system

Abstract

The system of the embodiment aims to provide a customized meal plan based on the user's preferences and constraints. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, a provision unit, and a learning unit. The reception unit receives input of a user's preferences or constraints. The analysis unit analyzes the information received by the reception unit. The generation unit generates a customized meal plan based on the information analyzed by the analysis unit. The provision unit provides the meal plan generated by the generation unit. The learning unit learns user feedback based on the meal plan provided by the provision unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to provide individually customized meal plans based on a user's preferences and constraints.

[0005] The system of the embodiment aims to provide a customized meal plan based on the user's preferences and constraints. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, a provision unit, and a learning unit. The reception unit receives input of a user's preferences or constraints. The analysis unit analyzes the information received by the reception unit. The generation unit generates a customized meal plan based on the information analyzed by the analysis unit. The provision unit provides the meal plan generated by the generation unit. The learning unit learns user feedback based on the meal plan provided by the provision unit. [Effects of the Invention]

[0007] An embodiment of the system can provide a customized meal plan based on a user's preferences and constraints. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A menu concierge system according to an embodiment of the present invention utilizes generative AI to understand a user's preferences and constraints and propose individually customized meal plans. The menu concierge system accepts user preferences and constraints as input, analyzes the information, and generates an optimal meal plan for the user. For example, if a user inputs "I'm looking for a gluten-free, high-protein diet," the generative AI will suggest appropriate ingredients and recipes based on that request. The menu concierge system also accommodates specific needs and allergies, supporting sustainable dietary choices. The generative AI's advanced natural language processing technology accurately understands the user's input and provides customized meal plans. Furthermore, the generative AI learns from the user's past choices and feedback to provide more accurate suggestions. For example, it optimizes its next suggestions based on the user's past menu choices and their ratings. This allows users to lead a healthy and fulfilling eating lifestyle. The menu concierge system also promotes the selection of sustainable ingredients and proposes environmentally friendly meals. For example, by prioritizing locally produced and seasonal ingredients, it reduces food miles and reduces environmental impact. This allows the menu concierge system to provide customized meal plans based on the user's preferences and constraints, enabling them to achieve a healthy and fulfilling dietary lifestyle.

[0029] A menu concierge system according to an embodiment includes a reception unit, an analysis unit, a generation unit, a provision unit, and a learning unit. The reception unit receives input of a user's preferences or constraints. Examples of the user's preferences or constraints include, but are not limited to, allergy information, dietary preferences, and calorie restrictions. The reception unit receives information entered by the user in text format. The reception unit can also receive information using voice input or image input. For example, the reception unit converts the user's voice input into text data using voice recognition technology. The reception unit can also extract information from images uploaded by the user using image analysis technology. The analysis unit analyzes the information received by the reception unit. The analysis is performed using, for example, but is not limited to, data analysis techniques and algorithms. For example, the analysis unit analyzes the user's preferences and constraints using machine learning algorithms. The analysis unit can also analyze the text data using natural language processing technology. The generation unit generates a customized meal plan based on the information analyzed by the analysis unit. The customized meal plan is generated based on, for example, nutritional balance and ingredient selection criteria, but is not limited to, such examples. For example, the generation unit generates an optimal meal plan for the user using a generation AI. The generation unit can also generate a meal plan tailored to the user's health condition and lifestyle. The provision unit provides the meal plan generated by the generation unit. The provision can be performed, for example, by digital distribution or by providing a printed document, but is not limited to such examples. For example, the provision unit can provide the meal plan through a web application or a mobile application. The provision unit can also provide the meal plan via email or a messaging app. The learning unit learns from the user's feedback based on the meal plan provided by the provision unit. The learning can be performed, for example, by using a machine learning algorithm, but is not limited to such examples. For example, the learning unit can analyze the user's past selections and feedback to optimize the next suggestion. The learning unit can also update the learning data based on the user's evaluation comments and selection history.As a result, the menu concierge system according to the embodiment can provide a meal plan customized based on the user's preferences and constraints, enabling the user to realize a healthy and fulfilling eating lifestyle.

[0030] The provision unit can suggest sustainable ingredients. Examples of sustainable ingredients include, but are not limited to, organically grown, locally produced, and seasonal ingredients. The provision unit, for example, prioritizes the suggestion of sustainable ingredients. The provision unit can also suggest locally produced ingredients and seasonal ingredients. For example, the provision unit can suggest local agricultural products and seasonal vegetables. This supports environmentally conscious meal choices by suggesting sustainable ingredients. Some or all of the above-described processing in the provision unit may be performed, for example, using or without the generation AI. For example, the provision unit can input a database of sustainable ingredients into the generation AI, which can then suggest the most suitable ingredients.

[0031] The analysis unit can analyze the user's past selections and feedback. Examples of past selections and feedback include, but are not limited to, selection history and user evaluation comments. The analysis unit, for example, analyzes the user's past selection history. The analysis unit can also analyze the user's feedback. For example, the analysis unit performs analysis based on the user's past menu selections and their evaluations. The analysis unit can also analyze the user's evaluation comments to optimize the next suggestion. By analyzing the user's past selections and feedback, a more accurate meal plan can be provided. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the user's past selection history into the generation AI, which then performs the analysis.

[0032] The generation unit can generate a meal plan tailored to the user's health condition and lifestyle. Examples of health condition and lifestyle include, but are not limited to, medical history, exercise habits, and daily routines. The generation unit generates a meal plan based on, for example, the user's health checkup results. The generation unit can also generate a meal plan based on the user's exercise habits. For example, the generation unit can adjust the meal plan according to the frequency and intensity of the user's exercise. The generation unit can also generate a meal plan tailored to the user's daily routine. For example, if the user works the night shift, the generation unit can suggest a meal plan suited to that daily routine. This provides a meal plan tailored to the user's health condition and lifestyle, thereby realizing an individually customized meal experience. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's health checkup results into the generation AI, which then generates an optimal meal plan.

[0033] The provision unit can prioritize locally produced ingredients or seasonal ingredients. Locally produced ingredients and seasonal ingredients include, but are not limited to, regional agricultural products and seasonal vegetables. The provision unit, for example, prioritizes locally produced agricultural products. The provision unit can also suggest seasonal ingredients. For example, the provision unit suggests spring vegetables in spring and summer vegetables in summer. This prioritizes the suggestion of locally produced ingredients and seasonal ingredients, thereby reducing food miles and mitigating environmental impact. Some or all of the above-described processing in the provision unit may be performed using, or without, a generation AI. For example, the provision unit can input a database of locally produced ingredients and seasonal ingredients into the generation AI, which can then suggest the most suitable ingredients.

[0034] The learning unit can learn the user's past selections and feedback and optimize the next suggestion. For example, a machine learning algorithm is used for learning, but the learning is not limited to this example. For example, the learning unit learns the user's past selection history. The learning unit can also learn the user's feedback. For example, the learning unit performs learning based on menus the user has previously selected and their ratings. The learning unit can also study the user's rating comments and optimize the next suggestion. In this way, by learning the user's past selections and feedback, the accuracy of the next suggestion is improved. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit can input the user's past selection history into the generation AI, which then performs learning.

[0035] The reception unit can analyze the user's past input history and provide an optimal input interface. For example, the reception unit can automatically display preferences and constraints that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit can predict and suggest preferences and constraints to be used in a specific time period based on the user's past input history. In this way, the optimal input interface is provided by analyzing the user's past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past input history into a generation AI, which can then suggest an optimal input interface.

[0036] The reception unit can filter the input content based on the user's current health condition and lifestyle. For example, if the user inputs the results of a health checkup, the reception unit can suggest appropriate dietary restrictions based on that information. Furthermore, if the user has a specific lifestyle habit (night shift, exercise habit, etc.), the reception unit can also suggest a meal plan that suits that habit. For example, if the user inputs their current physical condition (fatigue, stress, etc.), the reception unit can suggest a meal plan that suits that condition. In this way, by filtering the input content based on the user's health condition and lifestyle, a more appropriate meal plan can be provided. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's health checkup results into the generation AI, which can then suggest appropriate dietary restrictions.

[0037] The reception unit can select the optimal input means depending on the user's input method (voice, text, image, etc.). For example, when the user inputs preferences and constraints by voice, the reception unit analyzes the input content using voice recognition technology. Furthermore, when the user inputs preferences and constraints by text, the reception unit can also analyze the input content using natural language processing technology. For example, when the user uploads an image (e.g., a photo of a meal), the reception unit identifies ingredients and nutrients using image analysis technology. This improves input convenience by selecting the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's voice data to a generation AI, which then performs voice recognition.

[0038] The reception unit can prioritize receiving highly relevant input content by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize suggesting ingredients available in that area. Furthermore, if the user is traveling, the reception unit can also suggest local specialties and dishes from the travel destination. For example, if the user is at home, the reception unit can suggest ingredients that can be purchased at a nearby supermarket. This provides a more relevant meal plan by taking into account the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information into the generation AI, which can then suggest the most suitable ingredients.

[0039] The reception unit can analyze the user's social media activity and suggest related input content. For example, the reception unit can infer preferences and restrictions based on photos of meals shared by the user on social media. The reception unit can also analyze the user's social media posts and suggest related ingredients and recipes. For example, the reception unit can suggest related meal plans based on the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, more relevant meal plans can be provided. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's social media data into the generation AI, which then performs the analysis.

[0040] The reception unit can customize the input method by reflecting the user's past feedback. For example, the reception unit preferentially suggests input methods (such as voice and text) that the user has previously preferred. The reception unit can also improve the input interface based on the user's past feedback. For example, the reception unit simplifies the input procedure by referring to content that the user has previously input. This provides a more appropriate input method by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past feedback data into the generation AI, which then suggests the optimal input method.

[0041] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past selections and feedback. The analysis unit improves the accuracy of the analysis, for example, based on ingredients and recipes selected by the user in the past. The analysis unit can also optimize the analysis algorithm by reflecting the user's past feedback. For example, the analysis unit analyzes the user's past selection history and applies the optimal analysis method. This improves the accuracy of the analysis by referring to the user's past selections and feedback. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's past selection history into the generation AI, and the generation AI can perform the analysis.

[0042] During analysis, the analysis unit can optimize the analysis algorithm based on the user's health condition and lifestyle. For example, if the user has a specific health condition (e.g., diabetes), the analysis unit applies an analysis algorithm appropriate for that condition. The analysis unit can also optimize the analysis algorithm based on the user's lifestyle (e.g., vegetarian). For example, the analysis unit adjusts the analysis algorithm based on the user's health checkup results. This optimizes the analysis algorithm based on the user's health condition and lifestyle, thereby providing more appropriate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's health checkup results into the generation AI, which then applies the optimal analysis algorithm.

[0043] During analysis, the analysis unit can apply different analysis methods depending on the category of the user's input content. For example, if the user inputs allergy information, the analysis unit will prioritize analysis of ingredients that do not contain allergens. Furthermore, if the user inputs intake restrictions for specific nutrients, the analysis unit can also adjust the analysis method based on those restrictions. For example, if the user inputs a favorite ingredient, the analysis unit will focus on that ingredient in the analysis. This provides more appropriate analysis results by applying different analysis methods depending on the category of the user's input content. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's input content into a generation AI, which then applies the optimal analysis method.

[0044] During analysis, the analysis unit can adjust the analysis content taking into account the user's geographical location information. For example, if the user is in a specific area, the analysis unit prioritizes analysis of ingredients available in that area. Furthermore, if the user is traveling, the analysis unit can also analyze local specialties and local cuisine of the travel destination. For example, if the user is at home, the analysis unit analyzes ingredients available at a nearby supermarket. This provides more relevant analysis results by taking the user's geographical location information into account. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's geographical location information into the generation AI, which can then adjust the analysis content to be optimal.

[0045] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's social media activity. For example, the analysis unit infers preferences and restrictions based on photos of meals shared by the user on social media. The analysis unit can also analyze the content of the user's social media posts to analyze related ingredients and recipes. For example, the analysis unit analyzes related meal plans by referring to the activity of the user's friends on social media. This improves the accuracy of the analysis by referring to the user's social media activity. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's social media data into a generation AI, which then performs the analysis.

[0046] During analysis, the analysis unit can customize the analysis method by reflecting the user's past feedback. For example, the analysis unit preferentially applies an analysis method that the user previously preferred (e.g., detailed analysis results). The analysis unit can also improve the analysis algorithm based on the user's past feedback. For example, the analysis unit adjusts the analysis method by referring to content that the user previously input. In this way, the analysis method is optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's past feedback data into the generation AI, which then suggests the optimal analysis method.

[0047] The generation unit can optimize the meal plan based on the user's health condition and lifestyle when generating the meal plan. For example, if the user has a specific health condition (e.g., diabetes), the generation unit generates a meal plan appropriate for that condition. The generation unit can also optimize the meal plan based on the user's lifestyle (e.g., vegetarian). For example, the generation unit adjusts the meal plan based on the user's health checkup results. This provides a more appropriate meal plan by optimizing the meal plan based on the user's health condition and lifestyle. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's health checkup results into the generation AI, which then generates an optimal meal plan.

[0048] The generation unit can improve the accuracy of the meal plan by referring to the user's past selections and feedback when generating the meal plan. The generation unit improves the accuracy of the meal plan, for example, based on ingredients and recipes selected by the user in the past. The generation unit can also optimize the meal plan by reflecting the user's past feedback. For example, the generation unit analyzes the user's past selection history and generates an optimal meal plan. This improves the accuracy of the meal plan by referring to the user's past selections and feedback. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past selection history into the generation AI, which then generates an optimal meal plan.

[0049] During generation, the generation unit can apply different generation algorithms depending on the category of the user's input. For example, if the user inputs allergy information, the generation unit will prioritize generating ingredients that do not contain allergens. Furthermore, if the user inputs intake restrictions for specific nutrients, the generation unit can also adjust the generation algorithm based on those restrictions. For example, if the user inputs favorite ingredients, the generation unit will generate meals centered around those ingredients. This provides a more appropriate meal plan by applying different generation algorithms depending on the category of the user's input. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's input into a generation AI, which then applies the optimal generation algorithm.

[0050] The generation unit can adjust the meal plan taking into account the user's geographical location information when generating the meal plan. For example, if the user is in a specific area, the generation unit can prioritize generating ingredients available in that area. Furthermore, if the user is traveling, the generation unit can also generate local specialties and dishes from the travel destination. For example, if the user is at home, the generation unit generates ingredients that can be purchased at a nearby supermarket. This allows for providing a more relevant meal plan by taking the user's geographical location information into account. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI, which can then suggest optimal ingredients.

[0051] The generation unit can improve the accuracy of the meal plan by referring to the user's social media activity during generation. For example, the generation unit infers preferences and restrictions based on photos of meals shared by the user on social media. The generation unit can also analyze the user's social media posts to generate related ingredients and recipes. For example, the generation unit generates related meal plans by referring to the activity of the user's friends on social media. This improves the accuracy of the meal plan by referring to the user's social media activity. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's social media data into the generation AI, which then performs the analysis.

[0052] The generation unit can customize the meal plan by reflecting the user's past feedback when generating the meal plan. The generation unit customizes the meal plan based on, for example, ingredients and recipes that the user has previously preferred. The generation unit can also optimize the meal plan based on the user's past feedback. For example, the generation unit adjusts the meal plan by referring to content previously input by the user. This reflects the user's past feedback and provides a more appropriate meal plan. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past feedback data into the generation AI, which then proposes an optimal meal plan.

[0053] The providing unit can improve the accuracy of provision by referring to the user's past selections and feedback when providing food. The providing unit improves the accuracy of provision based on, for example, ingredients and recipes selected by the user in the past. The providing unit can also optimize the provision method by reflecting the user's past feedback. For example, the providing unit analyzes the user's past selection history and provides the optimal provision method. This improves the accuracy of provision by referring to the user's past selections and feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's past selection history into the generation AI, which can then suggest the optimal provision method.

[0054] The providing unit can optimize the provided content based on the user's health condition and lifestyle when providing the content. For example, if the user has a specific health condition (e.g., diabetes), the providing unit provides the provided content appropriate for that condition. The providing unit can also optimize the provided content based on the user's lifestyle (e.g., vegetarian). For example, the providing unit adjusts the provided content based on the user's health checkup results. This optimizes the provided content based on the user's health condition and lifestyle, thereby providing a more appropriate meal plan. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's health checkup results into the generation AI, which then suggests the optimal provided content.

[0055] The providing unit can apply different providing methods depending on the category of the user's input content when providing the meal plan. For example, if the user inputs allergy information, the providing unit will prioritize providing ingredients that do not contain allergens. Furthermore, if the user inputs intake restrictions for specific nutrients, the providing unit can adjust the providing method based on those restrictions. For example, if the user inputs a favorite ingredient, the providing unit will provide mainly those ingredients. In this way, a more appropriate meal plan can be provided by applying different providing methods depending on the category of the user's input content. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's input content into a generation AI, which can then suggest the optimal providing method.

[0056] The providing unit can adjust the provided content by taking into account the user's geographical location information. For example, if the user is in a specific area, the providing unit can prioritize providing ingredients available in that area. Furthermore, if the user is traveling, the providing unit can also provide local specialties and dishes from the travel destination. For example, if the user is at home, the providing unit can provide ingredients that can be purchased at a nearby supermarket. In this way, by taking the user's geographical location information into account, a more relevant meal plan can be provided. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's geographical location information into the generation AI, which can then suggest the most suitable ingredients.

[0057] The providing unit can improve the accuracy of the provision by referring to the user's social media activity when providing the data. For example, the providing unit infers preferences and restrictions based on photos of meals shared by the user on social media. The providing unit can also analyze the content of the user's social media posts to provide related ingredients and recipes. For example, the providing unit provides related meal plans by referring to the activity of the user's friends on social media. This improves the accuracy of the provision by referring to the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's social media data into the generation AI, which then performs the analysis.

[0058] The providing unit can customize the providing method by reflecting the user's past feedback when providing information. For example, the providing unit preferentially applies a providing method that the user previously preferred (e.g., detailed explanation). The providing unit can also optimize the providing method based on the user's past feedback. For example, the providing unit adjusts the providing method by referring to content that the user previously input. In this way, a more appropriate providing method is provided by reflecting the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's past feedback data into the generation AI, which then suggests the optimal providing method.

[0059] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit, for example, optimizes the learning algorithm based on past learning data. The learning unit can also improve the learning algorithm by reflecting the user's past selections and feedback. For example, the learning unit analyzes past learning data and applies an optimal learning method. In this way, the learning algorithm is optimized by referring to the past learning data. Some or all of the above-described processing in the learning unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the learning unit can input past learning data into the generation AI, which then proposes an optimal learning algorithm.

[0060] During learning, the learning unit can update the learning data by reflecting the user's past selections and feedback. The learning unit updates the learning data based on, for example, ingredients and recipes selected by the user in the past. The learning unit can also optimize the learning data by reflecting the user's past feedback. For example, the learning unit analyzes the user's past selection history and updates the optimal learning data. In this way, the learning data is updated by reflecting the user's past selections and feedback. Some or all of the above-described processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit can input the user's past selection history into the generation AI, which can then suggest optimal learning data.

[0061] During learning, the learning unit can expand the learning data by taking into account the user's geographical location information. For example, if the user is in a specific area, the learning unit can add ingredients available in that area to the learning data. Furthermore, if the user is traveling, the learning unit can also add local specialties and dishes from the travel destination to the learning data. For example, if the user is at home, the learning unit can add ingredients available at a nearby supermarket to the learning data. In this way, the learning data is expanded by taking into account the user's geographical location information. Some or all of the above-described processing in the learning unit may be performed using, or without, a generation AI. For example, the learning unit can input the user's geographical location information into the generation AI, which can then suggest optimal learning data.

[0062] During learning, the learning unit can improve the accuracy of the learning data by referring to the user's social media activity. For example, the learning unit reflects preferences and constraints in the learning data based on photos of meals shared by the user on social media. The learning unit can also analyze the user's social media posts and add related ingredients and recipes to the learning data. For example, the learning unit reflects related meal plans in the learning data by referring to the activity of the user's friends on social media. This improves the accuracy of the learning data by referring to the user's social media activity. Some or all of the above-described processing in the learning unit may be performed using, or without, a generation AI. For example, the learning unit can input the user's social media data into the generation AI, which then performs the analysis.

[0063] During learning, the learning unit can update the learning data by reflecting the user's past feedback. The learning unit updates the learning data based on, for example, ingredients or recipes that the user previously preferred. The learning unit can also optimize the learning data based on the user's past feedback. For example, the learning unit adjusts the learning data by referring to content previously input by the user. In this way, the learning data is updated by reflecting the user's past feedback. Some or all of the above-described processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit can input the user's past feedback data into the generation AI, which can then suggest optimal learning data.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] The providing unit can prioritize accepting highly relevant input content by taking into account the user's geographical location information. For example, if the user is in a specific area, it can prioritize suggesting ingredients available in that area. Also, if the user is traveling, it can suggest local specialties and local dishes from the travel destination. For example, if the user is at home, it can suggest ingredients that can be purchased at a nearby supermarket. In this way, by taking the user's geographical location information into account, a more relevant meal plan can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's geographical location information into the generation AI, which can then suggest the most suitable ingredients.

[0066] During analysis, the analysis unit can adjust the analysis content taking into account the user's geographical location information. For example, if the user is in a specific area, it can prioritize analysis of ingredients available in that area. Also, if the user is traveling, it can analyze local specialties and local dishes of the travel destination. For example, if the user is at home, it can analyze ingredients available at a nearby supermarket. By taking the user's geographical location information into account, more relevant analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's geographical location information into the generation AI, which can then adjust the analysis content to be optimal.

[0067] The reception unit can analyze the user's past input history and provide an optimal input interface. For example, preferences and constraints that the user frequently input in the past can be automatically displayed as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, preferences and constraints to be used during a specific time period can be predicted and suggested from the user's past input history. In this way, the optimal input interface can be provided by analyzing the user's past input history. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's past input history into a generation AI, which can then suggest an optimal input interface.

[0068] The generation unit can optimize the meal plan based on the user's health condition and lifestyle when generating the meal plan. For example, if the user has a specific health condition (e.g., diabetes), the generation unit generates a meal plan appropriate for that condition. The meal plan can also be optimized based on the user's lifestyle (e.g., vegetarian). For example, the meal plan can be adjusted based on the user's health checkup results. This provides a more appropriate meal plan by optimizing the meal plan based on the user's health condition and lifestyle. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's health checkup results into the generation AI, which then generates an optimal meal plan.

[0069] The providing unit can improve the accuracy of provision by referring to the user's past selections and feedback when providing food. For example, the providing unit can improve the accuracy of provision based on ingredients and recipes selected by the user in the past. The providing method can also be optimized by reflecting the user's past feedback. For example, the user's past selection history is analyzed and the optimal providing method is provided. This improves the accuracy of provision by referring to the user's past selections and feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's past selection history into the generation AI, which can then suggest the optimal serving method.

[0070] The processing flow of the first embodiment will be briefly explained below.

[0071] Step 1: The reception unit receives input of the user's preferences or constraints. The user's preferences or constraints may include allergy information, dietary preferences, calorie restrictions, etc. The reception unit receives information in text format, voice input, image input, or other methods. For example, voice input can be converted into text data using voice recognition technology, and information can be extracted from images using image analysis technology. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis is performed using data analysis methods and algorithms, machine learning algorithms, natural language processing technology, etc. This allows for a detailed analysis of the user's preferences and constraints. Step 3: The generation unit generates a customized meal plan based on the information analyzed by the analysis unit. The generated meal plan is based on nutritional balance, ingredient selection criteria, the user's health condition, and lifestyle. The generation unit can generate the optimal meal plan using generation AI. Step 4: The provision unit provides the meal plan generated by the generation unit, such as through digital distribution, print distribution, a web application, a mobile application, email, or a messaging app. Step 5: The learning unit learns the user's feedback based on the meal plan provided by the provision unit. Learning is performed using a machine learning algorithm, and the learning data is updated based on the user's past selections, feedback, evaluation comments, and selection history to optimize the next suggestions.

[0072] (Example 2) A menu concierge system according to an embodiment of the present invention utilizes generative AI to understand a user's preferences and constraints and propose individually customized meal plans. The menu concierge system accepts user preferences and constraints as input, analyzes the information, and generates an optimal meal plan for the user. For example, if a user inputs "I'm looking for a gluten-free, high-protein diet," the generative AI will suggest appropriate ingredients and recipes based on that request. The menu concierge system also accommodates specific needs and allergies, supporting sustainable dietary choices. The generative AI's advanced natural language processing technology accurately understands the user's input and provides customized meal plans. Furthermore, the generative AI learns from the user's past choices and feedback to provide more accurate suggestions. For example, it optimizes its next suggestions based on the user's past menu choices and their ratings. This allows users to lead a healthy and fulfilling eating lifestyle. The menu concierge system also promotes the selection of sustainable ingredients and proposes environmentally friendly meals. For example, by prioritizing locally produced and seasonal ingredients, it reduces food miles and reduces environmental impact. This allows the menu concierge system to provide customized meal plans based on the user's preferences and constraints, enabling them to achieve a healthy and fulfilling dietary lifestyle.

[0073] A menu concierge system according to an embodiment includes a reception unit, an analysis unit, a generation unit, a provision unit, and a learning unit. The reception unit receives input of a user's preferences or constraints. Examples of the user's preferences or constraints include, but are not limited to, allergy information, dietary preferences, and calorie restrictions. The reception unit receives information entered by the user in text format. The reception unit can also receive information using voice input or image input. For example, the reception unit converts the user's voice input into text data using voice recognition technology. The reception unit can also extract information from images uploaded by the user using image analysis technology. The analysis unit analyzes the information received by the reception unit. The analysis is performed using, for example, but is not limited to, data analysis techniques and algorithms. For example, the analysis unit analyzes the user's preferences and constraints using machine learning algorithms. The analysis unit can also analyze the text data using natural language processing technology. The generation unit generates a customized meal plan based on the information analyzed by the analysis unit. The customized meal plan is generated based on, for example, nutritional balance and ingredient selection criteria, but is not limited to, such examples. For example, the generation unit generates an optimal meal plan for the user using a generation AI. The generation unit can also generate a meal plan tailored to the user's health condition and lifestyle. The provision unit provides the meal plan generated by the generation unit. The provision can be performed, for example, by digital distribution or by providing a printed document, but is not limited to such examples. For example, the provision unit can provide the meal plan through a web application or a mobile application. The provision unit can also provide the meal plan via email or a messaging app. The learning unit learns from the user's feedback based on the meal plan provided by the provision unit. The learning can be performed, for example, by using a machine learning algorithm, but is not limited to such examples. For example, the learning unit can analyze the user's past selections and feedback to optimize the next suggestion. The learning unit can also update the learning data based on the user's evaluation comments and selection history.As a result, the menu concierge system according to the embodiment can provide a meal plan customized based on the user's preferences and constraints, enabling the user to realize a healthy and fulfilling eating lifestyle.

[0074] The provision unit can suggest sustainable ingredients. Examples of sustainable ingredients include, but are not limited to, organically grown, locally produced, and seasonal ingredients. The provision unit, for example, prioritizes the suggestion of sustainable ingredients. The provision unit can also suggest locally produced ingredients and seasonal ingredients. For example, the provision unit can suggest local agricultural products and seasonal vegetables. This supports environmentally conscious meal choices by suggesting sustainable ingredients. Some or all of the above-described processing in the provision unit may be performed, for example, using or without the generation AI. For example, the provision unit can input a database of sustainable ingredients into the generation AI, which can then suggest the most suitable ingredients.

[0075] The analysis unit can analyze the user's past selections and feedback. Examples of past selections and feedback include, but are not limited to, selection history and user evaluation comments. The analysis unit, for example, analyzes the user's past selection history. The analysis unit can also analyze the user's feedback. For example, the analysis unit performs analysis based on the user's past menu selections and their evaluations. The analysis unit can also analyze the user's evaluation comments to optimize the next suggestion. By analyzing the user's past selections and feedback, a more accurate meal plan can be provided. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the user's past selection history into the generation AI, which then performs the analysis.

[0076] The generation unit can generate a meal plan tailored to the user's health condition and lifestyle. Examples of health condition and lifestyle include, but are not limited to, medical history, exercise habits, and daily routines. The generation unit generates a meal plan based on, for example, the user's health checkup results. The generation unit can also generate a meal plan based on the user's exercise habits. For example, the generation unit can adjust the meal plan according to the frequency and intensity of the user's exercise. The generation unit can also generate a meal plan tailored to the user's daily routine. For example, if the user works the night shift, the generation unit can suggest a meal plan suited to that daily routine. This provides a meal plan tailored to the user's health condition and lifestyle, thereby realizing an individually customized meal experience. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's health checkup results into the generation AI, which then generates an optimal meal plan.

[0077] The provision unit can prioritize locally produced ingredients or seasonal ingredients. Locally produced ingredients and seasonal ingredients include, but are not limited to, regional agricultural products and seasonal vegetables. The provision unit, for example, prioritizes locally produced agricultural products. The provision unit can also suggest seasonal ingredients. For example, the provision unit suggests spring vegetables in spring and summer vegetables in summer. This prioritizes the suggestion of locally produced ingredients and seasonal ingredients, thereby reducing food miles and mitigating environmental impact. Some or all of the above-described processing in the provision unit may be performed using, or without, a generation AI. For example, the provision unit can input a database of locally produced ingredients and seasonal ingredients into the generation AI, which can then suggest the most suitable ingredients.

[0078] The learning unit can learn the user's past selections and feedback and optimize the next suggestion. For example, a machine learning algorithm is used for learning, but the learning is not limited to this example. For example, the learning unit learns the user's past selection history. The learning unit can also learn the user's feedback. For example, the learning unit performs learning based on menus the user has previously selected and their ratings. The learning unit can also study the user's rating comments and optimize the next suggestion. In this way, by learning the user's past selections and feedback, the accuracy of the next suggestion is improved. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit can input the user's past selection history into the generation AI, which then performs learning.

[0079] The reception unit can estimate the user's emotions and adjust the input method for preferences and constraints based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. For example, if the user is in a hurry, the reception unit can prioritize voice input and allow the user to quickly input preferences and constraints. This adjusts the input method according to the user's emotions, providing a more appropriate input experience. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI. For example, the reception unit can input the user's facial expression data into the generation AI, which can then estimate the emotion.

[0080] The reception unit can analyze the user's past input history and provide an optimal input interface. For example, the reception unit can automatically display preferences and constraints that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit can predict and suggest preferences and constraints to be used in a specific time period based on the user's past input history. In this way, the optimal input interface is provided by analyzing the user's past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past input history into a generation AI, which can then suggest an optimal input interface.

[0081] The reception unit can filter the input content based on the user's current health condition and lifestyle. For example, if the user inputs the results of a health checkup, the reception unit can suggest appropriate dietary restrictions based on that information. Furthermore, if the user has a specific lifestyle habit (night shift, exercise habit, etc.), the reception unit can also suggest a meal plan that suits that habit. For example, if the user inputs their current physical condition (fatigue, stress, etc.), the reception unit can suggest a meal plan that suits that condition. In this way, by filtering the input content based on the user's health condition and lifestyle, a more appropriate meal plan can be provided. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's health checkup results into the generation AI, which can then suggest appropriate dietary restrictions.

[0082] The reception unit can select the optimal input means depending on the user's input method (voice, text, image, etc.). For example, when the user inputs preferences and constraints by voice, the reception unit analyzes the input content using voice recognition technology. Furthermore, when the user inputs preferences and constraints by text, the reception unit can also analyze the input content using natural language processing technology. For example, when the user uploads an image (e.g., a photo of a meal), the reception unit identifies ingredients and nutrients using image analysis technology. This improves input convenience by selecting the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's voice data to a generation AI, which then performs voice recognition.

[0083] The reception unit can estimate the user's emotions and prioritize the input content based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can prioritize suggesting ingredients with a relaxing effect. Furthermore, if the user is tired, the reception unit can prioritize suggesting ingredients suitable for replenishing energy. For example, if the user is health-conscious, the reception unit can prioritize suggesting nutritionally balanced ingredients. This prioritizes the input content according to the user's emotions, thereby providing a more appropriate meal plan. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the reception unit can input the user's facial expression data into the generation AI, which can then estimate the emotion.

[0084] The reception unit can prioritize receiving highly relevant input content by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize suggesting ingredients available in that area. Furthermore, if the user is traveling, the reception unit can also suggest local specialties and dishes from the travel destination. For example, if the user is at home, the reception unit can suggest ingredients that can be purchased at a nearby supermarket. This provides a more relevant meal plan by taking into account the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information into the generation AI, which can then suggest the most suitable ingredients.

[0085] The reception unit can analyze the user's social media activity and suggest related input content. For example, the reception unit can infer preferences and restrictions based on photos of meals shared by the user on social media. The reception unit can also analyze the user's social media posts and suggest related ingredients and recipes. For example, the reception unit can suggest related meal plans based on the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, more relevant meal plans can be provided. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's social media data into the generation AI, which then performs the analysis.

[0086] The reception unit can customize the input method by reflecting the user's past feedback. For example, the reception unit preferentially suggests input methods (such as voice and text) that the user has previously preferred. The reception unit can also improve the input interface based on the user's past feedback. For example, the reception unit simplifies the input procedure by referring to content that the user has previously input. This provides a more appropriate input method by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past feedback data into the generation AI, which then suggests the optimal input method.

[0087] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results that focus on the main points. For example, if the user is feeling stressed, the analysis unit provides visually easy-to-understand analysis results. This allows for adjusting the way the analysis is presented according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's facial expression data into the generation AI, which can then estimate the emotion.

[0088] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past selections and feedback. The analysis unit improves the accuracy of the analysis, for example, based on ingredients and recipes selected by the user in the past. The analysis unit can also optimize the analysis algorithm by reflecting the user's past feedback. For example, the analysis unit analyzes the user's past selection history and applies the optimal analysis method. This improves the accuracy of the analysis by referring to the user's past selections and feedback. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's past selection history into the generation AI, and the generation AI can perform the analysis.

[0089] During analysis, the analysis unit can optimize the analysis algorithm based on the user's health condition and lifestyle. For example, if the user has a specific health condition (e.g., diabetes), the analysis unit applies an analysis algorithm appropriate for that condition. The analysis unit can also optimize the analysis algorithm based on the user's lifestyle (e.g., vegetarian). For example, the analysis unit adjusts the analysis algorithm based on the user's health checkup results. This optimizes the analysis algorithm based on the user's health condition and lifestyle, thereby providing more appropriate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's health checkup results into the generation AI, which then applies the optimal analysis algorithm.

[0090] During analysis, the analysis unit can apply different analysis methods depending on the category of the user's input content. For example, if the user inputs allergy information, the analysis unit will prioritize analysis of ingredients that do not contain allergens. Furthermore, if the user inputs intake restrictions for specific nutrients, the analysis unit can also adjust the analysis method based on those restrictions. For example, if the user inputs a favorite ingredient, the analysis unit will focus on that ingredient in the analysis. This provides more appropriate analysis results by applying different analysis methods depending on the category of the user's input content. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's input content into a generation AI, which then applies the optimal analysis method.

[0091] The analysis unit can estimate the user's emotions and determine analysis priorities based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can prioritize analyzing ingredients that have a relaxing effect. Furthermore, if the user is tired, the analysis unit can prioritize analyzing ingredients that are suitable for replenishing energy. For example, if the user is health-conscious, the analysis unit can prioritize analyzing ingredients that are nutritionally balanced. This allows for more appropriate analysis results to be provided by determining analysis priorities according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's facial expression data into the generation AI, which can then estimate the emotion.

[0092] During analysis, the analysis unit can adjust the analysis content taking into account the user's geographical location information. For example, if the user is in a specific area, the analysis unit prioritizes analysis of ingredients available in that area. Furthermore, if the user is traveling, the analysis unit can also analyze local specialties and local cuisine of the travel destination. For example, if the user is at home, the analysis unit analyzes ingredients available at a nearby supermarket. This provides more relevant analysis results by taking the user's geographical location information into account. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's geographical location information into the generation AI, which can then adjust the analysis content to be optimal.

[0093] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's social media activity. For example, the analysis unit infers preferences and restrictions based on photos of meals shared by the user on social media. The analysis unit can also analyze the content of the user's social media posts to analyze related ingredients and recipes. For example, the analysis unit analyzes related meal plans by referring to the activity of the user's friends on social media. This improves the accuracy of the analysis by referring to the user's social media activity. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's social media data into a generation AI, which then performs the analysis.

[0094] During analysis, the analysis unit can customize the analysis method by reflecting the user's past feedback. For example, the analysis unit preferentially applies an analysis method that the user previously preferred (e.g., detailed analysis results). The analysis unit can also improve the analysis algorithm based on the user's past feedback. For example, the analysis unit adjusts the analysis method by referring to content that the user previously input. In this way, the analysis method is optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's past feedback data into the generation AI, which then suggests the optimal analysis method.

[0095] The generation unit can estimate the user's emotions and adjust the meal plan generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates a meal plan that proceeds at a leisurely pace. Furthermore, if the user is in a hurry, the generation unit can generate a concise and quick-to-prepare meal plan. For example, if the user is excited, the generation unit generates a visually stimulating meal plan. This adjusts the meal plan generation method according to the user's emotions, thereby providing a more appropriate meal plan. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the user's facial expression data into the generation AI, which can then estimate the emotion.

[0096] The generation unit can optimize the meal plan based on the user's health condition and lifestyle when generating the meal plan. For example, if the user has a specific health condition (e.g., diabetes), the generation unit generates a meal plan appropriate for that condition. The generation unit can also optimize the meal plan based on the user's lifestyle (e.g., vegetarian). For example, the generation unit adjusts the meal plan based on the user's health checkup results. This provides a more appropriate meal plan by optimizing the meal plan based on the user's health condition and lifestyle. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's health checkup results into the generation AI, which then generates an optimal meal plan.

[0097] The generation unit can improve the accuracy of the meal plan by referring to the user's past selections and feedback when generating the meal plan. The generation unit improves the accuracy of the meal plan, for example, based on ingredients and recipes selected by the user in the past. The generation unit can also optimize the meal plan by reflecting the user's past feedback. For example, the generation unit analyzes the user's past selection history and generates an optimal meal plan. This improves the accuracy of the meal plan by referring to the user's past selections and feedback. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past selection history into the generation AI, which then generates an optimal meal plan.

[0098] During generation, the generation unit can apply different generation algorithms depending on the category of the user's input. For example, if the user inputs allergy information, the generation unit will prioritize generating ingredients that do not contain allergens. Furthermore, if the user inputs intake restrictions for specific nutrients, the generation unit can also adjust the generation algorithm based on those restrictions. For example, if the user inputs favorite ingredients, the generation unit will generate meals centered around those ingredients. This provides a more appropriate meal plan by applying different generation algorithms depending on the category of the user's input. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's input into a generation AI, which then applies the optimal generation algorithm.

[0099] The generation unit can estimate the user's emotions and prioritize meal plans based on the estimated user emotions. For example, if the user is stressed, the generation unit can prioritize ingredients that have a relaxing effect. Furthermore, if the user is tired, the generation unit can prioritize ingredients that are suitable for replenishing energy. For example, if the user is health-conscious, the generation unit can prioritize nutritionally balanced ingredients. This allows for prioritizing meal plans according to the user's emotions, thereby providing a more appropriate meal plan. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the user's facial expression data into the generation AI, which can then estimate the emotion.

[0100] The generation unit can adjust the meal plan taking into account the user's geographical location information when generating the meal plan. For example, if the user is in a specific area, the generation unit can prioritize generating ingredients available in that area. Furthermore, if the user is traveling, the generation unit can also generate local specialties and dishes from the travel destination. For example, if the user is at home, the generation unit generates ingredients that can be purchased at a nearby supermarket. This allows for providing a more relevant meal plan by taking the user's geographical location information into account. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI, which can then suggest optimal ingredients.

[0101] The generation unit can improve the accuracy of the meal plan by referring to the user's social media activity during generation. For example, the generation unit infers preferences and restrictions based on photos of meals shared by the user on social media. The generation unit can also analyze the user's social media posts to generate related ingredients and recipes. For example, the generation unit generates related meal plans by referring to the activity of the user's friends on social media. This improves the accuracy of the meal plan by referring to the user's social media activity. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's social media data into the generation AI, which then performs the analysis.

[0102] The generation unit can customize the meal plan by reflecting the user's past feedback when generating the meal plan. The generation unit customizes the meal plan based on, for example, ingredients and recipes that the user has previously preferred. The generation unit can also optimize the meal plan based on the user's past feedback. For example, the generation unit adjusts the meal plan by referring to content previously input by the user. This reflects the user's past feedback and provides a more appropriate meal plan. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past feedback data into the generation AI, which then proposes an optimal meal plan.

[0103] The providing unit can estimate the user's emotions and adjust the meal plan presentation method based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide a presentation method that includes detailed explanations. Furthermore, if the user is in a hurry, the providing unit can also provide a concise and quick presentation method. For example, if the user is feeling stressed, the providing unit can provide a visually easy-to-understand presentation method. This allows the provision of a more appropriate meal plan by adjusting the presentation method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the user's facial expression data into the generation AI, which can then estimate the emotion.

[0104] The providing unit can improve the accuracy of provision by referring to the user's past selections and feedback when providing food. The providing unit improves the accuracy of provision based on, for example, ingredients and recipes selected by the user in the past. The providing unit can also optimize the provision method by reflecting the user's past feedback. For example, the providing unit analyzes the user's past selection history and provides the optimal provision method. This improves the accuracy of provision by referring to the user's past selections and feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's past selection history into the generation AI, which can then suggest the optimal provision method.

[0105] The providing unit can optimize the provided content based on the user's health condition and lifestyle when providing the content. For example, if the user has a specific health condition (e.g., diabetes), the providing unit provides the provided content appropriate for that condition. The providing unit can also optimize the provided content based on the user's lifestyle (e.g., vegetarian). For example, the providing unit adjusts the provided content based on the user's health checkup results. This optimizes the provided content based on the user's health condition and lifestyle, thereby providing a more appropriate meal plan. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's health checkup results into the generation AI, which then suggests the optimal provided content.

[0106] The providing unit can apply different providing methods depending on the category of the user's input content when providing the meal plan. For example, if the user inputs allergy information, the providing unit will prioritize providing ingredients that do not contain allergens. Furthermore, if the user inputs intake restrictions for specific nutrients, the providing unit can adjust the providing method based on those restrictions. For example, if the user inputs a favorite ingredient, the providing unit will provide mainly those ingredients. In this way, a more appropriate meal plan can be provided by applying different providing methods depending on the category of the user's input content. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's input content into a generation AI, which can then suggest the optimal providing method.

[0107] The providing unit can estimate the user's emotions and prioritize the content to be provided based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can prioritize providing ingredients with a relaxing effect. Furthermore, if the user is tired, the providing unit can prioritize providing ingredients suitable for replenishing energy. For example, if the user is health-conscious, the providing unit can prioritize nutritionally balanced ingredients. This allows the prioritization of the content to be provided according to the user's emotions, thereby providing a more appropriate meal plan. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the user's facial expression data into the generation AI, which can then estimate the emotion.

[0108] The providing unit can adjust the provided content by taking into account the user's geographical location information. For example, if the user is in a specific area, the providing unit can prioritize providing ingredients available in that area. Furthermore, if the user is traveling, the providing unit can also provide local specialties and dishes from the travel destination. For example, if the user is at home, the providing unit can provide ingredients that can be purchased at a nearby supermarket. In this way, by taking the user's geographical location information into account, a more relevant meal plan can be provided. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's geographical location information into the generation AI, which can then suggest the most suitable ingredients.

[0109] The providing unit can improve the accuracy of the provision by referring to the user's social media activity when providing the data. For example, the providing unit infers preferences and restrictions based on photos of meals shared by the user on social media. The providing unit can also analyze the content of the user's social media posts to provide related ingredients and recipes. For example, the providing unit provides related meal plans by referring to the activity of the user's friends on social media. This improves the accuracy of the provision by referring to the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's social media data into the generation AI, which then performs the analysis.

[0110] The providing unit can customize the providing method by reflecting the user's past feedback when providing information. For example, the providing unit preferentially applies a providing method that the user previously preferred (e.g., detailed explanation). The providing unit can also optimize the providing method based on the user's past feedback. For example, the providing unit adjusts the providing method by referring to content that the user previously input. In this way, a more appropriate providing method is provided by reflecting the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's past feedback data into the generation AI, which then suggests the optimal providing method.

[0111] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is relaxed, the learning unit selects detailed training data. Furthermore, if the user is in a hurry, the learning unit can select concise training data that focuses on the main points. For example, if the user is stressed, the learning unit selects visually easy-to-understand training data. This allows for more appropriate learning by selecting training data according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the learning unit can be performed using, for example, the generation AI, or without the generation AI. For example, the learning unit can input the user's facial expression data into the generation AI, which can then estimate the emotion.

[0112] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit, for example, optimizes the learning algorithm based on past learning data. The learning unit can also improve the learning algorithm by reflecting the user's past selections and feedback. For example, the learning unit analyzes past learning data and applies an optimal learning method. In this way, the learning algorithm is optimized by referring to the past learning data. Some or all of the above-described processing in the learning unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the learning unit can input past learning data into the generation AI, which then proposes an optimal learning algorithm.

[0113] During learning, the learning unit can update the learning data by reflecting the user's past selections and feedback. The learning unit updates the learning data based on, for example, ingredients and recipes selected by the user in the past. The learning unit can also optimize the learning data by reflecting the user's past feedback. For example, the learning unit analyzes the user's past selection history and updates the optimal learning data. In this way, the learning data is updated by reflecting the user's past selections and feedback. Some or all of the above-described processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit can input the user's past selection history into the generation AI, which can then suggest optimal learning data.

[0114] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user emotions. For example, when the user is relaxed, the learning unit frequently performs detailed learning. Furthermore, when the user is in a hurry, the learning unit can frequently perform concise learning. For example, when the user is stressed, the learning unit frequently performs visually easy-to-understand learning. This allows for more appropriate learning by adjusting the frequency of learning according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the learning unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the learning unit can input the user's facial expression data into the generation AI, which can then estimate the emotion.

[0115] During learning, the learning unit can expand the learning data by taking into account the user's geographical location information. For example, if the user is in a specific area, the learning unit can add ingredients available in that area to the learning data. Furthermore, if the user is traveling, the learning unit can also add local specialties and dishes from the travel destination to the learning data. For example, if the user is at home, the learning unit can add ingredients available at a nearby supermarket to the learning data. In this way, the learning data is expanded by taking into account the user's geographical location information. Some or all of the above-described processing in the learning unit may be performed using, or without, a generation AI. For example, the learning unit can input the user's geographical location information into the generation AI, which can then suggest optimal learning data.

[0116] During learning, the learning unit can improve the accuracy of the learning data by referring to the user's social media activity. For example, the learning unit reflects preferences and constraints in the learning data based on photos of meals shared by the user on social media. The learning unit can also analyze the user's social media posts and add related ingredients and recipes to the learning data. For example, the learning unit reflects related meal plans in the learning data by referring to the activity of the user's friends on social media. This improves the accuracy of the learning data by referring to the user's social media activity. Some or all of the above-described processing in the learning unit may be performed using, or without, a generation AI. For example, the learning unit can input the user's social media data into the generation AI, which then performs the analysis.

[0117] During learning, the learning unit can update the learning data by reflecting the user's past feedback. The learning unit updates the learning data based on, for example, ingredients or recipes that the user previously preferred. The learning unit can also optimize the learning data based on the user's past feedback. For example, the learning unit adjusts the learning data by referring to content previously input by the user. In this way, the learning data is updated by reflecting the user's past feedback. Some or all of the above-described processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit can input the user's past feedback data into the generation AI, which can then suggest optimal learning data. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, generation unit, provision unit, and learning unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives user preferences and constraints as input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a customized meal plan based on the analyzed information. The provision unit is realized, for example, by the output device 40 of the smart device 14 and provides the generated meal plan to the user. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and learns user feedback and optimizes future suggestions. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, provision unit, and learning unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives user preferences and constraints as input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a customized meal plan based on the analyzed information. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides the generated meal plan to the user. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and learns user feedback and optimizes future suggestions. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, provision unit, and learning unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives user preferences and constraints as input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a customized meal plan based on the analyzed information. The provision unit is realized, for example, by the speaker 240 of the headset-type terminal 314 and provides the generated meal plan to the user. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and learns user feedback and optimizes future suggestions. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, provision unit, and learning unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives the user's preferences and constraints as input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a customized meal plan based on the analyzed information. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides the generated meal plan to the user. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and learns the user's feedback and optimizes the next suggestion.

[0118] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0119] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user's emotions. For example, if the user is feeling stressed, it can prioritize the analysis of ingredients with a relaxing effect. Also, if the user is tired, it can prioritize the analysis of ingredients suitable for replenishing energy. For example, if the user is health-conscious, it can prioritize the analysis of ingredients with a balanced nutritional profile. This provides more appropriate analysis results by determining the analysis priority according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's facial expression data into the generation AI, which can then estimate the emotion.

[0120] The providing unit can prioritize accepting highly relevant input content by taking into account the user's geographical location information. For example, if the user is in a specific area, it can prioritize suggesting ingredients available in that area. Also, if the user is traveling, it can suggest local specialties and local dishes from the travel destination. For example, if the user is at home, it can suggest ingredients that can be purchased at a nearby supermarket. In this way, by taking the user's geographical location information into account, a more relevant meal plan can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's geographical location information into the generation AI, which can then suggest the most suitable ingredients.

[0121] The reception unit can estimate the user's emotions and adjust the input method for preferences and constraints based on the estimated user emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize input steps. Alternatively, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. For example, if the user is in a hurry, voice input can be prioritized to allow the user to quickly input preferences and constraints. This provides a more appropriate input experience by adjusting the input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the reception unit can input the user's facial expression data into the generation AI, which can then estimate the emotion.

[0122] The generation unit can estimate the user's emotions and adjust the meal plan generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a meal plan that proceeds at a leisurely pace. Also, if the user is in a hurry, the generation unit can generate a concise and quick-to-prepare meal plan. For example, if the user is excited, the generation unit can generate a visually stimulating meal plan. This allows the generation unit to adjust the meal plan generation method according to the user's emotions, thereby providing a more appropriate meal plan. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the user's facial expression data into the generation AI, which can then estimate the emotion.

[0123] The providing unit can estimate the user's emotions and adjust the meal plan presentation method based on the estimated user's emotions. For example, if the user is relaxed, a detailed explanation can be provided. Also, if the user is in a hurry, a concise and quick presentation method can be provided. For example, if the user is stressed, a visually easy-to-understand presentation method can be provided. This allows the presentation method to be adjusted according to the user's emotions, thereby providing a more appropriate meal plan. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the user's facial expression data into the generation AI, which can then estimate the emotion.

[0124] During analysis, the analysis unit can adjust the analysis content taking into account the user's geographical location information. For example, if the user is in a specific area, it can prioritize analysis of ingredients available in that area. Also, if the user is traveling, it can analyze local specialties and local dishes of the travel destination. For example, if the user is at home, it can analyze ingredients available at a nearby supermarket. By taking the user's geographical location information into account, more relevant analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's geographical location information into the generation AI, which can then adjust the analysis content to be optimal.

[0125] The reception unit can analyze the user's past input history and provide an optimal input interface. For example, preferences and constraints that the user frequently input in the past can be automatically displayed as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, preferences and constraints to be used during a specific time period can be predicted and suggested from the user's past input history. In this way, the optimal input interface can be provided by analyzing the user's past input history. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's past input history into a generation AI, which can then suggest an optimal input interface.

[0126] The generation unit can optimize the meal plan based on the user's health condition and lifestyle when generating the meal plan. For example, if the user has a specific health condition (e.g., diabetes), the generation unit generates a meal plan appropriate for that condition. The meal plan can also be optimized based on the user's lifestyle (e.g., vegetarian). For example, the meal plan can be adjusted based on the user's health checkup results. This provides a more appropriate meal plan by optimizing the meal plan based on the user's health condition and lifestyle. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's health checkup results into the generation AI, which then generates an optimal meal plan.

[0127] The providing unit can improve the accuracy of provision by referring to the user's past selections and feedback when providing food. For example, the providing unit can improve the accuracy of provision based on ingredients and recipes selected by the user in the past. The providing method can also be optimized by reflecting the user's past feedback. For example, the user's past selection history is analyzed and the optimal providing method is provided. This improves the accuracy of provision by referring to the user's past selections and feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's past selection history into the generation AI, which can then suggest the optimal serving method.

[0128] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is relaxed, detailed training data can be selected. Also, if the user is in a hurry, concise training data that focuses on the main points can be selected. For example, if the user is stressed, visually easy-to-understand training data can be selected. This allows for more appropriate learning by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the learning unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the learning unit can input the user's facial expression data into the generation AI, which can then estimate the emotion.

[0129] The processing flow of the second embodiment will be briefly explained below.

[0130] Step 1: The reception unit receives input of the user's preferences or constraints. The user's preferences or constraints may include allergy information, dietary preferences, calorie restrictions, etc. The reception unit receives information in text format, voice input, image input, or other methods. For example, voice input can be converted into text data using voice recognition technology, and information can be extracted from images using image analysis technology. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis is performed using data analysis methods and algorithms, machine learning algorithms, natural language processing technology, etc. This allows for a detailed analysis of the user's preferences and constraints. Step 3: The generation unit generates a customized meal plan based on the information analyzed by the analysis unit. The generated meal plan is based on nutritional balance, ingredient selection criteria, the user's health condition, and lifestyle. The generation unit can generate the optimal meal plan using generation AI. Step 4: The provision unit provides the meal plan generated by the generation unit, such as through digital distribution, print distribution, a web application, a mobile application, email, or a messaging app. Step 5: The learning unit learns the user's feedback based on the meal plan provided by the provision unit. Learning is performed using a machine learning algorithm, and the learning data is updated based on the user's past selections, feedback, evaluation comments, and selection history to optimize the next suggestions.

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0136] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0142] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0143] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0145] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0147] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0152] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0153] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0154] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0155] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0157] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0158] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0159] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0160] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0161] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0162] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0163] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0165] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0168] 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.

[0169] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0170] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0171] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0172] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0173] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0174] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0175] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0176] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0177] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0178] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0179] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0180] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0181] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0182] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0183] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0184] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0185] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0186] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0187] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0188] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0189] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0190] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0191] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0192] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0193] 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.

[0194] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0195] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0196] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0197] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0198] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0199] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0200] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0201] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0202] [Explanation of symbols]

[0203] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a receiving unit for receiving input of user preferences or constraints; an analysis unit that analyzes the information received by the reception unit; a generation unit that generates a customized meal plan based on the information analyzed by the analysis unit; a providing unit that provides the meal plan generated by the generating unit; a learning unit that learns user feedback based on the meal plan provided by the providing unit. A system characterized by:

2. The providing unit Propose sustainable ingredients 2. The system of claim 1.

3. The analysis unit Analyzing users' past choices and feedback 2. The system of claim 1.

4. The generation unit Generate meal plans tailored to the user's health and lifestyle 2. The system of claim 1.

5. The providing unit Prioritize locally produced or seasonal ingredients 2. The system of claim 1.

6. The learning unit Learn from users' past choices and feedback to optimize next suggestions 2. The system of claim 1.

7. The reception unit Inferring user emotions and adjusting the input method of preferences and constraints based on the estimated user emotions 2. The system of claim 1.

8. The reception unit Analyzes the user's input history and provides the optimal input interface 2. The system of claim 1.

Images