system

Abstract

The system according to this embodiment aims to provide customized cooking recipes based on the user's taste profile. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and a serving unit. The reception unit collects the user's taste profile. The analysis unit analyzes the taste profile collected by the reception unit. The generation unit generates a cooking recipe based on the analysis results obtained by the analysis unit. The serving unit serves the dish based on the recipe generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to provide a cooking recipe that perfectly matches the user's taste, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a customized cooking recipe based on the user's taste profile.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a serving unit. The reception unit collects the user's taste profile. The analysis unit analyzes the taste profile collected by the reception unit. The generation unit generates a cooking recipe based on the analysis results obtained by the analysis unit. The serving unit serves the dish based on the recipe generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide customized cooking recipes based on the user's taste profile. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

[0016] [First Embodiment] 0FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

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

[0018] [[ID=,18]]The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI agent system according to an embodiment of the present invention is a system that deeply analyzes a user's taste profile and provides a fully customized cooking recipe based on that preference. This AI agent system creates a perfectly suited dining experience for the user by collecting and analyzing the user's taste profile, generating cooking recipes, and providing them. For example, the AI ​​agent system collects information such as the user's favorite and disliked ingredients and preferred seasonings. Next, the AI ​​agent system analyzes the collected taste profile. The AI ​​agent system uses an advanced taste analysis algorithm to analyze the user's taste profile in detail. For example, if the user likes spicy food, the AI ​​agent system will suggest a spicy dish recipe based on that information. Based on the analysis results, the AI ​​agent system generates the optimal cooking recipe for the user. The AI ​​agent system creates a recipe that suits the user's preferences by considering the combination of ingredients, cooking methods, spice selection, etc. For example, if the user likes spicy food, the AI ​​agent system will suggest a recipe using spicy spices. Furthermore, the AI ​​agent system collects taste feedback in real time. After the user tries the dish, they input their impressions and feedback into the AI ​​agent system. Based on this feedback, the AI ​​agent system continuously improves the recipe. For example, if a user provides feedback such as "I'd like it a little spicier," the AI ​​agent system will adjust the spiciness in the next recipe. This mechanism allows users to enjoy dishes perfectly suited to their preferences. The AI ​​agent system improves individual satisfaction and cooking success rates, and promotes the discovery of new ingredients and cooking methods. It also supports a natural introduction to healthy eating habits. For example, if a user requests a healthy meal, the AI ​​agent system will suggest a nutritionally balanced recipe. The AI ​​agent system provides a new cooking experience by combining taste data and AI technology, realizing a user-centered, interactive cooking process. It prevents cooking monotony and provides dishes tailored to individual health needs for cooking enthusiasts and health-conscious individuals of all ages.For example, if a user has specific health needs, the AI ​​agent system will suggest recipes tailored to those needs. This allows the AI ​​agent system to provide customized cooking recipes based on the user's taste profile.

[0029] The AI ​​agent system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit collects the user's taste profile. The user's taste profile includes, but is not limited to, favorite and disliked ingredients and preferred seasonings. The reception unit collects the user's taste profile, for example, through questionnaires. The reception unit can also collect a taste profile based on the user's past eating history. For example, the reception unit collects data on dishes the user has eaten in the past and creates a taste profile. Furthermore, the reception unit can collect allergy information and create a taste profile based on the user's health status. The analysis unit analyzes the collected taste profiles. The analysis unit uses advanced taste analysis algorithms to analyze the user's taste profile in detail. For example, the analysis unit uses machine learning algorithms to analyze the user's taste profile. The analysis unit can also use data mining techniques to analyze the user's taste profile. Furthermore, the analysis unit can optimize its analysis algorithm based on the user's past feedback. The provision unit generates cooking recipes based on the analysis results. The generation unit creates user-preferred recipes, taking into account factors such as ingredient combinations, cooking methods, and spice selections. For example, the generation unit creates recipes considering nutritional balance. It can also customize recipes based on the user's current ingredient inventory. Furthermore, the generation unit can generate recipes considering the user's geographical location. The serving unit provides the dishes based on the generated recipes. The serving unit collects real-time taste feedback and continuously improves the recipes. For example, the serving unit collects the user's impressions and feedback after they try the dish. It can also estimate the user's emotions and adjust the feedback collection method accordingly. Furthermore, the serving unit can collect feedback considering the user's geographical location. As a result, the AI ​​agent system according to this embodiment can provide customized cooking recipes based on the user's taste profile.

[0030] The reception desk collects the user's taste profile. This profile includes, but is not limited to, information such as favorite and disliked ingredients and preferred seasonings. The reception desk can collect the user's taste profile through, for example, questionnaires. These questionnaires are provided via online forms or applications, making them easy for users to answer. Furthermore, the reception desk can collect taste profiles based on past eating history. For example, it can analyze the dishes a user has ordered in the past and their dining history at restaurants to understand their preferences. The reception desk can also collect the user's allergy information and create a taste profile based on their health status. This information is collected based on medical information and past allergy reaction history provided by the user. This allows the reception desk to create a detailed profile tailored to the user's individual taste and health condition. Additionally, the reception desk can collect feedback on the user's meals and continuously update the taste profile. For example, after a user tries a new dish, they can provide feedback and evaluations to improve the accuracy of the profile. This allows the reception desk to collect comprehensive data on the user's taste and health condition, providing a foundation for personalized services across the entire system.

[0031] The analysis unit uses advanced taste analysis algorithms to analyze the collected taste profiles. For example, it can use machine learning algorithms to analyze the user's taste profile in detail. Machine learning algorithms are used to learn the user's preferences and tendencies and identify patterns. It can also use data mining techniques to analyze the user's taste profile. Data mining techniques are used to extract useful information from large amounts of data and gain insights into the user's taste. Furthermore, the analysis unit can optimize the analysis algorithm based on the user's past feedback. For example, it can analyze the feedback provided by the user and make adjustments to improve the accuracy of the algorithm. This allows the analysis unit to analyze the user's taste profile quickly and accurately, improving the quality of personalized services throughout the system. In addition, the analysis unit can continuously update the user's taste profile and perform analysis based on the latest information. This allows the analysis unit to respond to changes in the user's taste and new preferences, and provide ongoing service.

[0032] The generation unit considers ingredient combinations, cooking methods, and spice selections to generate cooking recipes based on analysis results. For example, the generation unit can create recipes while considering nutritional balance, thereby providing recipes that support the user's health. The generation unit can also customize recipes based on the user's current ingredient inventory. For example, if the user inputs the ingredients in their refrigerator, it can suggest recipes using those ingredients. Furthermore, the generation unit can generate recipes while considering the user's geographical location. For example, it can suggest recipes using local specialties or seasonal ingredients. This allows the generation unit to provide personalized recipes that comprehensively consider the user's preferences, health condition, ingredient inventory, and geographical location. In addition, the generation unit can improve recipes based on the user's past feedback. For example, by analyzing the feedback provided by the user and adjusting the seasoning and cooking methods of the recipes, it can provide more satisfying recipes. This allows the generation unit to continuously provide high-quality recipes that meet the user's needs.

[0033] The service provider collects real-time taste feedback to continuously improve recipes in order to serve dishes based on the generated recipes. For example, the service provider can collect users' impressions and feedback after they try a dish. Users can easily provide feedback through the application. The service provider can also estimate users' emotions and adjust how feedback is collected. For example, by analyzing users' facial expressions and tone of voice to estimate emotions, feedback can be requested at the appropriate time and in the appropriate way. Furthermore, the service provider can collect feedback considering the user's geographical location. For example, feedback specific to a particular region or culture can be collected, and recipes can be localized. This allows the service provider to collect detailed feedback based on users' tastes and emotions, and continuously improve the quality of recipes. In addition, the service provider can provide data to improve the overall service of the system based on user feedback. For example, by analyzing feedback and understanding user needs and trends, it can be used to develop new features and services. This allows the service provider to improve user satisfaction and enhance the overall quality of the system.

[0034] The reception desk can collect information such as the user's favorite and disliked foods and preferred seasonings. For example, the reception desk can collect this information through questionnaires. It can also collect this information based on the user's past eating history. For example, the reception desk can collect data on dishes the user has eaten in the past and create a taste profile. Furthermore, the reception desk can collect allergy information and create a taste profile based on the user's health status. This allows for the collection of a detailed taste profile of the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input questionnaire response data into a generating AI, which can then create the user's taste profile.

[0035] The analysis unit can analyze the user's taste profile in detail using advanced taste analysis algorithms. For example, the analysis unit can analyze the user's taste profile using machine learning algorithms. For instance, the analysis unit inputs the user's taste profile as input data into a machine learning model and outputs the analysis results. The analysis unit can also analyze the user's taste profile using data mining techniques. For example, the analysis unit inputs the user's taste profile into a data mining algorithm and outputs the analysis results. Furthermore, the analysis unit can optimize the analysis algorithm based on the user's past feedback. For example, the analysis unit inputs the user's past feedback data into the analysis algorithm and adjusts the algorithm's parameters. This allows for a detailed analysis of the user's taste profile. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's taste profile into a generating AI, and the generating AI can output the analysis results.

[0036] The generation unit can create user-preferred recipes by considering factors such as ingredient combinations, cooking methods, and spice selections. For example, the generation unit can create recipes considering nutritional balance. For instance, it can use the user's taste profile as input data to generate a nutritionally balanced recipe. The generation unit can also customize recipes based on the user's current ingredient inventory. For example, it can generate recipes based on the ingredients in the user's refrigerator. Furthermore, the generation unit can generate recipes considering the user's geographical location. For example, it can generate recipes using local ingredients based on the user's geographical location. This allows for the creation of recipes tailored to the user's preferences. Some or all of the above-described processes in the generation unit may be performed using AI, or without AI. For example, the generation unit can input the user's taste profile into a generation AI, which can then generate recipes.

[0037] The service provider can collect taste feedback in real time and continuously improve recipes. For example, the service provider can collect feedback from users after they have tried a dish. For example, the service provider can collect feedback through a feedback form after users have tried a dish. The service provider can also estimate the user's emotions and adjust the feedback collection method accordingly. For example, the service provider can estimate the user's emotions and collect detailed feedback if the user is relaxed, and simpler feedback if the user is stressed. Furthermore, the service provider can collect feedback considering the user's geographical location. For example, the service provider can collect feedback on local ingredients and dishes based on the user's geographical location. This allows the service provider to improve recipes based on user feedback. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input user feedback data into a generating AI, which can then improve the recipes.

[0038] The reception unit can analyze the user's past eating history and select the optimal data collection method. For example, the reception unit can customize the collection method based on data of dishes the user has previously enjoyed eating. For example, the reception unit can analyze the user's eating history data and identify patterns in the dishes the user has enjoyed. The reception unit can also adjust the collection method considering ingredients the user has previously avoided. For example, the reception unit can create a list of avoided ingredients based on the user's eating history data. Furthermore, the reception unit can analyze the user's eating patterns and determine the optimal timing for data collection. For example, the reception unit can analyze the user's eating patterns and identify the optimal timing for data collection. This allows for the collection of a taste profile in the most optimal way based on the user's past eating history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's eating history data into a generating AI, which can then select the optimal data collection method.

[0039] The reception unit can filter the collected taste profiles based on the user's current health status and dietary restrictions. For example, if the user has allergies, the reception unit can filter the collected data based on that information. For example, the reception unit can collect the user's allergy information and exclude foods that cause allergies. The reception unit can also prioritize collecting low-calorie foods if the user is on a diet. For example, the reception unit can collect the user's diet information and list low-calorie foods. Furthermore, if the user requires specific nutrients, the reception unit can collect foods containing those nutrients. For example, the reception unit can collect the user's nutrient needs and list foods containing those nutrients. This allows for the collection of taste profiles tailored to the user's health status and dietary restrictions. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's health information into a generating AI, which can then perform the filtering.

[0040] The reception unit can prioritize collecting highly relevant information when gathering taste profiles, taking into account the user's geographical location. For example, if the user lives in a specific region, the reception unit can prioritize collecting information about local ingredients and cuisine. For example, the reception unit can collect information about local ingredients and cuisine based on the user's geographical location. The reception unit can also prioritize collecting information about local ingredients and cuisine if the user is traveling. For example, the reception unit can collect information about local ingredients and cuisine based on the user's geographical location. Furthermore, if the user prefers the cuisine of a specific region, the reception unit can prioritize collecting information about the cuisine of that region. For example, the reception unit can collect information about the cuisine of that region based on the user's geographical location. This allows for the collection of highly relevant information based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location into a generating AI, which can then collect highly relevant information.

[0041] The reception unit can collect relevant information by analyzing the user's social media activity when collecting taste profiles. For example, the reception unit can collect taste profiles based on information about dishes the user has shared on social media. For example, the reception unit can analyze the user's social media accounts and collect information about the shared dishes. The reception unit can also collect taste profiles based on information about food accounts the user follows on social media. For example, the reception unit can collect information about food accounts the user follows and create a taste profile. Furthermore, the reception unit can also collect taste profiles based on information about dishes the user has "liked" on social media. For example, the reception unit can collect information about dishes the user has "liked" and create a taste profile. This allows the reception unit to collect relevant information based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media data into a generating AI, and the generating AI can collect relevant information.

[0042] The analysis unit can optimize its analysis algorithm by referring to the user's past feedback when analyzing the taste profile. For example, the analysis unit adjusts the analysis algorithm based on feedback previously provided by the user. For example, the analysis unit inputs the user's past feedback data into the analysis algorithm and adjusts the algorithm's parameters. The analysis unit can also optimize the analysis algorithm based on data of dishes the user has previously liked. For example, the analysis unit inputs the user's past cooking data into the analysis algorithm and adjusts the algorithm to obtain the optimal analysis result. Furthermore, the analysis unit can adjust the analysis algorithm based on data of ingredients the user has previously avoided. For example, the analysis unit inputs the user's past ingredient data into the analysis algorithm and performs the analysis taking the avoided ingredients into consideration. This allows the analysis algorithm to be optimized based on the user's past feedback. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's feedback data into a generating AI and optimize the analysis algorithm using the generating AI.

[0043] The analysis unit can improve the accuracy of its taste profile analysis based on the user's eating history. For example, the analysis unit can improve the accuracy of its analysis based on data of dishes the user has eaten in the past. For example, the analysis unit inputs the user's eating history data into an analysis algorithm to obtain highly accurate analysis results. The analysis unit can also improve the accuracy of its analysis by analyzing the user's eating patterns. For example, the analysis unit inputs the user's eating pattern data into an analysis algorithm and adjusts the algorithm to obtain the optimal analysis results. Furthermore, the analysis unit can improve the accuracy of its analysis based on data of ingredients the user has avoided in the past. For example, the analysis unit inputs the user's past ingredient data into an analysis algorithm and performs the analysis while considering the avoided ingredients. This allows the analysis to improve accuracy based on the user's eating history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's eating history data into a generating AI, and the generating AI can improve the accuracy of the analysis.

[0044] The analysis unit can perform taste profile analysis while taking into account the user's lifestyle. For example, if the user has a healthy lifestyle, the analysis unit can perform analysis based on that information. For example, the analysis unit can collect user lifestyle data and perform analysis based on data from users with a healthy lifestyle. The analysis unit can also perform analysis based on information if the user has specific dietary restrictions. For example, the analysis unit can collect user dietary restriction data and perform analysis based on data from users with specific dietary restrictions. Furthermore, the analysis unit can also perform analysis based on information if the user has a specific eating pattern. For example, the analysis unit can collect user eating pattern data and perform analysis based on data from users with specific eating patterns. This allows analysis to be performed based on the user's lifestyle. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user lifestyle data into a generating AI and have the generating AI perform the analysis.

[0045] The analysis unit can improve the accuracy of its analysis by referring to the user's health data when analyzing the taste profile. For example, the analysis unit can improve the accuracy of its analysis based on the user's health data. For example, the analysis unit can collect the user's health data and perform analysis based on that data. The analysis unit can also improve the accuracy of its analysis by considering the user's health status. For example, the analysis unit can collect the user's health status data and perform analysis considering the health status. Furthermore, the analysis unit can also improve the accuracy of its analysis based on the user's health goals. For example, the analysis unit can collect the user's health goal data and perform analysis based on the health goals. This allows the analysis to improve accuracy based on the user's health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's health data into a generating AI, and the generating AI can improve the accuracy of the analysis.

[0046] The generation unit can generate the optimal recipe by referring to the user's past cooking history when generating a recipe. For example, the generation unit can generate the optimal recipe based on data of dishes the user has liked in the past. For example, the generation unit can collect the user's past cooking data and identify patterns of dishes the user has liked. The generation unit can also generate the optimal recipe based on data of ingredients the user has avoided in the past. For example, the generation unit can collect the user's past ingredient data and generate a recipe considering the ingredients the user avoided. Furthermore, the generation unit can analyze the user's cooking patterns and generate the optimal recipe. For example, the generation unit can collect the user's cooking pattern data and generate the optimal recipe. This allows the generation of the optimal recipe to be generated based on the user's past cooking history. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's cooking history data into a generation AI, and the generation AI can generate the optimal recipe.

[0047] The generation unit can customize recipes based on the user's current ingredient inventory when generating recipes. For example, the generation unit can customize recipes based on the ingredients in the user's refrigerator. For example, the generation unit can collect the user's refrigerator inventory data and generate recipes based on the inventory ingredients. The generation unit can also customize recipes based on the spices the user possesses. For example, the generation unit can collect the user's spice data and generate recipes based on the spices the user possesses. Furthermore, the generation unit can also customize recipes based on ingredients the user has just purchased. For example, the generation unit can collect the user's purchase history data and generate recipes based on the purchased ingredients. This allows for the customization of recipes based on the user's current ingredient inventory. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's ingredient inventory data into a generation AI, which can then customize the recipes.

[0048] The generation unit can generate the optimal recipe by considering the user's geographical location information. For example, if the user lives in a specific region, the generation unit can generate a recipe using ingredients from that region. For example, the generation unit collects the user's geographical location information and generates a recipe based on the ingredients of that region. The generation unit can also generate a recipe using ingredients from the user's travel destination if the user is traveling. For example, the generation unit collects the user's geographical location information and generates a recipe based on the ingredients of the travel destination. Furthermore, if the user prefers the cuisine of a specific region, the generation unit can generate a recipe using ingredients from that region. For example, the generation unit collects the user's geographical location information and generates a recipe based on the cuisine of that region. This allows the generation unit to generate the optimal recipe based on the user's geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information into a generation AI, which can then generate the optimal recipe.

[0049] The generation unit can analyze the user's social media activity and suggest relevant recipes when generating recipes. For example, the generation unit can generate recipes based on information about dishes the user has shared on social media. For example, the generation unit can analyze the user's social media accounts and collect information about shared dishes. The generation unit can also generate recipes based on information about cooking accounts the user follows on social media. For example, the generation unit can collect information about cooking accounts the user follows and generate recipes. Furthermore, the generation unit can generate recipes based on information about dishes the user has "liked" on social media. For example, the generation unit can collect information about dishes the user has "liked" and generate recipes. This allows the system to suggest relevant recipes based on the user's social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media data into a generation AI, which can then suggest relevant recipes.

[0050] The service provider can select the optimal collection method by referring to the user's past feedback history when collecting feedback. For example, the service provider can adjust the collection method based on the feedback the user has provided in the past. For example, the service provider can collect the user's past feedback data and select the optimal collection method. The service provider can also optimize the feedback collection method based on data of dishes the user has liked in the past. For example, the service provider can collect the user's past dish data and select the optimal feedback collection method. Furthermore, the service provider can adjust the feedback collection method based on data of ingredients the user has avoided in the past. For example, the service provider can collect the user's past ingredient data and select the optimal feedback collection method. This allows the service provider to collect feedback in the most optimal way based on the user's past feedback history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's feedback history data into a generating AI, and the generating AI can select the optimal collection method.

[0051] The service provider can customize feedback based on the user's current eating situation when collecting feedback. For example, if the user is eating, the service provider can collect simple feedback. For example, the service provider can monitor the user's eating situation in real time and collect simple feedback. The service provider can also collect detailed feedback if the user has finished eating. For example, the service provider can monitor the user's situation after eating and collect detailed feedback. Furthermore, if the service provider has eaten a dish containing a specific ingredient, the service provider can collect feedback about that ingredient. For example, the service provider can monitor the user's meal content and collect feedback about that specific ingredient. This allows the service provider to customize feedback based on the user's current eating situation. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's eating situation data into a generating AI, and the generating AI can customize the feedback.

[0052] The service provider can collect optimal feedback by considering the user's geographical location information when collecting feedback. For example, if the user lives in a specific region, the service provider can prioritize collecting feedback on local ingredients and cuisine. For example, the service provider can collect the user's geographical location information and then collect feedback on local ingredients and cuisine. The service provider can also prioritize collecting feedback on local ingredients and cuisine if the user is traveling. For example, the service provider can collect the user's geographical location information and then collect feedback on local ingredients and cuisine. Furthermore, if the user prefers the cuisine of a specific region, the service provider can prioritize collecting feedback on the local ingredients and cuisine. For example, the service provider can collect the user's geographical location information and then collect feedback on the cuisine of that region. This allows the service provider to collect optimal feedback based on the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI, and the generating AI can collect optimal feedback.

[0053] The service provider can collect relevant feedback by analyzing the user's social media activity when collecting feedback. For example, the service provider can collect feedback based on information about dishes the user has shared on social media. For example, the service provider can analyze the user's social media account and collect information about the shared dishes. The service provider can also collect feedback based on information about cooking accounts the user follows on social media. For example, the service provider can collect information about cooking accounts the user follows and collect feedback. Furthermore, the service provider can also collect feedback based on information about dishes the user has "liked" on social media. For example, the service provider can collect information about dishes the user has "liked" and collect feedback. This allows the service provider to collect relevant feedback based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media data into a generating AI and have the generating AI collect relevant feedback.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] When the reception desk collects a user's taste profile, it can gather not only the user's food preferences but also their meal frequency and timing. For example, the reception desk can collect how many times a day a user eats and at what times of day they eat. It can also collect whether the user prefers certain foods on specific days of the week. Furthermore, it can collect whether the user prefers certain foods during specific events or seasons. This allows for the provision of more customized recipes based on the user's eating patterns.

[0056] The analysis unit can consider not only the user's food preferences but also their eating history and patterns when analyzing a user's taste profile. For example, the analysis unit can analyze a user's taste profile based on data of dishes the user has eaten in the past. It can also analyze whether the user likes specific ingredients. Furthermore, it can analyze whether the user likes specific cooking methods. This allows for the creation of a more detailed taste profile based on the user's eating history and patterns.

[0057] The recipe generation unit can consider not only the user's food preferences but also their eating history and patterns when generating recipes based on the user's taste profile. For example, the generation unit can analyze the user's taste profile based on data of dishes the user has eaten in the past and generate recipes. It can also analyze whether the user likes specific ingredients and generate recipes based on that. Furthermore, it can analyze whether the user prefers specific cooking methods and generate recipes based on that. This allows for the provision of more customized recipes based on the user's eating history and patterns.

[0058] When providing recipes based on a user's taste profile, the service provider can consider not only the user's food preferences but also their eating history and patterns. For example, the service provider can analyze a user's taste profile based on data of dishes the user has eaten in the past and provide recipes. Furthermore, the service provider can analyze whether the user likes specific ingredients and provide recipes accordingly. It can also analyze whether the user prefers specific cooking methods and provide recipes based on those. This allows for the provision of more customized recipes based on the user's eating history and patterns.

[0059] The reception desk can collect relevant information by analyzing users' social media activity when gathering user taste profiles. For example, the reception desk can collect taste profiles based on information about dishes that users have shared on social media. It can also collect taste profiles based on information about food accounts that users follow on social media. Furthermore, the reception desk can collect taste profiles based on information about dishes that users have "liked" on social media. This allows the reception desk to collect relevant information based on users' social media activity.

[0060] The analysis unit can analyze a user's social media activity and utilize relevant information in its analysis of the user's taste profile. For example, the analysis unit can analyze a user's taste profile based on information about dishes the user has shared on social media. It can also analyze a user's taste profile based on information about food accounts the user follows on social media. Furthermore, the analysis unit can analyze a user's taste profile based on information about dishes the user has "liked" on social media. This allows the analysis to utilize relevant information based on the user's social media activity.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception desk collects the user's taste profile. The user's taste profile includes favorite and disliked foods, preferred seasonings, etc. The reception desk collects the user's taste profile through questionnaires. It is also possible to collect the taste profile based on past eating history. Furthermore, allergy information can be collected and a taste profile can be created based on the user's health status. Step 2: The analysis unit analyzes the collected taste profiles. The analysis unit uses advanced taste analysis algorithms to analyze the user's taste profile in detail. For example, it may use machine learning algorithms or data mining techniques for analysis. It can also optimize the analysis algorithm based on the user's past feedback. Step 3: The generation unit generates a cooking recipe based on the analysis results. The generation unit creates a recipe tailored to the user's preferences, taking into account factors such as ingredient combinations, cooking methods, and spice selections. For example, it can create a recipe that considers nutritional balance. It can also customize recipes based on the user's current ingredient inventory and geographical location information. Step 4: The serving department prepares the dishes based on the generated recipes. The serving department collects real-time taste feedback and continuously improves the recipes. For example, it collects comments and feedback from users after they try the dishes. It can also estimate the user's emotions and adjust the feedback collection method accordingly. Furthermore, it can collect feedback while considering the user's geographical location.

[0063] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that deeply analyzes a user's taste profile and provides a fully customized cooking recipe based on that preference. This AI agent system creates a perfectly suited dining experience for the user by collecting and analyzing the user's taste profile, generating cooking recipes, and providing them. For example, the AI ​​agent system collects information such as the user's favorite and disliked ingredients and preferred seasonings. Next, the AI ​​agent system analyzes the collected taste profile. The AI ​​agent system uses an advanced taste analysis algorithm to analyze the user's taste profile in detail. For example, if the user likes spicy food, the AI ​​agent system will suggest a spicy dish recipe based on that information. Based on the analysis results, the AI ​​agent system generates the optimal cooking recipe for the user. The AI ​​agent system creates a recipe that suits the user's preferences by considering the combination of ingredients, cooking methods, spice selection, etc. For example, if the user likes spicy food, the AI ​​agent system will suggest a recipe using spicy spices. Furthermore, the AI ​​agent system collects taste feedback in real time. After the user tries the dish, they input their impressions and feedback into the AI ​​agent system. Based on this feedback, the AI ​​agent system continuously improves the recipe. For example, if a user provides feedback such as "I'd like it a little spicier," the AI ​​agent system will adjust the spiciness in the next recipe. This mechanism allows users to enjoy dishes perfectly suited to their preferences. The AI ​​agent system improves individual satisfaction and cooking success rates, and promotes the discovery of new ingredients and cooking methods. It also supports a natural introduction to healthy eating habits. For example, if a user requests a healthy meal, the AI ​​agent system will suggest a nutritionally balanced recipe. The AI ​​agent system provides a new cooking experience by combining taste data and AI technology, realizing a user-centered, interactive cooking process. It prevents cooking monotony and provides dishes tailored to individual health needs for cooking enthusiasts and health-conscious individuals of all ages.For example, if a user has specific health needs, the AI ​​agent system will suggest recipes tailored to those needs. This allows the AI ​​agent system to provide customized cooking recipes based on the user's taste profile.

[0064] The AI ​​agent system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit collects the user's taste profile. The user's taste profile includes, but is not limited to, favorite and disliked ingredients and preferred seasonings. The reception unit collects the user's taste profile, for example, through questionnaires. The reception unit can also collect a taste profile based on the user's past eating history. For example, the reception unit collects data on dishes the user has eaten in the past and creates a taste profile. Furthermore, the reception unit can collect allergy information and create a taste profile based on the user's health status. The analysis unit analyzes the collected taste profiles. The analysis unit uses advanced taste analysis algorithms to analyze the user's taste profile in detail. For example, the analysis unit uses machine learning algorithms to analyze the user's taste profile. The analysis unit can also use data mining techniques to analyze the user's taste profile. Furthermore, the analysis unit can optimize its analysis algorithm based on the user's past feedback. The provision unit generates cooking recipes based on the analysis results. The generation unit creates user-preferred recipes, taking into account factors such as ingredient combinations, cooking methods, and spice selections. For example, the generation unit creates recipes considering nutritional balance. It can also customize recipes based on the user's current ingredient inventory. Furthermore, the generation unit can generate recipes considering the user's geographical location. The serving unit provides the dishes based on the generated recipes. The serving unit collects real-time taste feedback and continuously improves the recipes. For example, the serving unit collects the user's impressions and feedback after they try the dish. It can also estimate the user's emotions and adjust the feedback collection method accordingly. Furthermore, the serving unit can collect feedback considering the user's geographical location. As a result, the AI ​​agent system according to this embodiment can provide customized cooking recipes based on the user's taste profile.

[0065] The reception desk collects the user's taste profile. This profile includes, but is not limited to, information such as favorite and disliked ingredients and preferred seasonings. The reception desk can collect the user's taste profile through, for example, questionnaires. These questionnaires are provided via online forms or applications, making them easy for users to answer. Furthermore, the reception desk can collect taste profiles based on past eating history. For example, it can analyze the dishes a user has ordered in the past and their dining history at restaurants to understand their preferences. The reception desk can also collect the user's allergy information and create a taste profile based on their health status. This information is collected based on medical information and past allergy reaction history provided by the user. This allows the reception desk to create a detailed profile tailored to the user's individual taste and health condition. Additionally, the reception desk can collect feedback on the user's meals and continuously update the taste profile. For example, after a user tries a new dish, they can provide feedback and evaluations to improve the accuracy of the profile. This allows the reception desk to collect comprehensive data on the user's taste and health condition, providing a foundation for personalized services across the entire system.

[0066] The analysis unit uses advanced taste analysis algorithms to analyze the collected taste profiles. For example, it can use machine learning algorithms to analyze the user's taste profile in detail. Machine learning algorithms are used to learn the user's preferences and tendencies and identify patterns. It can also use data mining techniques to analyze the user's taste profile. Data mining techniques are used to extract useful information from large amounts of data and gain insights into the user's taste. Furthermore, the analysis unit can optimize the analysis algorithm based on the user's past feedback. For example, it can analyze the feedback provided by the user and make adjustments to improve the accuracy of the algorithm. This allows the analysis unit to analyze the user's taste profile quickly and accurately, improving the quality of personalized services throughout the system. In addition, the analysis unit can continuously update the user's taste profile and perform analysis based on the latest information. This allows the analysis unit to respond to changes in the user's taste and new preferences, and provide ongoing service.

[0067] The generation unit considers ingredient combinations, cooking methods, and spice selections to generate cooking recipes based on analysis results. For example, the generation unit can create recipes while considering nutritional balance, thereby providing recipes that support the user's health. The generation unit can also customize recipes based on the user's current ingredient inventory. For example, if the user inputs the ingredients in their refrigerator, it can suggest recipes using those ingredients. Furthermore, the generation unit can generate recipes while considering the user's geographical location. For example, it can suggest recipes using local specialties or seasonal ingredients. This allows the generation unit to provide personalized recipes that comprehensively consider the user's preferences, health condition, ingredient inventory, and geographical location. In addition, the generation unit can improve recipes based on the user's past feedback. For example, by analyzing the feedback provided by the user and adjusting the seasoning and cooking methods of the recipes, it can provide more satisfying recipes. This allows the generation unit to continuously provide high-quality recipes that meet the user's needs.

[0068] The service provider collects real-time taste feedback to continuously improve recipes in order to serve dishes based on the generated recipes. For example, the service provider can collect users' impressions and feedback after they try a dish. Users can easily provide feedback through the application. The service provider can also estimate users' emotions and adjust how feedback is collected. For example, by analyzing users' facial expressions and tone of voice to estimate emotions, feedback can be requested at the appropriate time and in the appropriate way. Furthermore, the service provider can collect feedback considering the user's geographical location. For example, feedback specific to a particular region or culture can be collected, and recipes can be localized. This allows the service provider to collect detailed feedback based on users' tastes and emotions, and continuously improve the quality of recipes. In addition, the service provider can provide data to improve the overall service of the system based on user feedback. For example, by analyzing feedback and understanding user needs and trends, it can be used to develop new features and services. This allows the service provider to improve user satisfaction and enhance the overall quality of the system.

[0069] The reception desk can collect information such as the user's favorite and disliked foods and preferred seasonings. For example, the reception desk can collect this information through questionnaires. It can also collect this information based on the user's past eating history. For example, the reception desk can collect data on dishes the user has eaten in the past and create a taste profile. Furthermore, the reception desk can collect allergy information and create a taste profile based on the user's health status. This allows for the collection of a detailed taste profile of the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input questionnaire response data into a generating AI, which can then create the user's taste profile.

[0070] The analysis unit can analyze the user's taste profile in detail using advanced taste analysis algorithms. For example, the analysis unit can analyze the user's taste profile using machine learning algorithms. For instance, the analysis unit inputs the user's taste profile as input data into a machine learning model and outputs the analysis results. The analysis unit can also analyze the user's taste profile using data mining techniques. For example, the analysis unit inputs the user's taste profile into a data mining algorithm and outputs the analysis results. Furthermore, the analysis unit can optimize the analysis algorithm based on the user's past feedback. For example, the analysis unit inputs the user's past feedback data into the analysis algorithm and adjusts the algorithm's parameters. This allows for a detailed analysis of the user's taste profile. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's taste profile into a generating AI, and the generating AI can output the analysis results.

[0071] The generation unit can create user-preferred recipes by considering factors such as ingredient combinations, cooking methods, and spice selections. For example, the generation unit can create recipes considering nutritional balance. For instance, it can use the user's taste profile as input data to generate a nutritionally balanced recipe. The generation unit can also customize recipes based on the user's current ingredient inventory. For example, it can generate recipes based on the ingredients in the user's refrigerator. Furthermore, the generation unit can generate recipes considering the user's geographical location. For example, it can generate recipes using local ingredients based on the user's geographical location. This allows for the creation of recipes tailored to the user's preferences. Some or all of the above-described processes in the generation unit may be performed using AI, or without AI. For example, the generation unit can input the user's taste profile into a generation AI, which can then generate recipes.

[0072] The service provider can collect taste feedback in real time and continuously improve recipes. For example, the service provider can collect feedback from users after they have tried a dish. For example, the service provider can collect feedback through a feedback form after users have tried a dish. The service provider can also estimate the user's emotions and adjust the feedback collection method accordingly. For example, the service provider can estimate the user's emotions and collect detailed feedback if the user is relaxed, and simpler feedback if the user is stressed. Furthermore, the service provider can collect feedback considering the user's geographical location. For example, the service provider can collect feedback on local ingredients and dishes based on the user's geographical location. This allows the service provider to improve recipes based on user feedback. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input user feedback data into a generating AI, which can then improve the recipes.

[0073] The reception unit can estimate the user's emotions and adjust the timing of taste profile collection based on the estimated emotions. For example, if the user is relaxed, the reception unit can immediately begin collecting the taste profile. For example, the reception unit can capture the user's facial expression with a camera and use an emotion estimation algorithm to determine if the user is relaxed. The reception unit can also delay collection if the user is stressed, waiting until the user is relaxed. For example, the reception unit can record the user's voice and use voice analysis technology to estimate the stress level. Furthermore, if the user is busy, the reception unit can adjust the collection timing to match the user's schedule. For example, the reception unit can refer to the user's calendar information to determine the optimal collection timing. This allows for the collection of taste profiles at the optimal time according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input user facial expression data into a generating AI, which can then estimate the user's emotions.

[0074] The reception unit can analyze the user's past eating history and select the optimal data collection method. For example, the reception unit can customize the collection method based on data of dishes the user has previously enjoyed eating. For example, the reception unit can analyze the user's eating history data and identify patterns in the dishes the user has enjoyed. The reception unit can also adjust the collection method considering ingredients the user has previously avoided. For example, the reception unit can create a list of avoided ingredients based on the user's eating history data. Furthermore, the reception unit can analyze the user's eating patterns and determine the optimal timing for data collection. For example, the reception unit can analyze the user's eating patterns and identify the optimal timing for data collection. This allows for the collection of a taste profile in the most optimal way based on the user's past eating history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's eating history data into a generating AI, which can then select the optimal data collection method.

[0075] The reception unit can filter the collected taste profiles based on the user's current health status and dietary restrictions. For example, if the user has allergies, the reception unit can filter the collected data based on that information. For example, the reception unit can collect the user's allergy information and exclude foods that cause allergies. The reception unit can also prioritize collecting low-calorie foods if the user is on a diet. For example, the reception unit can collect the user's diet information and list low-calorie foods. Furthermore, if the user requires specific nutrients, the reception unit can collect foods containing those nutrients. For example, the reception unit can collect the user's nutrient needs and list foods containing those nutrients. This allows for the collection of taste profiles tailored to the user's health status and dietary restrictions. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's health information into a generating AI, which can then perform the filtering.

[0076] The reception system can estimate the user's emotions and prioritize the taste profiles to collect based on those emotions. For example, if the user is relaxed, the reception system will prioritize collecting detailed taste profiles. For instance, it might capture the user's facial expressions with a camera and use an emotion estimation algorithm to determine if the user is relaxed. Alternatively, if the user is stressed, the reception system might prioritize collecting basic taste profiles. For example, it might record the user's voice and use voice analysis technology to estimate their stress level. Furthermore, if the user is in a hurry, the reception system might collect taste profiles through simple questions. For example, it might refer to the user's schedule information to determine the optimal collection timing. This allows the system to prioritize the taste profiles to collect according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input user facial expression data into a generating AI, which can then estimate the user's emotions.

[0077] The reception unit can prioritize collecting highly relevant information when gathering taste profiles, taking into account the user's geographical location. For example, if the user lives in a specific region, the reception unit can prioritize collecting information about local ingredients and cuisine. For example, the reception unit can collect information about local ingredients and cuisine based on the user's geographical location. The reception unit can also prioritize collecting information about local ingredients and cuisine if the user is traveling. For example, the reception unit can collect information about local ingredients and cuisine based on the user's geographical location. Furthermore, if the user prefers the cuisine of a specific region, the reception unit can prioritize collecting information about the cuisine of that region. For example, the reception unit can collect information about the cuisine of that region based on the user's geographical location. This allows for the collection of highly relevant information based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location into a generating AI, which can then collect highly relevant information.

[0078] The reception unit can collect relevant information by analyzing the user's social media activity when collecting taste profiles. For example, the reception unit can collect taste profiles based on information about dishes the user has shared on social media. For example, the reception unit can analyze the user's social media accounts and collect information about the shared dishes. The reception unit can also collect taste profiles based on information about food accounts the user follows on social media. For example, the reception unit can collect information about food accounts the user follows and create a taste profile. Furthermore, the reception unit can also collect taste profiles based on information about dishes the user has "liked" on social media. For example, the reception unit can collect information about dishes the user has "liked" and create a taste profile. This allows the reception unit to collect relevant information based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media data into a generating AI, and the generating AI can collect relevant information.

[0079] The analysis unit can estimate the user's emotions and adjust the taste profile analysis method based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide highly accurate results. For example, the analysis unit can capture the user's facial expression with a camera, use an emotion estimation algorithm to determine if the user is relaxed, and perform a detailed analysis. The analysis unit can also perform a simplified analysis and provide results quickly if the user is stressed. For example, the analysis unit can record the user's voice, use voice analysis technology to estimate the stress level, and perform a simplified analysis. Furthermore, if the user is in a hurry, the analysis unit can perform a basic analysis and provide results in a short time. For example, the analysis unit can refer to the user's schedule information and select the optimal analysis method. This allows the taste profile to be analyzed in the most optimal way according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user facial expression data into a generating AI, and the generating AI can estimate emotions.

[0080] The analysis unit can optimize its analysis algorithm by referring to the user's past feedback when analyzing the taste profile. For example, the analysis unit adjusts the analysis algorithm based on feedback previously provided by the user. For example, the analysis unit inputs the user's past feedback data into the analysis algorithm and adjusts the algorithm's parameters. The analysis unit can also optimize the analysis algorithm based on data of dishes the user has previously liked. For example, the analysis unit inputs the user's past cooking data into the analysis algorithm and adjusts the algorithm to obtain the optimal analysis result. Furthermore, the analysis unit can adjust the analysis algorithm based on data of ingredients the user has previously avoided. For example, the analysis unit inputs the user's past ingredient data into the analysis algorithm and performs the analysis taking the avoided ingredients into consideration. This allows the analysis algorithm to be optimized based on the user's past feedback. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's feedback data into a generating AI and optimize the analysis algorithm using the generating AI.

[0081] The analysis unit can improve the accuracy of its taste profile analysis based on the user's eating history. For example, the analysis unit can improve the accuracy of its analysis based on data of dishes the user has eaten in the past. For example, the analysis unit inputs the user's eating history data into an analysis algorithm to obtain highly accurate analysis results. The analysis unit can also improve the accuracy of its analysis by analyzing the user's eating patterns. For example, the analysis unit inputs the user's eating pattern data into an analysis algorithm and adjusts the algorithm to obtain the optimal analysis results. Furthermore, the analysis unit can improve the accuracy of its analysis based on data of ingredients the user has avoided in the past. For example, the analysis unit inputs the user's past ingredient data into an analysis algorithm and performs the analysis while considering the avoided ingredients. This allows the analysis to improve accuracy based on the user's eating history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's eating history data into a generating AI, and the generating AI can improve the accuracy of the analysis.

[0082] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is relaxed, the analysis unit can display detailed analysis results. For example, the analysis unit can capture the user's facial expression with a camera, use an emotion estimation algorithm to determine if the user is relaxed, and then display detailed analysis results. The analysis unit can also display simplified analysis results if the user is stressed. For example, the analysis unit can record the user's voice, use voice analysis technology to estimate the stress level, and then display simplified analysis results. Furthermore, if the user is in a hurry, the analysis unit can display concise analysis results. For example, the analysis unit can refer to the user's schedule information and select the optimal display method. This allows the analysis results to be displayed in the most appropriate way according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user facial expression data into a generating AI, which can then use the generating AI to estimate emotions.

[0083] The analysis unit can perform taste profile analysis while taking into account the user's lifestyle. For example, if the user has a healthy lifestyle, the analysis unit can perform analysis based on that information. For example, the analysis unit can collect user lifestyle data and perform analysis based on data from users with a healthy lifestyle. The analysis unit can also perform analysis based on information if the user has specific dietary restrictions. For example, the analysis unit can collect user dietary restriction data and perform analysis based on data from users with specific dietary restrictions. Furthermore, the analysis unit can also perform analysis based on information if the user has a specific eating pattern. For example, the analysis unit can collect user eating pattern data and perform analysis based on data from users with specific eating patterns. This allows analysis to be performed based on the user's lifestyle. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user lifestyle data into a generating AI and have the generating AI perform the analysis.

[0084] The analysis unit can improve the accuracy of its analysis by referring to the user's health data when analyzing the taste profile. For example, the analysis unit can improve the accuracy of its analysis based on the user's health data. For example, the analysis unit can collect the user's health data and perform analysis based on that data. The analysis unit can also improve the accuracy of its analysis by considering the user's health status. For example, the analysis unit can collect the user's health status data and perform analysis considering the health status. Furthermore, the analysis unit can also improve the accuracy of its analysis based on the user's health goals. For example, the analysis unit can collect the user's health goal data and perform analysis based on the health goals. This allows the analysis to improve accuracy based on the user's health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's health data into a generating AI, and the generating AI can improve the accuracy of the analysis.

[0085] The generation unit can estimate the user's emotions and adjust the recipe generation method based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a detailed recipe. For example, the generation unit can capture the user's facial expression with a camera, use an emotion estimation algorithm to determine if the user is relaxed, and then generate a detailed recipe. The generation unit can also generate a simple recipe if the user is stressed. For example, the generation unit can record the user's voice, use voice analysis technology to estimate the stress level, and then generate a simple recipe. Furthermore, if the user is in a hurry, the generation unit can generate a recipe that can be made in a short time. For example, the generation unit can refer to the user's schedule information and select the most suitable recipe. This allows for the generation of recipes in the most optimal way according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user facial expression data into a generation AI, which can then estimate emotions.

[0086] The generation unit can generate the optimal recipe by referring to the user's past cooking history when generating a recipe. For example, the generation unit can generate the optimal recipe based on data of dishes the user has liked in the past. For example, the generation unit can collect the user's past cooking data and identify patterns of dishes the user has liked. The generation unit can also generate the optimal recipe based on data of ingredients the user has avoided in the past. For example, the generation unit can collect the user's past ingredient data and generate a recipe considering the ingredients the user avoided. Furthermore, the generation unit can analyze the user's cooking patterns and generate the optimal recipe. For example, the generation unit can collect the user's cooking pattern data and generate the optimal recipe. This allows the generation of the optimal recipe to be generated based on the user's past cooking history. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's cooking history data into a generation AI, and the generation AI can generate the optimal recipe.

[0087] The generation unit can customize recipes based on the user's current ingredient inventory when generating recipes. For example, the generation unit can customize recipes based on the ingredients in the user's refrigerator. For example, the generation unit can collect the user's refrigerator inventory data and generate recipes based on the inventory ingredients. The generation unit can also customize recipes based on the spices the user possesses. For example, the generation unit can collect the user's spice data and generate recipes based on the spices the user possesses. Furthermore, the generation unit can also customize recipes based on ingredients the user has just purchased. For example, the generation unit can collect the user's purchase history data and generate recipes based on the purchased ingredients. This allows for the customization of recipes based on the user's current ingredient inventory. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's ingredient inventory data into a generation AI, which can then customize the recipes.

[0088] The generation unit can estimate the user's emotions and adjust how recipes are displayed based on the estimated emotions. For example, if the user is relaxed, the generation unit can display a detailed recipe. For example, the generation unit can capture the user's facial expression with a camera, use an emotion estimation algorithm to determine if the user is relaxed, and then display a detailed recipe. The generation unit can also display a simplified recipe if the user is stressed. For example, the generation unit can record the user's voice, use voice analysis technology to estimate the stress level, and then display a simplified recipe. Furthermore, if the user is in a hurry, the generation unit can display a concise recipe. For example, the generation unit can refer to the user's schedule information and select the optimal display method. This allows recipes to be displayed in the most appropriate way according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user facial expression data into a generation AI, which can then estimate emotions.

[0089] The generation unit can generate the optimal recipe by considering the user's geographical location information. For example, if the user lives in a specific region, the generation unit can generate a recipe using ingredients from that region. For example, the generation unit collects the user's geographical location information and generates a recipe based on the ingredients of that region. The generation unit can also generate a recipe using ingredients from the user's travel destination if the user is traveling. For example, the generation unit collects the user's geographical location information and generates a recipe based on the ingredients of the travel destination. Furthermore, if the user prefers the cuisine of a specific region, the generation unit can generate a recipe using ingredients from that region. For example, the generation unit collects the user's geographical location information and generates a recipe based on the cuisine of that region. This allows the generation unit to generate the optimal recipe based on the user's geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information into a generation AI, which can then generate the optimal recipe.

[0090] The generation unit can analyze the user's social media activity and suggest relevant recipes when generating recipes. For example, the generation unit can generate recipes based on information about dishes the user has shared on social media. For example, the generation unit can analyze the user's social media accounts and collect information about shared dishes. The generation unit can also generate recipes based on information about cooking accounts the user follows on social media. For example, the generation unit can collect information about cooking accounts the user follows and generate recipes. Furthermore, the generation unit can generate recipes based on information about dishes the user has "liked" on social media. For example, the generation unit can collect information about dishes the user has "liked" and generate recipes. This allows the system to suggest relevant recipes based on the user's social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media data into a generation AI, which can then suggest relevant recipes.

[0091] The service provider can estimate the user's emotions and adjust the feedback collection method based on the estimated emotions. For example, if the user is relaxed, the service provider can collect detailed feedback. For example, the service provider can capture the user's facial expressions with a camera, use an emotion estimation algorithm to determine if the user is relaxed, and collect detailed feedback. The service provider can also collect simple feedback if the user is stressed. For example, the service provider can record the user's voice, use voice analysis technology to estimate the stress level, and collect simple feedback. Furthermore, if the user is in a hurry, the service provider can collect feedback that can be answered quickly. For example, the service provider can refer to the user's schedule information and select the optimal collection method. This allows for the collection of feedback in the most appropriate way according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provision unit may be performed using AI, for example, or without AI. For example, the service provision unit can input user facial expression data into a generating AI, and the generating AI can estimate emotions.

[0092] The service provider can select the optimal collection method by referring to the user's past feedback history when collecting feedback. For example, the service provider can adjust the collection method based on the feedback the user has provided in the past. For example, the service provider can collect the user's past feedback data and select the optimal collection method. The service provider can also optimize the feedback collection method based on data of dishes the user has liked in the past. For example, the service provider can collect the user's past dish data and select the optimal feedback collection method. Furthermore, the service provider can adjust the feedback collection method based on data of ingredients the user has avoided in the past. For example, the service provider can collect the user's past ingredient data and select the optimal feedback collection method. This allows the service provider to collect feedback in the most optimal way based on the user's past feedback history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's feedback history data into a generating AI, and the generating AI can select the optimal collection method.

[0093] The service provider can customize feedback based on the user's current eating situation when collecting feedback. For example, if the user is eating, the service provider can collect simple feedback. For example, the service provider can monitor the user's eating situation in real time and collect simple feedback. The service provider can also collect detailed feedback if the user has finished eating. For example, the service provider can monitor the user's situation after eating and collect detailed feedback. Furthermore, if the service provider has eaten a dish containing a specific ingredient, the service provider can collect feedback about that ingredient. For example, the service provider can monitor the user's meal content and collect feedback about that specific ingredient. This allows the service provider to customize feedback based on the user's current eating situation. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's eating situation data into a generating AI, and the generating AI can customize the feedback.

[0094] The service provider can estimate the user's emotions and prioritize feedback based on those emotions. For example, if the user is relaxed, the service provider will prioritize collecting detailed feedback. For instance, it might capture the user's facial expressions with a camera, use an emotion estimation algorithm to determine if the user is relaxed, and prioritize collecting detailed feedback. The service provider can also prioritize collecting basic feedback if the user is stressed. For example, it might record the user's voice, use voice analysis technology to estimate the stress level, and prioritize collecting basic feedback. Furthermore, if the user is in a hurry, the service provider can collect feedback through simple questions. For example, it might refer to the user's schedule information and select the optimal collection method. This allows the service provider to prioritize feedback according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provision unit may be performed using AI, for example, or without AI. For example, the service provision unit can input user facial expression data into a generating AI, and the generating AI can estimate emotions.

[0095] The service provider can collect optimal feedback by considering the user's geographical location information when collecting feedback. For example, if the user lives in a specific region, the service provider can prioritize collecting feedback on local ingredients and cuisine. For example, the service provider can collect the user's geographical location information and then collect feedback on local ingredients and cuisine. The service provider can also prioritize collecting feedback on local ingredients and cuisine if the user is traveling. For example, the service provider can collect the user's geographical location information and then collect feedback on local ingredients and cuisine. Furthermore, if the user prefers the cuisine of a specific region, the service provider can prioritize collecting feedback on the local ingredients and cuisine. For example, the service provider can collect the user's geographical location information and then collect feedback on the cuisine of that region. This allows the service provider to collect optimal feedback based on the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI, and the generating AI can collect optimal feedback.

[0096] The service provider can collect relevant feedback by analyzing the user's social media activity when collecting feedback. For example, the service provider can collect feedback based on information about dishes the user has shared on social media. For example, the service provider can analyze the user's social media account and collect information about the shared dishes. The service provider can also collect feedback based on information about cooking accounts the user follows on social media. For example, the service provider can collect information about cooking accounts the user follows and collect feedback. Furthermore, the service provider can also collect feedback based on information about dishes the user has "liked" on social media. For example, the service provider can collect information about dishes the user has "liked" and collect feedback. This allows the service provider to collect relevant feedback based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media data into a generating AI and have the generating AI collect relevant feedback.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] When the reception desk collects a user's taste profile, it can gather not only the user's food preferences but also their meal frequency and timing. For example, the reception desk can collect how many times a day a user eats and at what times of day they eat. It can also collect whether the user prefers certain foods on specific days of the week. Furthermore, it can collect whether the user prefers certain foods during specific events or seasons. This allows for the provision of more customized recipes based on the user's eating patterns.

[0099] The analysis unit can consider not only the user's food preferences but also their eating history and patterns when analyzing a user's taste profile. For example, the analysis unit can analyze a user's taste profile based on data of dishes the user has eaten in the past. It can also analyze whether the user likes specific ingredients. Furthermore, it can analyze whether the user likes specific cooking methods. This allows for the creation of a more detailed taste profile based on the user's eating history and patterns.

[0100] The recipe generation unit can consider not only the user's food preferences but also their eating history and patterns when generating recipes based on the user's taste profile. For example, the generation unit can analyze the user's taste profile based on data of dishes the user has eaten in the past and generate recipes. It can also analyze whether the user likes specific ingredients and generate recipes based on that. Furthermore, it can analyze whether the user prefers specific cooking methods and generate recipes based on that. This allows for the provision of more customized recipes based on the user's eating history and patterns.

[0101] When providing recipes based on a user's taste profile, the service provider can consider not only the user's food preferences but also their eating history and patterns. For example, the service provider can analyze a user's taste profile based on data of dishes the user has eaten in the past and provide recipes. Furthermore, the service provider can analyze whether the user likes specific ingredients and provide recipes accordingly. It can also analyze whether the user prefers specific cooking methods and provide recipes based on those. This allows for the provision of more customized recipes based on the user's eating history and patterns.

[0102] The reception desk can estimate the user's emotions when collecting their taste profile and adjust the collection method based on those emotions. For example, if the user is relaxed, the reception desk can ask detailed questions to collect their taste profile. If the user is stressed, the reception desk can ask simple questions to collect their taste profile. Furthermore, if the user is in a hurry, the reception desk can ask questions that can be answered quickly to collect their taste profile. This allows for the collection of taste profiles in the most optimal way according to the user's emotions.

[0103] The analysis unit can estimate the user's emotions when analyzing the user's taste profile and adjust the analysis method based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis to analyze the user's taste profile. If the user is stressed, the analysis unit can perform a simplified analysis to analyze the user's taste profile. Furthermore, if the user is in a hurry, the analysis unit can perform a quick analysis to analyze the user's taste profile. This allows the taste profile to be analyzed in the most optimal way according to the user's emotions.

[0104] The generation unit can estimate the user's emotions when generating recipes based on the user's taste profile, and adjust the recipe generation method based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a detailed recipe. If the user is stressed, it can also generate a simple recipe. Furthermore, if the user is in a hurry, it can generate a recipe that can be made in a short time. This allows for the generation of recipes in the most optimal way according to the user's emotions.

[0105] The service provider can estimate the user's emotions when providing recipes based on the user's taste profile, and adjust the recipe delivery method based on the estimated emotions. For example, if the user is relaxed, the service provider can provide a detailed recipe. If the user is stressed, the service provider can also provide a simple recipe. Furthermore, if the user is in a hurry, the service provider can provide a recipe that can be made in a short time. This allows the service provider to deliver recipes in the most optimal way according to the user's emotions.

[0106] The reception desk can collect relevant information by analyzing users' social media activity when gathering user taste profiles. For example, the reception desk can collect taste profiles based on information about dishes that users have shared on social media. It can also collect taste profiles based on information about food accounts that users follow on social media. Furthermore, the reception desk can collect taste profiles based on information about dishes that users have "liked" on social media. This allows the reception desk to collect relevant information based on users' social media activity.

[0107] The analysis unit can analyze a user's social media activity and utilize relevant information in its analysis of the user's taste profile. For example, the analysis unit can analyze a user's taste profile based on information about dishes the user has shared on social media. It can also analyze a user's taste profile based on information about food accounts the user follows on social media. Furthermore, the analysis unit can analyze a user's taste profile based on information about dishes the user has "liked" on social media. This allows the analysis to utilize relevant information based on the user's social media activity.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The reception desk collects the user's taste profile. The user's taste profile includes favorite and disliked foods, preferred seasonings, etc. The reception desk collects the user's taste profile through questionnaires. It is also possible to collect the taste profile based on past eating history. Furthermore, allergy information can be collected and a taste profile can be created based on the user's health status. Step 2: The analysis unit analyzes the collected taste profiles. The analysis unit uses advanced taste analysis algorithms to analyze the user's taste profile in detail. For example, it may use machine learning algorithms or data mining techniques for analysis. It can also optimize the analysis algorithm based on the user's past feedback. Step 3: The generation unit generates a cooking recipe based on the analysis results. The generation unit creates a recipe tailored to the user's preferences, taking into account factors such as ingredient combinations, cooking methods, and spice selections. For example, it can create a recipe that considers nutritional balance. It can also customize recipes based on the user's current ingredient inventory and geographical location information. Step 4: The serving department prepares the dishes based on the generated recipes. The serving department collects real-time taste feedback and continuously improves the recipes. For example, it collects comments and feedback from users after they try the dishes. It can also estimate the user's emotions and adjust the feedback collection method accordingly. Furthermore, it can collect feedback while considering the user's geographical location.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and serving unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and collects the user's taste profile. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected taste profile. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates a cooking recipe based on the analysis results. The serving unit is implemented by the control unit 46A of the smart device 14 and serves the dish based on the generated recipe and collects taste feedback in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and serving unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and collects the user's taste profile. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected taste profile. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates a cooking recipe based on the analysis results. The serving unit is implemented by the control unit 46A of the smart glasses 214 and serves the dish based on the generated recipe and collects taste feedback in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and serving unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and collects the user's taste profile. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected taste profile. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates a cooking recipe based on the analysis results. The serving unit is implemented by the control unit 46A of the headset terminal 314 and serves the dish based on the generated recipe and collects taste feedback in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and serving unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and collects the user's taste profile. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the collected taste profile. The generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and generates a cooking recipe based on the analysis results. The serving unit is implemented by, for example, the control unit 46A of the robot 414 and serves the dish based on the generated recipe and collects taste feedback in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0163] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0165] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0166] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0167] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0172] 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.

[0173] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0174] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0175] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0176] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0180] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0181] (Note 1) A reception area that collects users' taste profiles, An analysis unit analyzes the taste profile collected by the reception unit, A generation unit generates a cooking recipe based on the analysis results obtained by the analysis unit, The system includes a serving unit that provides a dish based on a recipe generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is We collect information about users' favorite and disliked ingredients, preferred seasonings, and more. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Using advanced taste analysis algorithms, we analyze the user's taste profile in detail. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is We create user-friendly recipes, taking into account ingredient combinations, cooking methods, spice selections, and more. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Collect real-time taste feedback to continuously improve recipes. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of taste profile collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the user's past meal history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When collecting taste profiles, filtering is performed based on the user's current health status and dietary restrictions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and determines the priority of taste profiles to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When collecting taste profiles, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When collecting taste profiles, we analyze users' social media activity and gather relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the taste profile analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, When analyzing taste profiles, the analysis algorithm is optimized by referring to the user's past feedback. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, When analyzing taste profiles, the accuracy of the analysis is improved based on the user's eating history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, When analyzing taste profiles, the analysis takes into account the user's lifestyle habits. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, When analyzing taste profiles, we refer to the user's health data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is It estimates the user's emotions and adjusts the recipe generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating a recipe, the system references the user's past cooking history to generate the most suitable recipe. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating a recipe, customize the recipe based on the user's current ingredient inventory. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates the user's emotions and adjusts how recipes are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating recipes, the system takes the user's geographical location into consideration to generate the most suitable recipe. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating recipes, the system analyzes the user's social media activity and suggests relevant recipes. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, We estimate the user's emotions and adjust the feedback collection method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When collecting feedback, the system selects the optimal collection method by referring to the user's past feedback history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When collecting feedback, customize the feedback based on the user's current eating habits. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When collecting feedback, the system takes into account the user's geographical location to collect the most relevant feedback. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When collecting feedback, analyze users' social media activity to gather relevant feedback. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that collects users' taste profiles, An analysis unit analyzes the taste profile collected by the reception unit, A generation unit generates a cooking recipe based on the analysis results obtained by the analysis unit, The system includes a serving unit that provides a dish based on a recipe generated by the generation unit. A system characterized by the following features.

2. The aforementioned reception unit is We collect information about users' favorite and disliked ingredients, preferred seasonings, and more. The system according to feature 1.

3. The aforementioned analysis unit, Using advanced taste analysis algorithms, we analyze the user's taste profile in detail. The system according to feature 1.

4. The generating unit is We create user-friendly recipes, taking into account ingredient combinations, cooking methods, spice selections, and more. The system according to feature 1.

5. The aforementioned supply unit is, Collect real-time taste feedback to continuously improve recipes. The system according to feature 1.

6. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of taste profile collection based on the estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyze the user's past meal history and select the optimal data collection method. The system according to feature 1.

8. The aforementioned reception unit is When collecting taste profiles, filtering is performed based on the user's current health status and dietary restrictions. The system according to feature 1.

Images