System

Abstract

An object of the system according to the embodiment is to propose an appropriate meal based on a preference of a user for food.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, a proposal unit, and a provision unit. The reception unit receives a preference of food of the user. The analysis unit analyzes the data input by the reception unit and grasps the preference of the user. The suggestion unit suggests an appropriate meal based on the preference of the user grasped by the analysis unit. The providing unit provides the meal proposed by the proposing unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques do not adequately suggest appropriate meals based on a user's food preferences, and there is room for improvement.

[0005] The system according to the embodiment aims to suggest appropriate meals based on the user's food preferences. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a suggestion unit, and a provision unit. The reception unit inputs a user's food preferences. The analysis unit analyzes the data input by the reception unit and identifies the user's preferences. The suggestion unit suggests an appropriate meal based on the user's preferences identified by the analysis unit. The provision unit provides the user with the meal suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest appropriate meals based on the user's food preferences. [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 meal recommendation system according to an embodiment of the present invention inputs a user's food preferences into a database, analyzes the data using AI to understand the user's preferences, and suggests optimal meals for travel destinations. In the meal recommendation system, the user inputs their usual food preferences and favorite foods into a database, and the AI ​​analyzes the data to understand the user's preferences. Next, when the user searches for food at a travel destination, the AI ​​suggests optimal meals based on the user's preferences. These suggestions are made taking into account the user's current location and travel destination information. For example, the meal recommendation system allows the user to input their usual food preferences and favorite foods into a database. For example, specific dish names, ingredients, and flavor characteristics can be input. Next, the meal recommendation system uses AI to analyze the input data and understand the user's food preferences. Based on the input data, the AI ​​learns the user's preference patterns and understands the type of food the user prefers. For example, if the user prefers spicy food, the AI ​​makes suggestions based on that information. Next, when the user searches for food at a travel destination, the AI ​​suggests optimal meals based on the user's preferences. For example, when the user searches for restaurants at a travel destination, the AI ​​suggests restaurants and dishes that match the user's preferences. These suggestions are made taking into consideration information about the user's current location and travel destination, allowing the user to enjoy meals that suit their preferences even when traveling. This allows the meal suggestion system to easily find meals that suit their preferences even when traveling, simply by having the user input their usual food preferences into the database. This allows the meal suggestion system to easily find meals that suit their preferences even when traveling, simply by having the user input their usual food preferences into the database. For example, if a user normally likes spicy food, restaurants that serve spicy food can be suggested even when traveling. Also, if a user likes sweet desserts, cafes that serve delicious desserts can be suggested at the travel destination. In this way, users can enjoy meals that suit their preferences even when traveling.

[0029] A meal suggestion system according to an embodiment includes a reception unit, an analysis unit, a suggestion unit, and a provision unit. The reception unit inputs a user's food preferences. The user's food preferences include, but are not limited to, favorite types of dishes, allergy information, and meal frequency. The reception unit, for example, inputs the user's usual food preferences and delicious foods into a database. The reception unit also allows the user to input specific dish names, ingredients, and flavor characteristics. For example, the user inputs information such as "spicy curry" or "sweet dessert." The analysis unit uses AI to analyze the data input by the reception unit and understand the user's preferences. For example, the analysis unit learns the user's preference patterns based on the input data and understands the type of food the user prefers. For example, the analysis unit analyzes the user's preferences using methods such as frequency analysis, co-occurrence analysis, and clustering. The suggestion unit suggests optimal meals based on the user's preferences identified by the analysis unit. For example, the suggestion unit considers the user's current location and travel destination information to suggest restaurants and dishes that match the user's preferences. For example, when a user is looking for a restaurant at a travel destination, the suggestion unit suggests restaurants and dishes that suit the user's preferences. The provision unit provides the user with the meals suggested by the suggestion unit. For example, the provision unit provides the user with information about the suggested meals. The provision unit provides the user with menu information, nutritional information, price information, and the like. In this way, the meal suggestion system according to the embodiment can suggest optimal meals at a travel destination, etc., based on the user's food preferences. For example, if a user normally likes spicy food, restaurants that serve spicy food can be suggested at the travel destination. Furthermore, if a user likes sweet desserts, cafes that serve delicious desserts can be suggested at the travel destination. In this way, users can enjoy meals that suit their preferences even when traveling.

[0030] The reception unit allows the user to input their usual food preferences or what they find delicious into the database. Usual food preferences include, for example, the types of food eaten daily and taste preferences, but are not limited to these examples. The reception unit, for example, allows the user to input their usual food preferences and what they find delicious into the database. What they find delicious includes, for example, the names of dishes eaten in the past, the names of restaurants, and photos of dishes, but are not limited to these examples. By inputting their usual food preferences into the database, the system can perform analysis based on that information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the data entered by the user into AI, which then analyzes the data.

[0031] The analysis unit learns the user's preference patterns based on the input data and can determine what types of food the user prefers. The analysis unit learns the user's preference patterns using, for example, frequency analysis. For example, the analysis unit analyzes the types of dishes the user frequently eats. The analysis unit can also learn the user's preference patterns using co-occurrence analysis. For example, the analysis unit analyzes the user's favorite combinations of dishes. The analysis unit can also learn the user's preference patterns using clustering. For example, the analysis unit classifies the user's preferences into several clusters and understands the characteristics of each cluster. This allows the user's preference patterns to be learned, enabling more accurate suggestions. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's input data into AI, which then analyzes the data.

[0032] The suggestion unit can suggest appropriate meals based on the user's preferences, based on information about the user's current location or travel destination. The suggestion unit, for example, acquires the user's current location using GPS data and makes suggestions based on that information. For example, the suggestion unit suggests restaurants near the user's current location. The suggestion unit can also make suggestions based on information about the user's travel destination. For example, the suggestion unit suggests restaurants at the travel destination based on the user's travel plan information and reservation information. The suggestion unit can also make suggestions based on information input by the user. For example, when the user inputs information about the travel destination, the suggestion unit makes suggestions based on that information. This makes it possible to suggest more appropriate meals by taking into account information about the user's current location and travel destination. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input information about the user's current location and travel destination into AI, which then makes suggestions.

[0033] The providing unit can provide information about the proposed meal to the user. The providing unit, for example, provides menu information about the proposed meal to the user. For example, the providing unit displays menu information about the proposed restaurant. The providing unit can also provide nutritional information about the proposed meal to the user. For example, the providing unit displays the nutritional components of the proposed dish. The providing unit can also provide price information about the proposed meal to the user. For example, the providing unit displays price information about the proposed restaurant. By providing the user with information about the proposed meal, the user can select a meal based on that information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input information about the proposed meal to AI, which then provides the information.

[0034] The suggestion unit can analyze reviews or ratings of restaurants at travel destinations and reflect them in suggestions. The suggestion unit, for example, acquires and analyzes data from review sites for restaurants at travel destinations. For example, the suggestion unit analyzes user evaluation comments and star ratings. The suggestion unit can also analyze evaluation criteria for restaurants at travel destinations. For example, the suggestion unit analyzes star ratings, score ratings, rankings, etc. The suggestion unit can also make suggestions based on data from review sites. For example, the suggestion unit suggests restaurants that suit the user's preferences based on data from review sites. This enables more reliable suggestions by analyzing reviews and ratings of restaurants at travel destinations. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data from review sites into AI, which then analyzes the data.

[0035] The analysis unit can also analyze the user's dining situation or past ratings and reflect them in the suggestions. The analysis unit, for example, analyzes the user's dining situation. For example, the analysis unit analyzes the time of day, location, and accompanying persons of the meal. The analysis unit can also analyze the user's past ratings. For example, the analysis unit analyzes past reviews, star ratings, feedback comments, and the like. The analysis unit can also make suggestions based on the user's dining situation and past ratings. For example, the analysis unit suggests meals that match the user's preferences based on the user's dining situation and past ratings. This enables more personalized suggestions by analyzing the user's dining situation and past ratings. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's dining situation and past ratings into AI, which then analyzes the data.

[0036] The reception unit can analyze the user's past meal history and select the optimal input method. For example, the reception unit can automatically display as candidates the types of meals that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest the types of meals to be eaten during a specific time period based on the user's past input history. In this way, by analyzing the user's past meal history, the optimal input method for the user can be provided. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past meal history into AI, which then analyzes the data.

[0037] When inputting food preferences, the reception unit can filter based on the user's current dietary situation or health condition. For example, if the user is on a diet, the reception unit can prioritize low-calorie meals. Furthermore, if the user has allergies, the reception unit can also suggest meals that do not contain allergens. Furthermore, if the user wants to consume a specific nutrient, the reception unit can also suggest meals that are high in that nutrient. Thus, filtering based on the user's current dietary situation or health condition enables more appropriate suggestions. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's health data into AI, which then analyzes the data.

[0038] When inputting food preferences, the reception unit can select the optimal input means according to the user's input method. For example, if the user prefers voice input, the reception unit can preferentially suggest voice input. Furthermore, if the user prefers text input, the reception unit can also preferentially suggest text input. Furthermore, if the user prefers image input, the reception unit can also suggest an input method using image recognition. This improves input convenience by selecting the optimal input means according to the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data into AI, which can analyze the data.

[0039] When inputting food preferences, the reception unit can prioritize inputting highly relevant preferences by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize suggesting local specialties. Furthermore, if the user is traveling, the reception unit can prioritize suggesting popular dishes at the travel destination. Furthermore, if the user is at home, the reception unit can prioritize suggesting menus from nearby restaurants. In this way, by taking the user's geographical location information into consideration, more relevant preferences can be prioritized and input. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to AI, which can then analyze the data.

[0040] When inputting food preferences, the reception unit can analyze the user's social media activity and input related preferences. For example, the reception unit can automatically display dishes shared by the user on social media as candidates. The reception unit can also analyze the content of the user's social media posts and suggest related dishes. The reception unit can also suggest related dishes by referring to the activities of the user's friends on social media. In this way, related preferences can be input by analyzing the user's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into AI, which then analyzes the data.

[0041] The reception unit can customize the input method by reflecting the user's past feedback when inputting food preferences. For example, the reception unit preferentially suggests input methods that the user has previously preferred. The reception unit can also customize the input interface based on the user's past feedback. The reception unit can also optimize the input procedure based on the user's past feedback. This allows the input method to be customized by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into AI, which can then analyze the data.

[0042] During analysis, the analysis unit can adjust the level of detail of the analysis based on the priority of food preferences. For example, the analysis unit performs a detailed analysis of ingredients that the user particularly likes. The analysis unit can also simplify the analysis of ingredients that the user is not particularly interested in. The analysis unit can also perform a detailed analysis of dishes that the user eats frequently. This allows for more appropriate analysis by adjusting the level of detail of the analysis based on the importance of food preferences. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's food preference data into AI, which then analyzes the data.

[0043] The analysis unit can apply different analysis algorithms depending on the food category during analysis. For example, the analysis unit can apply algorithms related to sweetness and calories to the analysis of sweets. The analysis unit can also apply algorithms related to spiciness and types of spices to the analysis of spicy dishes. The analysis unit can also apply algorithms related to nutrients and calories to the analysis of healthy dishes. In this way, applying different analysis algorithms depending on the food category enables more accurate analysis. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input food category data into AI, which then analyzes the data.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit improves the accuracy of the analysis, for example, based on the user's past preferred analysis results. The analysis unit can also adjust the analysis algorithm based on the user's past feedback. The analysis unit can also adjust the level of detail of the analysis based on the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into AI, which then analyzes the data.

[0045] During analysis, the analysis unit can determine the priority of analysis based on the time when the food preferences were input. For example, the analysis unit prioritizes analysis of food preferences recently input by the user. The analysis unit can also prioritize analysis of food preferences input by the user before a specific event. The analysis unit can also prioritize analysis of food preferences input by the user before a trip. This enables more appropriate analysis by determining the priority of analysis based on the time when the food preferences were submitted. Some or all of the above-mentioned processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time when the user's food preferences were input into AI, and the AI ​​can analyze the data.

[0046] During analysis, the analysis unit can adjust the order of analysis based on food relevance. For example, the analysis unit prioritizes analysis of dishes related to ingredients preferred by the user. The analysis unit can also prioritize analysis of ingredients related to a specific dish preferred by the user. The analysis unit can also prioritize analysis of new dishes related to dishes preferred by the user in the past. This allows for more appropriate analysis by adjusting the order of analysis based on food relevance. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's food relevance data into AI, which then analyzes the data.

[0047] 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 is a cooking expert, the analysis unit can use detailed technical terms. If the user is a beginner cook, the analysis unit can also use simple terms. If the user is an intermediate cook, the analysis unit can also use appropriate technical terms. This allows for more appropriate analysis by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's level of expertise data into AI, which can then analyze the data.

[0048] When making a suggestion, the suggestion unit can adjust the level of detail of the suggestion based on the priority of the meal. For example, the suggestion unit makes detailed suggestions regarding ingredients that the user particularly likes. The suggestion unit can also simplify suggestions regarding ingredients that the user is not particularly interested in. The suggestion unit can also make detailed suggestions regarding dishes that the user eats frequently. This allows for more appropriate suggestions by adjusting the level of detail of the suggestion based on the importance of the meal. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the user's meal priority data into AI, which can then analyze the data.

[0049] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the food category. For example, the suggestion unit can apply an algorithm related to sweetness and calories to a suggestion related to sweets. The suggestion unit can also apply an algorithm related to spiciness and types of spices to a suggestion related to spicy dishes. The suggestion unit can also apply an algorithm related to nutrients and calories to a suggestion related to healthy dishes. In this way, applying different suggestion algorithms depending on the food category enables more appropriate suggestions. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input food category data into AI, which then analyzes the data.

[0050] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit improves the accuracy of the suggestion, for example, based on the suggestion results that the user liked in the past. The suggestion unit can also adjust the suggestion algorithm based on the user's past feedback. The suggestion unit can also adjust the level of detail of the suggestion based on the user's past suggestion results. In this way, the accuracy of the suggestion is improved by referring to the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the user's past suggestion result data into AI, which can analyze the data.

[0051] When making a suggestion, the suggestion unit can determine the priority of the suggestions based on the time when the meal was input. For example, the suggestion unit can prioritize suggestions based on the food preferences recently input by the user. The suggestion unit can also prioritize suggestions based on the food preferences input by the user before a specific event. The suggestion unit can also prioritize suggestions based on the food preferences input by the user before a trip. This enables more appropriate suggestions by determining the priority of suggestions based on the time when the meal was submitted. Some or all of the above-mentioned processing by the suggestion unit can be performed using, or without, AI. For example, the suggestion unit can input data on the time when the user inputs meals into AI, which can then analyze the data.

[0052] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of the meals. For example, the suggestion unit may preferentially suggest dishes related to ingredients preferred by the user. The suggestion unit may also preferentially suggest ingredients related to a specific dish preferred by the user. The suggestion unit may also preferentially suggest new dishes related to dishes preferred by the user in the past. This allows for more appropriate suggestions by adjusting the order of suggestions based on the relevance of the meals. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit may input the user's meal relevance data into AI, which may then analyze the data.

[0053] When making a suggestion, the suggestion unit can adjust the use of technical terminology in the suggestion according to the user's level of expertise. For example, if the user is a cooking expert, the suggestion unit can use detailed technical terminology. If the user is a beginner cook, the suggestion unit can also use simple terminology. If the user is an intermediate cook, the suggestion unit can also use appropriate technical terminology. This allows for more appropriate suggestions by adjusting the use of technical terminology in the suggestion according to the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into AI, which can then analyze the data.

[0054] At the time of provision, the providing unit can select the optimal provision method by analyzing the user's past eating behavior. For example, the providing unit preferentially suggests a provision method that the user has previously preferred. The providing unit can also customize the provision method based on the user's past feedback. The providing unit can also suggest the optimal provision timing based on the user's past eating behavior. In this way, the optimal provision method can be selected by analyzing the user's past eating behavior. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past eating behavior data into AI, which can analyze the data.

[0055] The providing unit can customize the means of providing information based on the user's current living situation when providing the information. For example, if the user is busy, the providing unit can provide concise information. Furthermore, if the user is relaxed, the providing unit can also provide detailed information. Furthermore, if the user is traveling, the providing unit can prioritize providing information about the travel destination. This enables more appropriate information to be provided by customizing the means of providing information based on the user's current living situation. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's living situation data into AI, which can then analyze the data.

[0056] The providing unit can improve the method of provision by reflecting user feedback at the time of provision. The providing unit can improve the method of provision based on, for example, a provision method that the user has previously preferred. The providing unit can also customize the provision interface based on the user's past feedback. The providing unit can also optimize the provision procedure based on the user's past feedback. In this way, the method of provision can be improved by reflecting the user's feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input user feedback data into AI, which can analyze the data.

[0057] The providing unit can select the optimal providing method based on the user's geographical location information at the time of providing. For example, if the user is in a specific area, the providing unit can provide information about local specialties. Furthermore, if the user is traveling, the providing unit can also provide information about popular dishes at the travel destination. Furthermore, if the user is at home, the providing unit can also provide information about nearby restaurants. This makes it possible to select the optimal providing method by taking the user's geographical location information into consideration. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information data into AI, which can then analyze the data.

[0058] At the time of providing information, the providing unit can analyze the user's social media activity and suggest a means of providing the information. The providing unit, for example, provides information about dishes shared by the user on social media. The providing unit can also analyze the content of the user's social media posts and provide information about related dishes. The providing unit can also provide information about related dishes by referring to the activities of the user's friends on social media. This makes it possible to provide more relevant information by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media data into AI, which then analyzes the data.

[0059] The providing unit can customize the method of provision by reflecting the user's past feedback when providing the information. The providing unit customizes the method of provision based on, for example, a provision method that the user has previously preferred. The providing unit can also customize the provision interface based on the user's past feedback. The providing unit can also optimize the provision procedure based on the user's past feedback. In this way, the method of provision can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's feedback data into AI, which can analyze the data.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] When analyzing a user's dietary preferences, the analysis unit can take into account not only the user's past dietary history but also the user's health data. For example, if the user needs to consume a specific nutrient, it can prioritize meals that are rich in that nutrient. Also, if the user has a specific health condition (e.g., diabetes or high blood pressure), it can suggest meals that are suitable for that condition. It can also suggest balanced meals by taking into account the user's exercise level and sleep data. This makes it possible to suggest more appropriate meals based on the user's health condition.

[0062] The providing unit can make suggestions not only taking into account the user's dietary preferences, but also the frequency and timing of the user's meals. For example, if the user often skips breakfast, the providing unit can suggest easy-to-eat breakfast menus. Also, if the user often eats late at night, the providing unit can suggest easy-to-digest snacks. Furthermore, if the user has a habit of eating meals at a specific time of day, the providing unit can suggest meals suitable for that time of day. This makes it possible to make suggestions based on the user's eating habits.

[0063] When analyzing the user's food preferences, the analysis unit can also take into account the user's cultural background and religious restrictions. For example, if the user follows a particular religion, suggestions can be made that take into account the food restrictions of that religion. Also, if the user belongs to a particular cultural sphere, it is possible to suggest meals based on that culture. Furthermore, if the user has a preference for a particular food culture, it is also possible to suggest meals based on that food culture. This makes it possible to make suggestions that take into account the user's cultural background and religious restrictions.

[0064] The providing unit can make suggestions not only taking into account the user's food preferences but also the user's food budget. For example, if the user is looking for a meal on a low budget, the providing unit can suggest cost-effective meals. If the user wants to enjoy a luxurious meal on a special occasion, the providing unit can suggest high-end restaurants and special menus. Furthermore, if the user wants to enjoy a meal within a specific budget, the providing unit can suggest meals that fit that budget. This makes it possible to make suggestions that fit the user's budget.

[0065] The suggestion unit can make suggestions not only taking into account the user's dietary preferences but also the user's dietary objectives. For example, if the user's goal is to lose weight, a low-calorie, nutritionally balanced meal can be suggested. If the user's goal is to build muscle, a high-protein meal can be suggested. Furthermore, if the user's goal is to relax, a meal with a relaxing effect can be suggested. This makes it possible to make suggestions according to the user's dietary objectives.

[0066] When analyzing the user's dining preferences, the analysis unit can also take into account the user's dining environment. For example, if the user is eating at home, meals suitable for that environment can be suggested. Also, if the user is eating out, meals suitable for that environment can be suggested. Furthermore, if the user is eating at a specific event (e.g., a party or picnic), meals suitable for that environment can be suggested. This makes it possible to suggest meals according to the user's dining environment.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The reception unit inputs the user's food preferences. The user's food preferences include, for example, favorite types of food, allergy information, and meal frequency. The reception unit inputs the user's usual food preferences and delicious foods into the database. The user can also input specific dish names, ingredients, and flavor characteristics. For example, the user can input information such as "spicy curry" or "sweet dessert." Step 2: The analysis unit uses AI to analyze the data entered by the reception unit and understand the user's preferences. The analysis unit learns the user's preference patterns based on the entered data and understands what kind of food the user prefers. For example, it analyzes the user's preferences using methods such as frequency analysis, co-occurrence analysis, and clustering. Step 3: The suggestion unit suggests optimal meals based on the user's preferences ascertained by the analysis unit. The suggestion unit takes into account the user's current location and travel destination information to suggest restaurants and dishes that suit the user's preferences. For example, if the user is looking for a restaurant at their travel destination, the suggestion unit suggests restaurants and dishes that suit the user's preferences. Step 4: The providing unit provides the user with the meal suggested by the suggestion unit. The providing unit provides the user with information about the suggested meal, such as menu information, nutritional information, and price information.

[0069] (Example 2) A meal recommendation system according to an embodiment of the present invention inputs a user's food preferences into a database, analyzes the data using AI to understand the user's preferences, and suggests optimal meals for travel destinations. In the meal recommendation system, the user inputs their usual food preferences and favorite foods into a database, and the AI ​​analyzes the data to understand the user's preferences. Next, when the user searches for food at a travel destination, the AI ​​suggests optimal meals based on the user's preferences. These suggestions are made taking into account the user's current location and travel destination information. For example, the meal recommendation system allows the user to input their usual food preferences and favorite foods into a database. For example, specific dish names, ingredients, and flavor characteristics can be input. Next, the meal recommendation system uses AI to analyze the input data and understand the user's food preferences. Based on the input data, the AI ​​learns the user's preference patterns and understands the type of food the user prefers. For example, if the user prefers spicy food, the AI ​​makes suggestions based on that information. Next, when the user searches for food at a travel destination, the AI ​​suggests optimal meals based on the user's preferences. For example, when the user searches for restaurants at a travel destination, the AI ​​suggests restaurants and dishes that match the user's preferences. These suggestions are made taking into consideration information about the user's current location and travel destination, allowing the user to enjoy meals that suit their preferences even when traveling. This allows the meal suggestion system to easily find meals that suit their preferences even when traveling, simply by having the user input their usual food preferences into the database. This allows the meal suggestion system to easily find meals that suit their preferences even when traveling, simply by having the user input their usual food preferences into the database. For example, if a user normally likes spicy food, restaurants that serve spicy food can be suggested even when traveling. Also, if a user likes sweet desserts, cafes that serve delicious desserts can be suggested at the travel destination. In this way, users can enjoy meals that suit their preferences even when traveling.

[0070] A meal suggestion system according to an embodiment includes a reception unit, an analysis unit, a suggestion unit, and a provision unit. The reception unit inputs a user's food preferences. The user's food preferences include, but are not limited to, favorite types of dishes, allergy information, and meal frequency. The reception unit, for example, inputs the user's usual food preferences and delicious foods into a database. The reception unit also allows the user to input specific dish names, ingredients, and flavor characteristics. For example, the user inputs information such as "spicy curry" or "sweet dessert." The analysis unit uses AI to analyze the data input by the reception unit and understand the user's preferences. For example, the analysis unit learns the user's preference patterns based on the input data and understands the type of food the user prefers. For example, the analysis unit analyzes the user's preferences using methods such as frequency analysis, co-occurrence analysis, and clustering. The suggestion unit suggests optimal meals based on the user's preferences identified by the analysis unit. For example, the suggestion unit considers the user's current location and travel destination information to suggest restaurants and dishes that match the user's preferences. For example, when a user is looking for a restaurant at a travel destination, the suggestion unit suggests restaurants and dishes that suit the user's preferences. The provision unit provides the user with the meals suggested by the suggestion unit. For example, the provision unit provides the user with information about the suggested meals. The provision unit provides the user with menu information, nutritional information, price information, and the like. In this way, the meal suggestion system according to the embodiment can suggest optimal meals at a travel destination, etc., based on the user's food preferences. For example, if a user normally likes spicy food, restaurants that serve spicy food can be suggested at the travel destination. Furthermore, if a user likes sweet desserts, cafes that serve delicious desserts can be suggested at the travel destination. In this way, users can enjoy meals that suit their preferences even when traveling.

[0071] The reception unit allows the user to input their usual food preferences or what they find delicious into the database. Usual food preferences include, for example, the types of food eaten daily and taste preferences, but are not limited to these examples. The reception unit, for example, allows the user to input their usual food preferences and what they find delicious into the database. What they find delicious includes, for example, the names of dishes eaten in the past, the names of restaurants, and photos of dishes, but are not limited to these examples. By inputting their usual food preferences into the database, the system can perform analysis based on that information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the data entered by the user into AI, which then analyzes the data.

[0072] The analysis unit learns the user's preference patterns based on the input data and can determine what types of food the user prefers. The analysis unit learns the user's preference patterns using, for example, frequency analysis. For example, the analysis unit analyzes the types of dishes the user frequently eats. The analysis unit can also learn the user's preference patterns using co-occurrence analysis. For example, the analysis unit analyzes the user's favorite combinations of dishes. The analysis unit can also learn the user's preference patterns using clustering. For example, the analysis unit classifies the user's preferences into several clusters and understands the characteristics of each cluster. This allows the user's preference patterns to be learned, enabling more accurate suggestions. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's input data into AI, which then analyzes the data.

[0073] The suggestion unit can suggest appropriate meals based on the user's preferences, based on information about the user's current location or travel destination. The suggestion unit, for example, acquires the user's current location using GPS data and makes suggestions based on that information. For example, the suggestion unit suggests restaurants near the user's current location. The suggestion unit can also make suggestions based on information about the user's travel destination. For example, the suggestion unit suggests restaurants at the travel destination based on the user's travel plan information and reservation information. The suggestion unit can also make suggestions based on information input by the user. For example, when the user inputs information about the travel destination, the suggestion unit makes suggestions based on that information. This makes it possible to suggest more appropriate meals by taking into account information about the user's current location and travel destination. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input information about the user's current location and travel destination into AI, which then makes suggestions.

[0074] The providing unit can provide information about the proposed meal to the user. The providing unit, for example, provides menu information about the proposed meal to the user. For example, the providing unit displays menu information about the proposed restaurant. The providing unit can also provide nutritional information about the proposed meal to the user. For example, the providing unit displays the nutritional components of the proposed dish. The providing unit can also provide price information about the proposed meal to the user. For example, the providing unit displays price information about the proposed restaurant. By providing the user with information about the proposed meal, the user can select a meal based on that information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input information about the proposed meal to AI, which then provides the information.

[0075] The suggestion unit can analyze reviews or ratings of restaurants at travel destinations and reflect them in suggestions. The suggestion unit, for example, acquires and analyzes data from review sites for restaurants at travel destinations. For example, the suggestion unit analyzes user evaluation comments and star ratings. The suggestion unit can also analyze evaluation criteria for restaurants at travel destinations. For example, the suggestion unit analyzes star ratings, score ratings, rankings, etc. The suggestion unit can also make suggestions based on data from review sites. For example, the suggestion unit suggests restaurants that suit the user's preferences based on data from review sites. This enables more reliable suggestions by analyzing reviews and ratings of restaurants at travel destinations. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data from review sites into AI, which then analyzes the data.

[0076] The analysis unit can also analyze the user's dining situation or past ratings and reflect them in the suggestions. The analysis unit, for example, analyzes the user's dining situation. For example, the analysis unit analyzes the time of day, location, and accompanying persons of the meal. The analysis unit can also analyze the user's past ratings. For example, the analysis unit analyzes past reviews, star ratings, feedback comments, and the like. The analysis unit can also make suggestions based on the user's dining situation and past ratings. For example, the analysis unit suggests meals that match the user's preferences based on the user's dining situation and past ratings. This enables more personalized suggestions by analyzing the user's dining situation and past ratings. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's dining situation and past ratings into AI, which then analyzes the data.

[0077] The reception unit can estimate the user's emotions and adjust the timing of inputting food preferences based on the estimated user emotions. For example, if the user is relaxed, the reception unit can send a notification prompting the user to input their food preferences. Furthermore, if the user is feeling stressed, the reception unit can also suggest postponing the input. Furthermore, if the user is excited, the reception unit can display a guide to simplify the input. This allows input at a more appropriate time by adjusting the input timing according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or without an AI. For example, the reception unit can input the user's emotion data into an AI, which then analyzes the emotion.

[0078] The reception unit can analyze the user's past meal history and select the optimal input method. For example, the reception unit can automatically display as candidates the types of meals that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest the types of meals to be eaten during a specific time period based on the user's past input history. In this way, by analyzing the user's past meal history, the optimal input method for the user can be provided. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past meal history into AI, which then analyzes the data.

[0079] When inputting food preferences, the reception unit can filter based on the user's current dietary situation or health condition. For example, if the user is on a diet, the reception unit can prioritize low-calorie meals. Furthermore, if the user has allergies, the reception unit can also suggest meals that do not contain allergens. Furthermore, if the user wants to consume a specific nutrient, the reception unit can also suggest meals that are high in that nutrient. Thus, filtering based on the user's current dietary situation or health condition enables more appropriate suggestions. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's health data into AI, which then analyzes the data.

[0080] When inputting food preferences, the reception unit can select the optimal input means according to the user's input method. For example, if the user prefers voice input, the reception unit can preferentially suggest voice input. Furthermore, if the user prefers text input, the reception unit can also preferentially suggest text input. Furthermore, if the user prefers image input, the reception unit can also suggest an input method using image recognition. This improves input convenience by selecting the optimal input means according to the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data into AI, which can analyze the data.

[0081] The reception unit can estimate the user's emotions and determine the priority of food preferences to be input based on the estimated user emotions. For example, if the user is relaxed, the reception unit can prompt the user to input more detailed information. Furthermore, if the user is stressed, the reception unit can also prioritize simplified input. Furthermore, if the user is excited, the reception unit can suggest an intuitive input method. This enables more appropriate input by determining the priority of food preferences to be input according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the reception unit can input the user's emotion data into an AI, which can then analyze the emotion.

[0082] When inputting food preferences, the reception unit can prioritize inputting highly relevant preferences by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize suggesting local specialties. Furthermore, if the user is traveling, the reception unit can prioritize suggesting popular dishes at the travel destination. Furthermore, if the user is at home, the reception unit can prioritize suggesting menus from nearby restaurants. In this way, by taking the user's geographical location information into consideration, more relevant preferences can be prioritized and input. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to AI, which can then analyze the data.

[0083] When inputting food preferences, the reception unit can analyze the user's social media activity and input related preferences. For example, the reception unit can automatically display dishes shared by the user on social media as candidates. The reception unit can also analyze the content of the user's social media posts and suggest related dishes. The reception unit can also suggest related dishes by referring to the activities of the user's friends on social media. In this way, related preferences can be input by analyzing the user's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into AI, which then analyzes the data.

[0084] The reception unit can customize the input method by reflecting the user's past feedback when inputting food preferences. For example, the reception unit preferentially suggests input methods that the user has previously preferred. The reception unit can also customize the input interface based on the user's past feedback. The reception unit can also optimize the input procedure based on the user's past feedback. This allows the input method to be customized by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into AI, which can then analyze the data.

[0085] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can display detailed analysis results. Furthermore, if the user is stressed, the analysis unit can display concise analysis results. Furthermore, if the user is excited, the analysis unit can display visually appealing analysis results. By adjusting the way the analysis is presented based on the user's emotions, more appropriate analysis results can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into an AI, which then analyzes the emotion.

[0086] During analysis, the analysis unit can adjust the level of detail of the analysis based on the priority of food preferences. For example, the analysis unit performs a detailed analysis of ingredients that the user particularly likes. The analysis unit can also simplify the analysis of ingredients that the user is not particularly interested in. The analysis unit can also perform a detailed analysis of dishes that the user eats frequently. This allows for more appropriate analysis by adjusting the level of detail of the analysis based on the importance of food preferences. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's food preference data into AI, which then analyzes the data.

[0087] The analysis unit can apply different analysis algorithms depending on the food category during analysis. For example, the analysis unit can apply algorithms related to sweetness and calories to the analysis of sweets. The analysis unit can also apply algorithms related to spiciness and types of spices to the analysis of spicy dishes. The analysis unit can also apply algorithms related to nutrients and calories to the analysis of healthy dishes. In this way, applying different analysis algorithms depending on the food category enables more accurate analysis. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input food category data into AI, which then analyzes the data.

[0088] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit improves the accuracy of the analysis, for example, based on the user's past preferred analysis results. The analysis unit can also adjust the analysis algorithm based on the user's past feedback. The analysis unit can also adjust the level of detail of the analysis based on the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into AI, which then analyzes the data.

[0089] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, the analysis unit can perform a detailed analysis when the user is relaxed. The analysis unit can also perform a concise analysis when the user is stressed. The analysis unit can also perform a visually appealing analysis when the user is excited. This allows for more appropriate analysis by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into an AI, which then analyzes the emotion.

[0090] During analysis, the analysis unit can determine the priority of analysis based on the time when the food preferences were input. For example, the analysis unit prioritizes analysis of food preferences recently input by the user. The analysis unit can also prioritize analysis of food preferences input by the user before a specific event. The analysis unit can also prioritize analysis of food preferences input by the user before a trip. This enables more appropriate analysis by determining the priority of analysis based on the time when the food preferences were submitted. Some or all of the above-mentioned processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time when the user's food preferences were input into AI, and the AI ​​can analyze the data.

[0091] During analysis, the analysis unit can adjust the order of analysis based on food relevance. For example, the analysis unit prioritizes analysis of dishes related to ingredients preferred by the user. The analysis unit can also prioritize analysis of ingredients related to a specific dish preferred by the user. The analysis unit can also prioritize analysis of new dishes related to dishes preferred by the user in the past. This allows for more appropriate analysis by adjusting the order of analysis based on food relevance. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's food relevance data into AI, which then analyzes the data.

[0092] 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 is a cooking expert, the analysis unit can use detailed technical terms. If the user is a beginner cook, the analysis unit can also use simple terms. If the user is an intermediate cook, the analysis unit can also use appropriate technical terms. This allows for more appropriate analysis by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's level of expertise data into AI, which can then analyze the data.

[0093] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is stressed, the suggestion unit can also provide concise suggestions. If the user is excited, the suggestion unit can also provide visually appealing suggestions. This enables more appropriate suggestions by adjusting the way the suggestions are expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using an AI, for example, or without an AI. For example, the suggestion unit can input the user's emotion data into an AI, which can then analyze the emotion.

[0094] When making a suggestion, the suggestion unit can adjust the level of detail of the suggestion based on the priority of the meal. For example, the suggestion unit makes detailed suggestions regarding ingredients that the user particularly likes. The suggestion unit can also simplify suggestions regarding ingredients that the user is not particularly interested in. The suggestion unit can also make detailed suggestions regarding dishes that the user eats frequently. This allows for more appropriate suggestions by adjusting the level of detail of the suggestion based on the importance of the meal. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the user's meal priority data into AI, which can then analyze the data.

[0095] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the food category. For example, the suggestion unit can apply an algorithm related to sweetness and calories to a suggestion related to sweets. The suggestion unit can also apply an algorithm related to spiciness and types of spices to a suggestion related to spicy dishes. The suggestion unit can also apply an algorithm related to nutrients and calories to a suggestion related to healthy dishes. In this way, applying different suggestion algorithms depending on the food category enables more appropriate suggestions. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input food category data into AI, which then analyzes the data.

[0096] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit improves the accuracy of the suggestion, for example, based on the suggestion results that the user liked in the past. The suggestion unit can also adjust the suggestion algorithm based on the user's past feedback. The suggestion unit can also adjust the level of detail of the suggestion based on the user's past suggestion results. In this way, the accuracy of the suggestion is improved by referring to the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the user's past suggestion result data into AI, which can analyze the data.

[0097] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is stressed, the suggestion unit can also provide concise suggestions. If the user is excited, the suggestion unit can also provide visually appealing suggestions. This enables more appropriate suggestions by adjusting the length of the suggestions according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using an AI, for example, or without an AI. For example, the suggestion unit can input the user's emotion data into an AI, which can then analyze the emotion.

[0098] When making a suggestion, the suggestion unit can determine the priority of the suggestions based on the time when the meal was input. For example, the suggestion unit can prioritize suggestions based on the food preferences recently input by the user. The suggestion unit can also prioritize suggestions based on the food preferences input by the user before a specific event. The suggestion unit can also prioritize suggestions based on the food preferences input by the user before a trip. This enables more appropriate suggestions by determining the priority of suggestions based on the time when the meal was submitted. Some or all of the above-mentioned processing by the suggestion unit can be performed using, or without, AI. For example, the suggestion unit can input data on the time when the user inputs meals into AI, which can then analyze the data.

[0099] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of the meals. For example, the suggestion unit may preferentially suggest dishes related to ingredients preferred by the user. The suggestion unit may also preferentially suggest ingredients related to a specific dish preferred by the user. The suggestion unit may also preferentially suggest new dishes related to dishes preferred by the user in the past. This allows for more appropriate suggestions by adjusting the order of suggestions based on the relevance of the meals. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit may input the user's meal relevance data into AI, which may then analyze the data.

[0100] When making a suggestion, the suggestion unit can adjust the use of technical terminology in the suggestion according to the user's level of expertise. For example, if the user is a cooking expert, the suggestion unit can use detailed technical terminology. If the user is a beginner cook, the suggestion unit can also use simple terminology. If the user is an intermediate cook, the suggestion unit can also use appropriate technical terminology. This allows for more appropriate suggestions by adjusting the use of technical terminology in the suggestion according to the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into AI, which can then analyze the data.

[0101] The providing unit can estimate the user's emotions and adjust the method of providing information based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide detailed information. Furthermore, if the user is stressed, the providing unit can provide concise information. Furthermore, if the user is excited, the providing unit can provide visually appealing information. This enables more appropriate information to be provided by adjusting the method of providing information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using an AI, for example, or without using an AI. For example, the providing unit can input the user's emotion data into an AI, which can analyze the emotion.

[0102] At the time of provision, the providing unit can select the optimal provision method by analyzing the user's past eating behavior. For example, the providing unit preferentially suggests a provision method that the user has previously preferred. The providing unit can also customize the provision method based on the user's past feedback. The providing unit can also suggest the optimal provision timing based on the user's past eating behavior. In this way, the optimal provision method can be selected by analyzing the user's past eating behavior. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past eating behavior data into AI, which can analyze the data.

[0103] The providing unit can customize the means of providing information based on the user's current living situation when providing the information. For example, if the user is busy, the providing unit can provide concise information. Furthermore, if the user is relaxed, the providing unit can also provide detailed information. Furthermore, if the user is traveling, the providing unit can prioritize providing information about the travel destination. This enables more appropriate information to be provided by customizing the means of providing information based on the user's current living situation. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's living situation data into AI, which can then analyze the data.

[0104] The providing unit can improve the method of provision by reflecting user feedback at the time of provision. The providing unit can improve the method of provision based on, for example, a provision method that the user has previously preferred. The providing unit can also customize the provision interface based on the user's past feedback. The providing unit can also optimize the provision procedure based on the user's past feedback. In this way, the method of provision can be improved by reflecting the user's feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input user feedback data into AI, which can analyze the data.

[0105] The providing unit can estimate the user's emotions and determine the priority of information provision based on the estimated user's emotions. For example, when the user is relaxed, the providing unit can prioritize providing detailed information. Furthermore, when the user is stressed, the providing unit can prioritize providing concise information. Furthermore, when the user is excited, the providing unit can prioritize providing visually appealing information. This enables more appropriate information provision by determining the priority of information provision according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using an AI, for example, or without an AI. For example, the providing unit can input the user's emotion data into an AI, which can analyze the emotion.

[0106] The providing unit can select the optimal providing method based on the user's geographical location information at the time of providing. For example, if the user is in a specific area, the providing unit can provide information about local specialties. Furthermore, if the user is traveling, the providing unit can also provide information about popular dishes at the travel destination. Furthermore, if the user is at home, the providing unit can also provide information about nearby restaurants. This makes it possible to select the optimal providing method by taking the user's geographical location information into consideration. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information data into AI, which can then analyze the data.

[0107] At the time of providing information, the providing unit can analyze the user's social media activity and suggest a means of providing the information. The providing unit, for example, provides information about dishes shared by the user on social media. The providing unit can also analyze the content of the user's social media posts and provide information about related dishes. The providing unit can also provide information about related dishes by referring to the activities of the user's friends on social media. This makes it possible to provide more relevant information by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media data into AI, which then analyzes the data.

[0108] The providing unit can customize the method of provision by reflecting the user's past feedback when providing the information. The providing unit customizes the method of provision based on, for example, a provision method that the user has previously preferred. The providing unit can also customize the provision interface based on the user's past feedback. The providing unit can also optimize the provision procedure based on the user's past feedback. In this way, the method of provision can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's feedback data into AI, which can analyze the data. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, suggestion unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and inputs the user's food preferences. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input data using AI to understand the user's preferences. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an optimal meal based on the user's preferences. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides information on the suggested meal to the user. The reception unit can, for example, estimate the user's emotions and adjust the timing of inputting the food preferences based on the estimated emotions. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, analysis unit, suggestion unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and inputs the user's food preferences. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input data using AI to understand the user's preferences. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an optimal meal based on the user's preferences. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides information on the suggested meal to the user. The reception unit can, for example, estimate the user's emotions and adjust the timing of inputting the food preferences based on the estimated emotions. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, suggestion unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and inputs the user's food preferences. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input data using AI to understand the user's preferences. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an optimal meal based on the user's preferences. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and provides information on the suggested meal to the user. The reception unit can, for example, estimate the user's emotions and adjust the timing of inputting the food preferences based on the estimated emotions. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, suggestion unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and inputs the user's food preferences. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input data using AI to understand the user's preferences. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an optimal meal based on the user's preferences. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides information on the suggested meal to the user. The reception unit can, for example, estimate the user's emotions and adjust the timing of inputting the food preferences based on the estimated emotions.

[0109] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0110] When analyzing a user's dietary preferences, the analysis unit can take into account not only the user's past dietary history but also the user's health data. For example, if the user needs to consume a specific nutrient, it can prioritize meals that are rich in that nutrient. Also, if the user has a specific health condition (e.g., diabetes or high blood pressure), it can suggest meals that are suitable for that condition. It can also suggest balanced meals by taking into account the user's exercise level and sleep data. This makes it possible to suggest more appropriate meals based on the user's health condition.

[0111] The suggestion unit can estimate the user's emotions and adjust the type of meal to be suggested based on the estimated user's emotions. For example, if the user is feeling stressed, a meal with a relaxing effect can be suggested. Also, if the user is excited, a meal to replenish energy can be suggested. Furthermore, if the user is feeling sad, a meal to brighten the mood can be suggested. This makes it possible to suggest meals according to the user's emotions, thereby improving user satisfaction.

[0112] The providing unit can make suggestions not only taking into account the user's dietary preferences, but also the frequency and timing of the user's meals. For example, if the user often skips breakfast, the providing unit can suggest easy-to-eat breakfast menus. Also, if the user often eats late at night, the providing unit can suggest easy-to-digest snacks. Furthermore, if the user has a habit of eating meals at a specific time of day, the providing unit can suggest meals suitable for that time of day. This makes it possible to make suggestions based on the user's eating habits.

[0113] When analyzing the user's food preferences, the analysis unit can also take into account the user's cultural background and religious restrictions. For example, if the user follows a particular religion, suggestions can be made that take into account the food restrictions of that religion. Also, if the user belongs to a particular cultural sphere, it is possible to suggest meals based on that culture. Furthermore, if the user has a preference for a particular food culture, it is also possible to suggest meals based on that food culture. This makes it possible to make suggestions that take into account the user's cultural background and religious restrictions.

[0114] The suggestion unit can estimate the user's emotions and adjust the presentation method of the suggested meal based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit can make detailed explanations and beautiful images. If the user is stressed, the suggestion unit can make concise and easy-to-understand suggestions. Furthermore, if the user is excited, the suggestion unit can make visually appealing suggestions. This makes it possible to maximize the effectiveness of suggestions by adjusting the presentation method according to the user's emotions.

[0115] The providing unit can make suggestions not only taking into account the user's food preferences but also the user's food budget. For example, if the user is looking for a meal on a low budget, the providing unit can suggest cost-effective meals. If the user wants to enjoy a luxurious meal on a special occasion, the providing unit can suggest high-end restaurants and special menus. Furthermore, if the user wants to enjoy a meal within a specific budget, the providing unit can suggest meals that fit that budget. This makes it possible to make suggestions that fit the user's budget.

[0116] The analysis unit can estimate the user's emotions and adjust the priority of analysis based on the estimated user's emotions. For example, if the user is relaxed, a detailed analysis can be prioritized. Also, if the user is stressed, a concise analysis can be prioritized. Furthermore, if the user is excited, a visually appealing analysis can be prioritized. In this way, by adjusting the priority of analysis according to the user's emotions, more appropriate analysis results can be provided.

[0117] The suggestion unit can make suggestions not only taking into account the user's dietary preferences but also the user's dietary objectives. For example, if the user's goal is to lose weight, a low-calorie, nutritionally balanced meal can be suggested. If the user's goal is to build muscle, a high-protein meal can be suggested. Furthermore, if the user's goal is to relax, a meal with a relaxing effect can be suggested. This makes it possible to make suggestions according to the user's dietary objectives.

[0118] The providing unit can estimate the user's emotions and adjust the amount of information to be provided based on the estimated user's emotions. For example, if the user is relaxed, detailed information can be provided. If the user is stressed, concise information can be provided. Furthermore, if the user is excited, visually appealing information can be provided. In this way, by adjusting the amount of information according to the user's emotions, more appropriate information can be provided.

[0119] When analyzing the user's dining preferences, the analysis unit can also take into account the user's dining environment. For example, if the user is eating at home, meals suitable for that environment can be suggested. Also, if the user is eating out, meals suitable for that environment can be suggested. Furthermore, if the user is eating at a specific event (e.g., a party or picnic), meals suitable for that environment can be suggested. This makes it possible to suggest meals according to the user's dining environment.

[0120] The processing flow of the second embodiment will be briefly explained below.

[0121] Step 1: The reception unit inputs the user's food preferences. The user's food preferences include, for example, favorite types of food, allergy information, and meal frequency. The reception unit inputs the user's usual food preferences and delicious foods into the database. The user can also input specific dish names, ingredients, and flavor characteristics. For example, the user can input information such as "spicy curry" or "sweet dessert." Step 2: The analysis unit uses AI to analyze the data entered by the reception unit and understand the user's preferences. The analysis unit learns the user's preference patterns based on the entered data and understands what kind of food the user prefers. For example, it analyzes the user's preferences using methods such as frequency analysis, co-occurrence analysis, and clustering. Step 3: The suggestion unit suggests optimal meals based on the user's preferences ascertained by the analysis unit. The suggestion unit takes into account the user's current location and travel destination information to suggest restaurants and dishes that suit the user's preferences. For example, if the user is looking for a restaurant at their travel destination, the suggestion unit suggests restaurants and dishes that suit the user's preferences. Step 4: The providing unit provides the user with the meal suggested by the suggestion unit. The providing unit provides the user with information about the suggested meal, such as menu information, nutritional information, and price information.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0127] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0143] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

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

[0158] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0159] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] [Explanation of symbols]

[0194] 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 food preferences; an analysis unit that analyzes the data input by the reception unit and grasps the user's preferences; a suggestion unit that suggests an appropriate meal based on the user's preferences identified by the analysis unit; a providing unit that provides the meal suggested by the suggestion unit to the user. A system characterized by:

2. The reception unit The user inputs their usual food preferences or what they find delicious into a database.

2. The system of claim 1.

3. The analysis unit Learns user preference patterns based on input data and understands what type of food the user prefers 2. The system of claim 1.

4. The proposal unit Based on the user's current location or travel destination, the app will suggest suitable meals based on the user's preferences.

2. The system of claim 1.

5. The providing unit Providing users with suggested meal information 2. The system of claim 1.

6. The proposal unit Analyze reviews or ratings of restaurants at your travel destination and incorporate them into your recommendations 2. The system of claim 1.

7. The analysis unit The system also analyzes the user's eating situation and past evaluations and reflects them in the suggestions.

2. The system of claim 1.

8. The reception unit Estimates the user's emotions and adjusts the timing of inputting food preferences based on the estimated user emotions.

2. The system of claim 1.

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