collects eating out information. For example, the collection unit collects information on nearby restaurants. The eating out analysis unit analyzes the eating out information collected by the collection unit and proposes optimal menus. For example, the eating out analysis unit proposes menus that take calories and nutritional balance into consideration. As a result, the menu suggestion system according to the embodiment can suggest menus and dining out menus that take nutritional balance into consideration based on the user's health condition and desired conditions.

[0073] The reception unit can input the user's allergy information and specific nutrient intake restrictions. Allergy information includes, for example, allergies to specific ingredients and the severity of the allergy, but is not limited to such examples. The reception unit can, for example, allow the user to input allergy information to specific ingredients. The reception unit can also allow the user to input intake restrictions for specific nutrients. For example, the reception unit can allow the user to input intake restrictions for salt, sugar, and fat. This makes it possible to propose menus that take into account the user's allergy information and intake restrictions for specific nutrients.

[0074] The providing unit can provide the user with a recipe and cooking method for the proposed menu. The recipe includes, for example, but is not limited to, the types and amounts of ingredients, cooking procedures, and cooking time. The providing unit can provide, for example, the types and amounts of ingredients for the proposed menu to the user. The providing unit can also provide the user with cooking procedures for the proposed menu. The providing unit can also provide the user with cooking times for the proposed menu. The cooking methods include, for example, cooking techniques and procedures such as baking, boiling, and steaming, but are not limited to, for example. The providing unit can provide the user with baking procedures for the proposed menu. The providing unit can also provide the user with boiling procedures for the proposed menu. The providing unit can also provide the user with steaming procedures for the proposed menu. This allows the user to obtain information for specifically cooking the proposed menu.

[0075] The collection unit can collect restaurant information within a specified range. The specified range includes, but is not limited to, a geographical range, a distance, or an area. For example, the collection unit collects restaurant information within a geographical range specified by a user. The collection unit can also collect restaurant information within a distance specified by a user. Furthermore, the collection unit can collect restaurant information within an area specified by a user. This allows a user to find dining out options based on nearby restaurant information.

[0076] The reception unit can estimate the user's emotions and adjust the input method for the health condition and desired conditions 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 the user to quickly input the health condition and desired conditions. This allows for more appropriate input by adjusting the input 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 can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0077] The reception unit can analyze the user's past health data and suggest optimal input items. The reception unit can automatically suggest related input items based on, for example, the health condition and desired conditions previously input by the user. The reception unit can also analyze the user's past health data and, if the intake of a specific nutrient needs to be restricted, can preferentially display that input item. Furthermore, the reception unit can suggest input items related to a specific health condition from the user's past health data. This improves input efficiency by suggesting optimal input items based on past health data.

[0078] The reception unit can perform filtering based on the user's current lifestyle habits when inputting health conditions or desired conditions. For example, when the user inputs current lifestyle habits, the reception unit filters and displays related items based on past data. The reception unit can also preferentially display input items related to specific health conditions or desired conditions based on the user's lifestyle habits. Furthermore, the reception unit can analyze the user's lifestyle habit data and customize and display input items. This allows for more appropriate input by filtering input items based on the user's current lifestyle habits.

[0079] When inputting the health condition or desired conditions, the reception unit can select the optimal input means according to the user's input method. For example, if the user selects voice input, the reception unit inputs the health condition or desired conditions using voice recognition technology. Furthermore, if the user selects text input, the reception unit can provide a text box to allow for easy input. Furthermore, if the user selects image input, the reception unit can also input the health condition or desired conditions using image recognition technology. This improves input efficiency by selecting the optimal input means according to the user's input method.

[0080] The reception unit can estimate the user's emotions and prioritize input items based on the estimated user emotions. For example, when the user is stressed, the reception unit can prioritize displaying important input items to simplify the input procedure. Furthermore, when the user is relaxed, the reception unit can display detailed input items and provide a customizable input method. Furthermore, when the user is in a hurry, the reception unit can prioritize displaying the most important input items to enable quick input. This improves input efficiency by prioritizing input items according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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.

[0081] When inputting health conditions and desired conditions, the reception unit can prioritize input of highly relevant information taking into account the user's geographical location information. For example, if the user lives in a specific area, the reception unit can prioritize displaying health conditions and desired conditions related to that area. In addition, if the user is traveling, the reception unit can also suggest related health conditions and desired conditions based on the user's current location. Furthermore, the reception unit can prioritize displaying region-specific health conditions and desired conditions based on the user's geographical location information. This improves input efficiency by preferentially inputting highly relevant information taking into account geographical location information.

[0082] The reception unit can analyze the user's social media activity and input related information when inputting a health condition or desired conditions. The reception unit can automatically suggest related input items based on, for example, the health condition or desired conditions shared by the user on social media. The reception unit can also analyze the user's social media activity and preferentially display input items related to a specific health condition or desired condition. Furthermore, the reception unit can also suggest related health conditions or desired conditions by referring to the activity of the user's friends on social media. This improves input efficiency by analyzing social media activity and inputting related information.

[0083] The reception unit can customize the input method by reflecting the user's past feedback when inputting the health condition or desired conditions. For example, the reception unit customizes and displays the input method based on feedback provided by the user in the past. The reception unit can also analyze the user's past feedback and preferentially display input items related to a specific health condition or desired conditions. Furthermore, the reception unit can also optimize the input procedure by referring to the user's past feedback. In this way, customizing the input method by reflecting past feedback improves input efficiency.

[0084] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results that focus on the main points. Furthermore, if the user is excited, the analysis unit can also provide analysis results with visually stimulating effects. This allows the analysis results to be better understood by adjusting the way the analysis is presented 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 can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the health state and desired conditions. For example, if the user's health state is important, the analysis unit provides detailed analysis results. Furthermore, if the user's desired conditions are important, the analysis unit can also provide detailed analysis results related to the desired conditions. Furthermore, the analysis unit can adjust the level of detail of the analysis results based on the importance of the user's health state and desired conditions. In this way, adjusting the level of detail of the analysis based on the importance of the health state and desired conditions improves the accuracy of the analysis results.

[0086] During analysis, the analysis unit can apply different analysis algorithms depending on the category of health status or desired conditions. For example, the analysis unit can apply an analysis algorithm specialized for calorie calculation to a user on a diet. The analysis unit can also apply an analysis algorithm that emphasizes cost performance to a user who is trying to save money. Furthermore, the analysis unit can apply an analysis algorithm specialized for a specific nutrient to a user who has intake restrictions for that nutrient. In this way, applying an analysis algorithm according to the category improves the accuracy of the analysis results.

[0087] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit adjusts the current analysis result based on the user's past analysis results. The analysis unit can also analyze the user's past analysis results and find specific patterns to improve the accuracy of the analysis. Furthermore, the analysis unit can also optimize the analysis algorithm by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the past analysis results.

[0088] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short, to-the-point analysis result. Alternatively, if the user is relaxed, the analysis unit can provide a longer analysis result with detailed explanations. Furthermore, if the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. This allows the analysis results to be better understood by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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.

[0089] During analysis, the analysis unit can determine the priority of analysis based on the user's health condition and the time of submission of the desired conditions. For example, if the user is in a hurry, the analysis unit determines the priority of analysis based on the time of submission. Furthermore, if the user is relaxed, the analysis unit can adjust the priority of analysis based on the time of submission. Furthermore, the analysis unit can determine the priority of analysis based on the user's health condition and the time of submission of the desired conditions. In this way, by determining the priority of analysis based on the time of submission, the efficiency of analysis is improved.

[0090] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the health state and desired conditions. For example, if the user's health state is important, the analysis unit adjusts the order of analysis based on the relevance. Furthermore, if the user's desired conditions are important, the analysis unit can also adjust the order of analysis based on the relevance. Furthermore, the analysis unit can also adjust the order of analysis based on the relevance of the user's health state and desired conditions. In this way, adjusting the order of analysis based on the relevance improves the efficiency of the analysis.

[0091] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide the analysis results using detailed technical terms. Alternatively, if the user does not have technical expertise, the analysis unit can provide the analysis results in simple terms. Furthermore, the analysis unit can adjust the use of technical terms in the analysis results according to the user's level of expertise. This allows the user to better understand the analysis results by adjusting the use of technical terms in the analysis according to the user's level of expertise.

[0092] The providing unit can estimate the user's emotions and adjust the presentation method based on the estimated user emotions. For example, if the user is relaxed, the providing unit can select a presentation method that includes detailed information. If the user is in a hurry, the providing unit can also select a concise presentation method that focuses on the main points. Furthermore, if the user is excited, the providing unit can also select a presentation method that adds a visually stimulating effect. This allows the presentation method to be adjusted according to the user's emotions, thereby deepening the understanding of the presentation results. Emotion estimation is achieved 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.

[0093] The providing unit can adjust the level of detail of the provision based on the importance of the proposed menu when providing the menu. For example, if the proposed menu is important, the providing unit selects a provision method that includes detailed information. Also, if the proposed menu is not important, the providing unit can select a provision method that includes concise information. Furthermore, the providing unit can adjust the level of detail of the provision based on the importance of the proposed menu. In this way, adjusting the level of detail of the provision based on the importance of the proposed menu improves the accuracy of the provision results.

[0094] When providing the menu, the providing unit can apply different providing algorithms depending on the category of the proposed menu. For example, the providing unit applies an algorithm that provides a low-calorie menu to a user who is on a diet. The providing unit can also apply an algorithm that provides a cost-effective menu to a user who is trying to save money. Furthermore, the providing unit can also apply an algorithm that provides a menu specialized for a specific nutrient to a user who has intake restrictions for that nutrient. In this way, by applying a providing algorithm according to the category, the accuracy of the provided results is improved.

[0095] The providing unit can improve the accuracy of the provision by referring to the user's past provision results when providing the information. For example, the providing unit adjusts the current provision results based on the user's past provision results. The providing unit can also analyze the user's past provision results and find specific patterns to improve the accuracy of the provision. Furthermore, the providing unit can also optimize the provision algorithm by referring to the user's past provision results. In this way, the accuracy of the provision is improved by referring to the past provision results.

[0096] The providing unit can estimate the user's emotions and adjust the length of the presentation based on the estimated user emotions. For example, if the user is in a hurry, the providing unit can select a short, to-the-point presentation method. If the user is relaxed, the providing unit can also select a longer presentation method including detailed explanations. Furthermore, if the user is excited, the providing unit can also select a presentation method that adds visually stimulating effects. This allows the length of the presentation to be adjusted according to the user's emotions, thereby deepening the understanding of the presentation results. Emotion estimation is achieved 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.

[0097] The providing unit can determine the priority of provision based on the time of submission of the proposed menu at the time of provision. For example, if the user is in a hurry, the providing unit determines the priority of provision based on the time of submission. In addition, if the user is relaxed, the providing unit can also adjust the priority of provision based on the time of submission. Furthermore, the providing unit can also determine the priority of provision based on the user's health condition or the time of submission of desired conditions. In this way, determining the priority of provision based on the time of submission improves the efficiency of provision.

[0098] The providing unit can adjust the order of serving based on the relevance of the proposed menu when serving. For example, if the user's health condition is important, the providing unit adjusts the order of serving based on the relevance. Furthermore, if the user's desired conditions are important, the providing unit can also adjust the order of serving based on the relevance. Furthermore, the providing unit can also adjust the order of serving based on the relevance of the user's health condition or desired conditions. In this way, adjusting the order of serving based on the relevance improves the efficiency of serving.

[0099] The providing unit may adjust the use of technical terms in the provided results according to the user's level of expertise when providing the results. For example, if the user has technical expertise, the providing unit may provide the results using detailed technical terms. Also, if the user does not have technical expertise, the providing unit may provide the results in simple language. Furthermore, the providing unit may adjust the use of technical terms in the provided results according to the user's level of expertise. This allows the user to better understand the provided results by adjusting the use of technical terms in the provided results according to the user's level of expertise.

[0100] The collection unit can estimate the user's emotions and adjust the collection method of dining out information based on the estimated user emotions. For example, when the user is relaxed, the collection unit collects detailed dining out information. When the user is in a hurry, the collection unit can also collect concise dining out information that focuses on the main points. Furthermore, when the user is excited, the collection unit can also collect dining out information with visually stimulating effects. This improves the accuracy of the collection results by adjusting the collection method of dining out information 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.

[0101] The collection unit can adjust the level of detail of the collection based on the importance of the dining out information when collecting the information. For example, if the dining out information is important, the collection unit collects detailed information. Also, if the dining out information is not important, the collection unit can collect concise information. Furthermore, the collection unit can adjust the level of detail of the collection based on the importance of the dining out information. This improves the accuracy of the collection results by adjusting the level of detail of the collection based on the importance of the dining out information.

[0102] When collecting, the collection unit can apply different collection algorithms depending on the category of dining out information. For example, the collection unit can apply an algorithm that collects low-calorie dining out information to a user who is on a diet. The collection unit can also apply an algorithm that collects cost-effective dining out information to a user who is trying to save money. Furthermore, the collection unit can also apply an algorithm that collects dining out information specialized for a specific nutrient to a user who has intake restrictions for that nutrient. In this way, by applying a collection algorithm according to the category, the accuracy of the collection results is improved.

[0103] The collection unit can improve the accuracy of collection by referring to the user's past collection results when collecting data. For example, the collection unit adjusts the current collection results based on the user's past collection results. The collection unit can also analyze the user's past collection results and find specific patterns to improve the accuracy of collection. Furthermore, the collection unit can also optimize the collection algorithm by referring to the user's past collection results. In this way, the accuracy of collection is improved by referring to the past collection results.

[0104] The collection unit can estimate the user's emotions and determine the priority of dining out information to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting relaxing dining out information. Furthermore, if the user is relaxed, the collection unit can also prioritize collecting detailed dining out information. Furthermore, if the user is in a hurry, the collection unit can also prioritize collecting concise dining out information that covers the main points. This improves the efficiency of collection by prioritizing dining out information 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.

[0105] The collection unit can determine the collection priority based on the time of submission of the dining out information at the time of collection. For example, if the user is in a hurry, the collection unit determines the collection priority based on the time of submission. The collection unit can also adjust the collection priority based on the time of submission if the user is relaxed. Furthermore, the collection unit can also determine the collection priority based on the user's health condition or the time of submission of the desired conditions. In this way, by determining the collection priority based on the time of submission, the efficiency of collection is improved.

[0106] The collection unit can adjust the order of collection based on the relevance of dining out information when collecting the information. For example, if the user's health condition is important, the collection unit adjusts the order of collection based on the relevance. Furthermore, if the user's desired conditions are important, the collection unit can also adjust the order of collection based on the relevance. Furthermore, the collection unit can also adjust the order of collection based on the relevance of the user's health condition or desired conditions. In this way, adjusting the order of collection based on the relevance improves collection efficiency.

[0107] The collection unit can adjust the use of technical terms in the collection according to the user's level of expertise during collection. For example, if the user has technical expertise, the collection unit provides the collection results using detailed technical terms. Also, if the user does not have technical expertise, the collection unit can provide the collection results in simple terms. Furthermore, the collection unit can adjust the use of technical terms in the collection results according to the user's level of expertise. This allows the user to better understand the collection results by adjusting the use of technical terms in the collection according to the user's level of expertise.

[0108] The dining out analysis unit can estimate the user's emotions and adjust the presentation of the dining out analysis based on the estimated user emotions. For example, if the user is relaxed, the dining out analysis unit can provide detailed analysis results. If the user is in a hurry, the dining out analysis unit can also provide concise analysis results that focus on the main points. Furthermore, if the user is excited, the dining out analysis unit can also provide analysis results with visually stimulating effects. This allows the analysis results to be better understood by adjusting the presentation of the dining out analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0109] When analyzing eating out, the eating out analysis unit can adjust the level of detail of the analysis based on the importance of the eating out information. For example, if the eating out information is important, the eating out analysis unit provides detailed analysis results. Also, if the eating out information is not important, the eating out analysis unit can provide concise analysis results. Furthermore, the eating out analysis unit can adjust the level of detail of the analysis based on the importance of the eating out information. In this way, adjusting the level of detail of the analysis based on the importance of the eating out information improves the accuracy of the analysis results.

[0110] When analyzing eating out, the eating out analysis unit can apply different analysis algorithms depending on the category of eating out information. For example, the eating out analysis unit can apply an algorithm that analyzes low-calorie eating out information to a user who is on a diet. The eating out analysis unit can also apply an algorithm that analyzes cost-effective eating out information to a user who is trying to save money. Furthermore, the eating out analysis unit can also apply an algorithm that analyzes eating out information specialized for that nutrient to a user who has intake restrictions for a specific nutrient. In this way, by applying an analysis algorithm according to the category, the accuracy of the analysis results can be improved.

[0111] When analyzing eating out, the eating out analysis unit can improve the accuracy of the analysis by referring to the user's past eating out analysis results. For example, the eating out analysis unit adjusts the current analysis results based on the user's past eating out analysis results. The eating out analysis unit can also analyze the user's past eating out analysis results and find specific patterns to improve the accuracy of the analysis. Furthermore, the eating out analysis unit can also optimize the analysis algorithm by referring to the user's past eating out analysis results. In this way, the accuracy of the analysis is improved by referring to the past eating out analysis results.

[0112] The dining out analysis unit can estimate the user's emotions and adjust the length of the dining out analysis based on the estimated user emotions. For example, if the user is in a hurry, the dining out analysis unit can provide a short, to-the-point analysis result. Furthermore, if the user is relaxed, the dining out analysis unit can provide a longer analysis result with detailed explanations. Furthermore, if the user is excited, the dining out analysis unit can provide an analysis result with visually stimulating effects. This allows the length of the dining out analysis to be adjusted according to the user's emotions, thereby deepening the understanding of the analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0113] When analyzing dining out, the dining out analysis unit can determine the analysis priority based on the time of submission of dining out information. For example, if the user is in a hurry, the dining out analysis unit determines the analysis priority based on the time of submission. The dining out analysis unit can also adjust the analysis priority based on the time of submission if the user is relaxed. Furthermore, the dining out analysis unit can also determine the analysis priority based on the user's health condition or the time of submission of desired conditions. In this way, determining the analysis priority based on the time of submission improves the efficiency of the analysis.

[0114] When analyzing eating out, the eating out analysis unit can adjust the order of analysis based on the relevance of the eating out information. For example, if the user's health condition is important, the eating out analysis unit adjusts the order of analysis based on that relevance. Furthermore, if the user's desired conditions are important, the eating out analysis unit can also adjust the order of analysis based on that relevance. Furthermore, the eating out analysis unit can also adjust the order of analysis based on the relevance of the user's health condition and desired conditions. In this way, adjusting the order of analysis based on relevance improves the efficiency of the analysis.

[0115] The eating out analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise when analyzing eating out. For example, if the user has technical expertise, the eating out analysis unit provides the analysis results using detailed technical terms. Also, if the user does not have technical expertise, the eating out analysis unit can provide the analysis results in simple language. Furthermore, the eating out analysis unit can adjust the use of technical terms in the analysis results according to the user's level of expertise. This allows the user to better understand the analysis results by adjusting the use of technical terms in the analysis according to the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, provision unit, collection unit, and eating out analysis unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and inputs the user's health condition and desired conditions. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information using AI. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the user with a suggested menu. The collection unit collects eating out information using, for example, the camera 42 and communication I / F 44 of the smart device 14. The eating out analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected eating out information. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, provision unit, collection unit, and eating out analysis unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and inputs the user's health condition and desired conditions. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information using AI. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the user with a suggested menu. The collection unit collects eating out information using, for example, the camera 42 and communication I / F 44 of the smart glasses 214. The eating out analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected eating out information. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, analysis unit, provision unit, collection unit, and eating out analysis unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset terminal 314 and inputs the user's health condition and desired conditions. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information using AI. The provision unit is realized, for example, by the control unit 46A of the headset terminal 314 and provides the user with a suggested menu. The collection unit collects eating out information using, for example, the camera 42 and communication I / F 44 of the headset terminal 314. The eating out analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected eating out information. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, provision unit, collection unit, and eating out analysis unit, described above, 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 inputs the user's health condition and desired conditions. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information using AI. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the user with a suggested menu. The collection unit collects eating out information using, for example, the camera 42 and communication I / F 44 of the robot 414. The eating out analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected eating out information.

[0116] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0117] The analysis unit can also estimate the user's emotions and adjust the analysis priority based on the estimated user emotions. For example, if the user is feeling stressed, important analysis items can be prioritized and results can be provided quickly. If the user is relaxed, detailed analysis can be performed and comprehensive results can be provided. Furthermore, if the user is excited, visually appealing analysis results can be provided. In this way, adjusting the analysis priority according to the user's emotions deepens the user's understanding of the analysis results.

[0118] The provision unit can also analyze the user's past selection history and propose optimal menus. For example, it can analyze the user's tendency in choosing menus in the past and propose new menus that match their preferences. Also, if the user has a preference for a particular ingredient, it can preferentially propose menus that use that ingredient. Furthermore, it can take into account ingredients that the user has avoided in the past and propose menus that do not include those ingredients. In this way, satisfaction is improved by proposing optimal menus based on the user's past selection history.

[0119] The collection unit can also estimate the user's emotions and adjust the collection range of dining out information based on the estimated user emotions. For example, if the user is feeling stressed, it can prioritize collecting restaurant information with a relaxing atmosphere. Also, if the user is relaxed, it can collect detailed dining out information and provide a wide range of options. Furthermore, if the user is in a hurry, it can quickly collect information on nearby restaurants. In this way, adjusting the collection range of dining out information according to the user's emotions improves the accuracy of the collection results.

[0120] The analysis unit can also suggest food preservation and cooking methods based on the user's health condition and desired conditions. For example, if the user wants to store fresh food for a long period of time, the analysis unit can suggest an appropriate preservation method. Also, if the user prefers a specific cooking method, the analysis unit can suggest food ingredients suitable for that method. Furthermore, if the user wants to consume a specific nutrient, the analysis unit can suggest food ingredients containing that nutrient and how to cook them. In this way, suggesting food preservation and cooking methods based on the user's health condition and desired conditions improves the quality of meals.

[0121] The providing unit can also estimate the user's emotions and adjust the format of the information to be provided based on the estimated user's emotions. For example, if the user is feeling stressed, concise and to the point information can be provided. If the user is relaxed, detailed information can be provided to encourage deeper understanding. Furthermore, if the user is excited, information can be provided in a visually appealing format. In this way, by adjusting the format of information according to the user's emotions, information acceptability can be improved.

[0122] The collection unit can also collect information about ingredients and dishes from a specific region based on the user's geographical location information. For example, if the user is traveling, information about local specialties and dishes from that region can be collected. If the user lives in a specific region, information about ingredients and dishes available in that region can also be collected. Furthermore, if the user wants to try dishes from a specific region, information about recipes and cooking methods for that region can also be collected. In this way, collecting information about ingredients and dishes from a specific region based on geographical location information enriches the user's dining experience.

[0123] The analysis unit can also estimate the user's emotions and adjust the presentation method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide concise and to-the-point analysis results. If the user is relaxed, the analysis unit can provide detailed analysis results to encourage deeper understanding. Furthermore, if the user is excited, the analysis results can be presented in a visually appealing format. In this way, by adjusting the presentation method of the analysis results according to the user's emotions, the acceptability of the analysis results can be improved.

[0124] The providing unit can also automatically generate an ingredient shopping list based on the user's meal history. For example, it can list the necessary ingredients based on the ingredients of recipes the user has previously selected. If the user frequently uses a particular ingredient, it can prioritize that ingredient and add it to the list. Furthermore, if the user wants to avoid a particular ingredient, it can exclude that ingredient from the list. In this way, automatically generating an ingredient shopping list based on the user's meal history improves shopping efficiency.

[0125] The collection unit can also estimate the user's emotions and adjust the method of collecting dining out information based on the estimated user emotions. For example, if the user is feeling stressed, it can prioritize collecting restaurant information with a relaxing atmosphere. Also, if the user is relaxed, it can collect detailed dining out information and provide a wide range of options. Furthermore, if the user is in a hurry, it can quickly collect information on nearby restaurants. In this way, adjusting the method of collecting dining out information according to the user's emotions improves the accuracy of the collection results.

[0126] The analysis unit can also suggest food preservation and cooking methods based on the user's health condition and desired conditions. For example, if the user wants to store fresh food for a long period of time, the analysis unit can suggest an appropriate preservation method. Also, if the user prefers a specific cooking method, the analysis unit can suggest food ingredients suitable for that method. Furthermore, if the user wants to consume a specific nutrient, the analysis unit can suggest food ingredients containing that nutrient and how to cook them. In this way, suggesting food preservation and cooking methods based on the user's health condition and desired conditions improves the quality of meals.

[0127] The processing flow of the second embodiment will be briefly explained below.

[0128] Step 1: The reception unit inputs the user's health condition and desired conditions. For example, the user can input information such as whether they are on a diet or trying to save money, allergy information, or intake restrictions for specific nutrients. Step 2: The analysis unit uses AI to analyze the information entered by the reception unit and proposes menus that take nutritional balance into consideration. For example, it will propose dishes using low-calorie ingredients to a user on a diet, and dishes using cost-effective ingredients to a user trying to save money. Step 3: The providing unit provides the user with the menu proposed by the analysis unit, for example, the recipe and cooking method of the proposed menu. Step 4: The collection unit collects dining out information, for example, information about nearby restaurants. Step 5: The dining out analysis unit analyzes the dining out information collected by the collection unit and proposes optimal menus, for example, menus that take into consideration calories and nutritional balance.

[0129] 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.

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

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0134] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0147] 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.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0163] 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.

[0164] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0165] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0166] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0180] 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.

[0181] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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).

[0186] 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.

[0187] 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."

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] [Explanation of symbols]

[0201] 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 unit for inputting the user's health condition and desired conditions; an analysis unit that analyzes the information input by the reception unit and proposes a menu that takes nutritional balance into consideration; a providing unit that provides a menu suggested by the analysis unit to a user; a collection department that collects dining out information; and a dining out analysis unit that analyzes the dining out information collected by the collection unit and proposes an appropriate menu. A system characterized by:

2. The reception unit Enter the user's allergy information and specific nutrient intake restrictions 2. The system of claim 1.

3. The providing unit Providing users with recipes and cooking instructions for suggested meals 2. The system of claim 1.

4. The collecting unit Collect restaurant information within a specified range 2. The system of claim 1.

5. The reception unit Estimate the user's emotions and adjust the input method for health status and desired conditions based on the estimated user emotions.

2. The system of claim 1.

6. The reception unit Analyzes the user's past health data and suggests the most appropriate input items 2. The system of claim 1.

7. The reception unit When entering health conditions and desired conditions, filtering is performed based on the user's current lifestyle.

2. The system of claim 1.

8. The reception unit When entering health conditions or desired conditions, the optimal input method is selected according to the user's input method.

2. The system of claim 1.

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