system

Abstract

The system according to the embodiment aims to propose the most suitable menu taking into consideration the food allergies, likes and dislikes of family members, and the inventory in the refrigerator. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, and a suggestion unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The suggestion unit suggests an appropriate menu based on the information analyzed by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately consider family members' food allergies, likes and dislikes, refrigerator inventory, etc. to suggest optimal menus, and there is room for improvement.

[0005] The system according to the embodiment aims to propose the most suitable menu taking into consideration the food allergies, likes and dislikes of family members, and the inventory in the refrigerator. [Means for solving the problem]

[0006] A system according to an embodiment includes a collection unit, an analysis unit, and a suggestion unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The suggestion unit suggests an appropriate menu based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose the most suitable menu taking into consideration the family's food allergies, likes and dislikes, and the inventory in the refrigerator. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A menu suggestion system according to an embodiment of the present invention uses AI to analyze family members' food allergies, likes and dislikes, the ingredients in the refrigerator, and their mood for the day, and then suggests optimal menus. The menu suggestion system allows users to register their family members' food allergies and likes and dislikes in advance, input information about the ingredients in the refrigerator, and input their mood for the day. The AI ​​analyzes this information and suggests optimal menus. For example, if a family member has a specific allergy, the system suggests a menu that does not include that allergy. If a specific ingredient is in the refrigerator, the system suggests dishes that use that ingredient. Furthermore, if a user inputs, "I want something light today," the system suggests light dishes. This allows the whole family to enjoy meals without worry. It also allows the system to use ingredients in the refrigerator efficiently, thereby reducing food waste. Furthermore, by suggesting menus tailored to the user's mood, the system improves meal satisfaction.

[0029] A menu suggestion system according to an embodiment includes a collection unit, an analysis unit, and a suggestion unit. The collection unit collects information. Examples of the information include, but are not limited to, family members' food allergies, likes and dislikes, ingredients in the refrigerator, and their mood for the day. For example, the collection unit allows a user to register their family members' food allergies and likes and dislikes in advance. The collection unit can also input information about ingredients in the refrigerator. Furthermore, the collection unit can also input the user's mood for the day. The analysis unit analyzes the information collected by the collection unit. The analysis can be performed using, but is not limited to, data mining techniques, statistical analysis, machine learning algorithms, or other methods. For example, the analysis unit analyzes the information to suggest an optimal menu that makes the most of the ingredients in the refrigerator while taking into account the family members' food allergies and likes and dislikes and that matches their mood for the day. The suggestion unit suggests an appropriate menu based on the information analyzed by the analysis unit. The suggestion can be performed based on, but is not limited to, criteria such as nutritional balance, calorie restriction, and allergy-friendly options. For example, if a member has a specific allergy, the suggestion unit can suggest a menu that does not include that allergy. Furthermore, if a user has specific ingredients in the refrigerator, the suggestion unit can suggest dishes that use those ingredients. Furthermore, if the user inputs, "I want to eat something light today," the suggestion unit can suggest light dishes. In this way, the menu suggestion system according to the embodiment can collect and analyze information and suggest optimal menus.

[0030] The menu suggestion system includes an allergy information collecting unit. The allergy information collecting unit allows, for example, a user to register food allergies of family members in advance. The allergy information includes, but is not limited to, food allergies, drug allergies, and environmental allergies. For example, the allergy information collecting unit allows a user to input in detail the type of allergy and the intensity of the reaction. This allows the allergy information to be collected.

[0031] The menu suggestion system includes a unit for collecting ingredient information. The unit for collecting ingredient information allows, for example, a user to input information about ingredients in a refrigerator. The ingredient information includes, for example, nutritional components, origin, and storage method of ingredients, but is not limited to these examples. For example, the unit for collecting ingredient information allows a user to input specific ingredients. This allows the ingredient information to be collected.

[0032] The menu suggestion system includes a mood information collecting unit. The mood information collecting unit allows, for example, a user to input today's mood. Mood information includes, but is not limited to, the user's emotional state, stress level, mood fluctuations, etc. For example, the mood information collecting unit allows the user to input information such as "I want to eat something light today" or "I want to eat something substantial today." This allows mood information to be collected.

[0033] The menu suggestion system includes an allergy information analyzing unit. The allergy information analyzing unit can, for example, analyze the collected allergy information. The analysis of the allergy information includes, but is not limited to, identifying allergens and the intensity of allergic reactions. For example, the allergy information analyzing unit can identify allergens and evaluate the intensity of allergic reactions based on the collected allergy information. This allows the allergy information to be analyzed.

[0034] The menu suggestion system includes a unit that analyzes ingredient information. The unit that analyzes ingredient information can, for example, analyze collected ingredient information. Analysis of ingredient information includes, but is not limited to, calculation of nutritional value and the effect of ingredient combinations. For example, the unit that analyzes ingredient information can calculate nutritional value and evaluate the effect of ingredient combinations based on the collected ingredient information. This allows the ingredient information to be analyzed.

[0035] The menu suggestion system includes a mood information analyzing unit. The mood information analyzing unit can, for example, analyze the collected mood information. The analysis of mood information includes, for example, emotion analysis, stress level evaluation, and the like, but is not limited to these examples. For example, the mood information analyzing unit can perform emotion analysis based on the collected mood information and evaluate the stress level. This allows the mood information to be analyzed.

[0036] The menu suggestion system includes a unit for suggesting allergy-friendly menus. The unit for suggesting allergy-friendly menus can suggest allergy-friendly menus, for example, based on collected allergy information. Suggestions for allergy-friendly menus include, but are not limited to, removing allergens and suggesting alternative ingredients. For example, the unit for suggesting allergy-friendly menus can suggest menus that do not contain allergens and suggest alternative ingredients. This allows allergy-friendly menus to be suggested.

[0037] The menu suggestion system includes a unit that suggests menus that utilize ingredients. The unit that suggests menus that utilize ingredients can suggest menus that utilize ingredients based on, for example, collected ingredient information. Proposing menus that utilize ingredients includes, but is not limited to, using seasonal ingredients and taking nutritional balance into consideration. For example, the unit that suggests menus that utilize ingredients can suggest menus that use seasonal ingredients and take nutritional balance into consideration. This makes it possible to suggest menus that utilize ingredients.

[0038] The menu suggestion system includes a unit for suggesting a menu that matches the mood. The unit for suggesting a menu that matches the mood can suggest a menu that matches the mood, for example, based on collected mood information. Suggestions for a menu that matches the mood include, but are not limited to, ingredients that have a relaxing effect and ingredients that are suitable for replenishing energy. For example, the unit for suggesting a menu that matches the mood can suggest a menu that uses ingredients that have a relaxing effect or ingredients that are suitable for replenishing energy. This makes it possible to suggest a menu that matches the mood.

[0039] In the menu suggestion system, the collection unit analyzes the user's past information collection history and selects an appropriate collection method. The collection unit can, for example, analyze the user's past information collection history and select an appropriate collection method. The information collection history includes, for example, past collection data, collection frequency, and the effectiveness of the collection method, but is not limited to these examples. For example, the collection unit prioritizes collection methods that the user has frequently used in the past. The collection unit can also refer to the collection timing that the user preferred in the past. The collection unit can also suggest the optimal collection means based on the user's past collection history. In this way, the user's past information collection history can be analyzed and an appropriate collection method can be selected.

[0040] In the menu suggestion system, when the collection unit collects information, the collection unit filters the information based on the user's current living situation and areas of interest. For example, when collecting information, the collection unit can filter the information based on the user's current living situation and areas of interest. Examples of living situations include, but are not limited to, lifestyle habits, occupation, and home environment. Examples of areas of interest include, but are not limited to, hobbies, topics of interest, and past search history. For example, if the user is health-conscious, the collection unit can prioritize collecting health-related information. Furthermore, if the user is on a diet, the collection unit can collect information about low-calorie ingredients. Furthermore, if the user is interested in a particular ingredient, the collection unit can collect information about that ingredient. This makes it possible to filter information based on the user's current living situation and areas of interest.

[0041] In the menu suggestion system, when the collection unit collects information, it selects an appropriate collection means according to the user's input method. For example, when collecting information, the collection unit can select an appropriate collection means according to the user's input method (voice, text, image, etc.). Input methods include, but are not limited to, voice input, text input, image input, etc. For example, when the user uses voice input, the collection unit can collect information using voice recognition technology. Furthermore, when the user uses text input, the collection unit can also collect information using text analysis technology. Furthermore, when the user uses image input, the collection unit can also collect information using image recognition technology. In this way, it is possible to select an appropriate collection means according to the user's input method.

[0042] In the menu suggestion system, when collecting information, the collection unit prioritizes collecting highly relevant information by taking into consideration the user's geographical location information. For example, when collecting information, the collection unit can prioritize collecting highly relevant information by taking into consideration the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, location information services, etc. For example, if the user lives in a specific area, the collection unit can collect information on ingredients available in that area. Furthermore, if the user is traveling, the collection unit can also collect information on ingredients available at the travel destination. Furthermore, if the user is interested in cuisine from a specific area, the collection unit can also collect recipe information for that area. In this way, highly relevant information can be prioritized by taking into consideration the user's geographical location information.

[0043] In the menu suggestion system, when the collection unit collects information, the collection unit analyzes the user's social media activity and collects related information. For example, when collecting information, the collection unit can analyze the user's social media activity and collect related information. Social media activity includes, but is not limited to, the content of posts, the number of likes, the number of followers, etc. For example, the collection unit collects recipe information shared by the user on social media. The collection unit can also analyze the content of the user's social media posts and collect related ingredient information. The collection unit can also refer to the activities of the user's friends on social media to collect related recipe information. In this way, the user's social media activity can be analyzed and related information can be collected.

[0044] In the menu suggestion system, when the collection unit collects information, the collection method is customized by reflecting the user's past feedback. For example, the collection unit can customize the collection method by reflecting the user's past feedback when collecting information. Feedback includes, but is not limited to, survey results, reviews, comments, etc. For example, the collection unit prioritizes the collection method that the user has previously preferred. The collection unit can also select the optimal collection timing based on the user's past feedback. The collection unit can also adjust the type of information to be collected based on the user's past feedback. This allows the collection method to be customized by reflecting the user's past feedback.

[0045] In the menu suggestion system, the analysis unit adjusts the level of detail of the analysis based on the importance of the information during analysis. The analysis unit can, for example, adjust the level of detail of the analysis based on the importance of the information during analysis. The importance of the information includes, but is not limited to, the urgency, impact, and relevance of the information. For example, the analysis unit performs a detailed analysis of important allergy information. The analysis unit can also perform a concise analysis of general ingredient information. The analysis unit can also perform an appropriate level of detail analysis of the user's mood information. This makes it possible to adjust the level of detail of the analysis based on the importance of the information.

[0046] In the menu suggestion system, the analysis unit applies different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply different analysis algorithms depending on the category of information during analysis. Information categories include, but are not limited to, health information, ingredient information, and allergy information. For example, the analysis unit can apply an allergen detection algorithm to allergy information. The analysis unit can also apply a nutritional value analysis algorithm to ingredient information. The analysis unit can also apply an emotion analysis algorithm to mood information. This makes it possible to apply different analysis algorithms depending on the category of information.

[0047] In the menu suggestion system, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results during analysis. The analysis unit can, for example, improve the accuracy of the analysis by referring to the user's past analysis results during analysis. Past analysis results include, but are not limited to, past analysis data, evaluations of analysis results, and areas for improvement. For example, the analysis unit refers to the user's past analysis results of allergy information. The analysis unit can also refer to the user's past analysis results of ingredient information. The analysis unit can also refer to the user's past analysis results of mood information. This makes it possible to improve the accuracy of the analysis by referring to the user's past analysis results.

[0048] In the menu suggestion system, the analysis unit determines the analysis priority based on the time of information submission during analysis. The analysis unit can, for example, determine the analysis priority based on the time of information submission during analysis. The time of information submission includes, but is not limited to, the submission date and time, the submission frequency, and the submission timing. For example, the analysis unit prioritizes analysis of allergy information with high urgency. The analysis unit can also prioritize analysis of ingredient information with an approaching deadline. The analysis unit can also analyze the user's mood information in real time. This allows the analysis priority to be determined based on the time of information submission.

[0049] In the menu suggestion system, the analysis unit adjusts the analysis order based on the relevance of information during analysis. The analysis unit can, for example, adjust the analysis order based on the relevance of information during analysis. The relevance of information includes, but is not limited to, interrelationships, commonalities, and degrees of association of information. For example, the analysis unit determines the analysis order taking into account the relevance of allergy information and ingredient information. The analysis unit can also determine the analysis order taking into account the relevance of ingredient information and mood information. The analysis unit can also determine the analysis order taking into account the relevance of allergy information and mood information. This makes it possible to adjust the analysis order based on the relevance of information.

[0050] In the menu suggestion system, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. Examples of the level of expertise include, but are not limited to, the level of understanding of technical terms, past learning history, and work experience. For example, the analysis unit uses a lot of technical terms when the user has specialized knowledge. Furthermore, the analysis unit can use simple terms when the user only has general knowledge. Furthermore, the analysis unit can avoid using technical terms when the user is a beginner. This allows the use of technical terms in the analysis to be adjusted according to the user's level of expertise.

[0051] The menu suggestion system adjusts the level of detail of the suggestion based on the importance of the menu when the suggestion unit makes a suggestion. For example, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the menu when making a suggestion. Examples of the importance of a menu include, but are not limited to, nutritional value, calories, and allergy-friendly options. For example, the suggestion unit makes detailed suggestions for menus based on important allergy information. The suggestion unit can also make concise suggestions for menus based on general ingredient information. The suggestion unit can also make suggestions with an appropriate level of detail for menus based on the user's mood information. This makes it possible to adjust the level of detail of the suggestion based on the importance of the menu.

[0052] The menu suggestion system applies different suggestion algorithms depending on the category of the menu when the suggestion unit suggests the menu. For example, the suggestion unit can apply different suggestion algorithms depending on the category of the menu when suggesting the menu. Menu categories include, but are not limited to, breakfast, lunch, dinner, and dessert. For example, the suggestion unit can apply an allergen removal algorithm to a menu based on allergy information. The suggestion unit can also apply a nutritional value optimization algorithm to a menu based on ingredient information. The suggestion unit can also apply a sentiment analysis algorithm to a menu based on mood information. This makes it possible to apply different suggestion algorithms depending on the category of the menu.

[0053] In the menu suggestion system, when making a suggestion, the suggestion unit refers to the user's past suggestion results to improve the accuracy of the suggestion. For example, when making a suggestion, the suggestion unit can refer to the user's past suggestion results to improve the accuracy of the suggestion. Past suggestion results include, but are not limited to, past suggestion data, evaluations of suggestion results, and areas for improvement. For example, the suggestion unit refers to suggestion results based on the user's past allergy information. The suggestion unit can also refer to suggestion results based on the user's past ingredient information. The suggestion unit can also refer to suggestion results based on the user's past mood information. This allows the accuracy of the suggestion to be improved by referring to the user's past suggestion results.

[0054] The menu suggestion system determines the priority of suggestions based on the time of menu submission when the suggestion unit makes suggestions. For example, the suggestion unit can determine the priority of suggestions based on the time of menu submission when making suggestions. The time of menu submission includes, but is not limited to, the submission date and time, the frequency of submission, and the timing of submission. For example, the suggestion unit prioritizes suggesting menus based on allergy information with high urgency. The suggestion unit can also prioritize suggesting menus based on ingredient information with an approaching deadline. The suggestion unit can also analyze the user's mood information in real time and prioritize suggesting menus. This allows the priority of suggestions to be determined based on the time of menu submission.

[0055] The menu suggestion system adjusts the order of suggestions based on the relevance of the menus when the suggestion unit suggests the menus. For example, the suggestion unit can adjust the order of suggestions based on the relevance of the menus when suggesting the menus. Examples of the relevance of the menus include, but are not limited to, interrelationships, commonalities, and degrees of relevance between the menus. For example, the suggestion unit determines the order of suggestions taking into account the relevance between allergy information and ingredient information. The suggestion unit can also determine the order of suggestions taking into account the relevance between ingredient information and mood information. The suggestion unit can also determine the order of suggestions taking into account the relevance between allergy information and mood information. This makes it possible to adjust the order of suggestions based on the relevance of the menus.

[0056] The menu suggestion system adjusts the use of technical terms in the suggestions made by the suggestion unit according to the user's level of expertise. For example, the suggestion unit can adjust the use of technical terms in the suggestions made according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, the user's level of understanding of technical terms, past learning history, and work experience. For example, the suggestion unit uses a lot of technical terms when the user has specialized knowledge. Furthermore, the suggestion unit can use simple terms when the user only has general knowledge. Furthermore, the suggestion unit can avoid using technical terms when the user is a beginner. This allows the use of technical terms in the suggestions to be adjusted according to the user's level of expertise.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The menu suggestion system can also collect the user's meal history, and the analysis unit can analyze the user's eating patterns based on that history. For example, it can identify the menus the user liked in the past and the ingredients they eat frequently, and make suggestions based on that. It can also take into account the ingredients and dishes the user likes in specific seasons. It can also detect imbalances in nutritional balance from the user's meal history and suggest menus that supplement the necessary nutrients. This makes it possible to make more personalized menu suggestions by utilizing the user's meal history.

[0059] The menu suggestion system also collects the user's exercise history, and the analysis unit can suggest menus based on the user's exercise volume. For example, it can suggest a high-calorie, nutritious menu on days when the user exercised heavily, and a low-calorie, light menu on days when the user exercised less. It can also suggest menus containing nutrients suitable for after a specific exercise session. It can also provide a meal plan tailored to the user's exercise goals. This makes it possible to suggest healthy meals that take the user's exercise history into consideration.

[0060] The menu suggestion system also collects the user's sleep data, and the analysis unit can use that data to suggest menus based on the quality of sleep. For example, if the user did not get enough sleep, the system can suggest a menu suitable for replenishing energy, and conversely, if the user got good quality sleep, the system can suggest a lighter menu. It can also suggest menus that include ingredients that improve sleep quality. Furthermore, it can suggest appropriate meal timings based on the user's sleep patterns. This makes it possible to suggest healthy meals using the user's sleep data.

[0061] The menu suggestion system also collects the user's health checkup data, and the analysis unit can suggest menus based on that data according to the user's health condition. For example, it can suggest menus to improve the user's health condition based on data such as the user's blood pressure, blood sugar level, and cholesterol level. It can also suggest menus that include ingredients to reduce risks for users with specific health risks. It can also provide meal plans tailored to the user's health goals. This makes it possible to suggest healthy meals that utilize the user's health checkup data.

[0062] The menu suggestion system can also collect a user's purchasing history, and the analysis unit can analyze purchasing patterns based on that history. For example, it can identify ingredients that a user has purchased in the past and frequently purchased items and make suggestions based on that. It can also take into account ingredients and dishes that a user purchases in specific seasons. It can also suggest menus that reduce food waste based on the user's purchasing history. This makes it possible to make more personalized menu suggestions by utilizing the user's purchasing history.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The collection unit collects information. This information includes family members' food allergies, likes and dislikes, ingredients in the refrigerator, and their mood for the day. The collection unit allows users to register their family members' food allergies and likes and dislikes in advance, input information about ingredients in the refrigerator, and even input their mood for the day. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis is carried out using methods such as data mining techniques, statistical analysis, and machine learning algorithms. The analysis unit analyzes the information to make the most of the ingredients in the refrigerator, taking into account the family's food allergies and likes and dislikes, and to suggest the optimal menu to suit that day's mood. Step 3: The suggestion unit suggests an appropriate menu based on the information analyzed by the analysis unit. Suggestions are made based on criteria such as nutritional balance, calorie restrictions, and allergy-friendly options. If someone has a specific allergy, the suggestion unit will suggest a menu that does not include that allergy. Also, if there are specific ingredients in the refrigerator, the suggestion unit can suggest dishes that use those ingredients. Furthermore, if the user inputs, "I want to eat something light today," the suggestion unit can suggest light dishes.

[0065] (Example 2) A menu suggestion system according to an embodiment of the present invention uses AI to analyze family members' food allergies, likes and dislikes, the ingredients in the refrigerator, and their mood for the day, and then suggests optimal menus. The menu suggestion system allows users to register their family members' food allergies and likes and dislikes in advance, input information about the ingredients in the refrigerator, and input their mood for the day. The AI ​​analyzes this information and suggests optimal menus. For example, if a family member has a specific allergy, the system suggests a menu that does not include that allergy. If a specific ingredient is in the refrigerator, the system suggests dishes that use that ingredient. Furthermore, if a user inputs, "I want something light today," the system suggests light dishes. This allows the whole family to enjoy meals without worry. It also allows the system to use ingredients in the refrigerator efficiently, thereby reducing food waste. Furthermore, by suggesting menus tailored to the user's mood, the system improves meal satisfaction.

[0066] A menu suggestion system according to an embodiment includes a collection unit, an analysis unit, and a suggestion unit. The collection unit collects information. Examples of the information include, but are not limited to, family members' food allergies, likes and dislikes, ingredients in the refrigerator, and their mood for the day. For example, the collection unit allows a user to register their family members' food allergies and likes and dislikes in advance. The collection unit can also input information about ingredients in the refrigerator. Furthermore, the collection unit can also input the user's mood for the day. The analysis unit analyzes the information collected by the collection unit. The analysis can be performed using, but is not limited to, data mining techniques, statistical analysis, machine learning algorithms, or other methods. For example, the analysis unit analyzes the information to suggest an optimal menu that makes the most of the ingredients in the refrigerator while taking into account the family members' food allergies and likes and dislikes and that matches their mood for the day. The suggestion unit suggests an appropriate menu based on the information analyzed by the analysis unit. The suggestion can be performed based on, but is not limited to, criteria such as nutritional balance, calorie restriction, and allergy-friendly options. For example, if a member has a specific allergy, the suggestion unit can suggest a menu that does not include that allergy. Furthermore, if a user has specific ingredients in the refrigerator, the suggestion unit can suggest dishes that use those ingredients. Furthermore, if the user inputs, "I want to eat something light today," the suggestion unit can suggest light dishes. In this way, the menu suggestion system according to the embodiment can collect and analyze information and suggest optimal menus.

[0067] The menu suggestion system includes an allergy information collecting unit. The allergy information collecting unit allows, for example, a user to register food allergies of family members in advance. The allergy information includes, but is not limited to, food allergies, drug allergies, and environmental allergies. For example, the allergy information collecting unit allows a user to input in detail the type of allergy and the intensity of the reaction. This allows the allergy information to be collected.

[0068] The menu suggestion system includes a unit for collecting ingredient information. The unit for collecting ingredient information allows, for example, a user to input information about ingredients in a refrigerator. The ingredient information includes, for example, nutritional components, origin, and storage method of ingredients, but is not limited to these examples. For example, the unit for collecting ingredient information allows a user to input specific ingredients. This allows the ingredient information to be collected.

[0069] The menu suggestion system includes a mood information collecting unit. The mood information collecting unit allows, for example, a user to input today's mood. Mood information includes, but is not limited to, the user's emotional state, stress level, mood fluctuations, etc. For example, the mood information collecting unit allows the user to input information such as "I want to eat something light today" or "I want to eat something substantial today." This allows mood information to be collected.

[0070] The menu suggestion system includes an allergy information analyzing unit. The allergy information analyzing unit can, for example, analyze the collected allergy information. The analysis of the allergy information includes, but is not limited to, identifying allergens and the intensity of allergic reactions. For example, the allergy information analyzing unit can identify allergens and evaluate the intensity of allergic reactions based on the collected allergy information. This allows the allergy information to be analyzed.

[0071] The menu suggestion system includes a unit that analyzes ingredient information. The unit that analyzes ingredient information can, for example, analyze collected ingredient information. Analysis of ingredient information includes, but is not limited to, calculation of nutritional value and the effect of ingredient combinations. For example, the unit that analyzes ingredient information can calculate nutritional value and evaluate the effect of ingredient combinations based on the collected ingredient information. This allows the ingredient information to be analyzed.

[0072] The menu suggestion system includes a mood information analyzing unit. The mood information analyzing unit can, for example, analyze the collected mood information. The analysis of mood information includes, for example, emotion analysis, stress level evaluation, and the like, but is not limited to these examples. For example, the mood information analyzing unit can perform emotion analysis based on the collected mood information and evaluate the stress level. This allows the mood information to be analyzed.

[0073] The menu suggestion system includes a unit for suggesting allergy-friendly menus. The unit for suggesting allergy-friendly menus can suggest allergy-friendly menus, for example, based on collected allergy information. Suggestions for allergy-friendly menus include, but are not limited to, removing allergens and suggesting alternative ingredients. For example, the unit for suggesting allergy-friendly menus can suggest menus that do not contain allergens and suggest alternative ingredients. This allows allergy-friendly menus to be suggested.

[0074] The menu suggestion system includes a unit that suggests menus that utilize ingredients. The unit that suggests menus that utilize ingredients can suggest menus that utilize ingredients based on, for example, collected ingredient information. Proposing menus that utilize ingredients includes, but is not limited to, using seasonal ingredients and taking nutritional balance into consideration. For example, the unit that suggests menus that utilize ingredients can suggest menus that use seasonal ingredients and take nutritional balance into consideration. This makes it possible to suggest menus that utilize ingredients.

[0075] The menu suggestion system includes a unit for suggesting a menu that matches the mood. The unit for suggesting a menu that matches the mood can suggest a menu that matches the mood, for example, based on collected mood information. Suggestions for a menu that matches the mood include, but are not limited to, ingredients that have a relaxing effect and ingredients that are suitable for replenishing energy. For example, the unit for suggesting a menu that matches the mood can suggest a menu that uses ingredients that have a relaxing effect or ingredients that are suitable for replenishing energy. This makes it possible to suggest a menu that matches the mood.

[0076] In the menu suggestion system, a collection unit estimates a user's emotions and adjusts the timing of information collection based on the estimated user emotions. The collection unit can, for example, estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and self-reporting. For example, if the user is feeling stressed, the collection unit collects information during a relaxed time. If the user is busy, the collection unit can also collect information in a short time. If the user is relaxed, the collection unit can also collect detailed information. This makes it possible to adjust the timing of information collection based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0077] In the menu suggestion system, the collection unit analyzes the user's past information collection history and selects an appropriate collection method. The collection unit can, for example, analyze the user's past information collection history and select an appropriate collection method. The information collection history includes, for example, past collection data, collection frequency, and the effectiveness of the collection method, but is not limited to these examples. For example, the collection unit prioritizes collection methods that the user has frequently used in the past. The collection unit can also refer to the collection timing that the user preferred in the past. The collection unit can also suggest the optimal collection means based on the user's past collection history. In this way, the user's past information collection history can be analyzed and an appropriate collection method can be selected.

[0078] In the menu suggestion system, when the collection unit collects information, the collection unit filters the information based on the user's current living situation and areas of interest. For example, when collecting information, the collection unit can filter the information based on the user's current living situation and areas of interest. Examples of living situations include, but are not limited to, lifestyle habits, occupation, and home environment. Examples of areas of interest include, but are not limited to, hobbies, topics of interest, and past search history. For example, if the user is health-conscious, the collection unit can prioritize collecting health-related information. Furthermore, if the user is on a diet, the collection unit can collect information about low-calorie ingredients. Furthermore, if the user is interested in a particular ingredient, the collection unit can collect information about that ingredient. This makes it possible to filter information based on the user's current living situation and areas of interest.

[0079] In the menu suggestion system, when the collection unit collects information, it selects an appropriate collection means according to the user's input method. For example, when collecting information, the collection unit can select an appropriate collection means according to the user's input method (voice, text, image, etc.). Input methods include, but are not limited to, voice input, text input, image input, etc. For example, when the user uses voice input, the collection unit can collect information using voice recognition technology. Furthermore, when the user uses text input, the collection unit can also collect information using text analysis technology. Furthermore, when the user uses image input, the collection unit can also collect information using image recognition technology. In this way, it is possible to select an appropriate collection means according to the user's input method.

[0080] In the menu suggestion system, a collection unit estimates a user's emotions and determines the priority of information to be collected based on the estimated user emotions. The collection unit can, for example, estimate a user's emotions and determine the priority of information to be collected based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and self-reporting. For example, if the user is tired, the collection unit can prioritize easy-to-cook recipe information. Also, if the user is energetic, the collection unit can prioritize recipe information that allows the user to try new dishes. Also, if the user is stressed, the collection unit can prioritize information on ingredients that are relaxing. In this way, the priority of information to be collected can be determined based on the user's emotions. 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.

[0081] In the menu suggestion system, when collecting information, the collection unit prioritizes collecting highly relevant information by taking into consideration the user's geographical location information. For example, when collecting information, the collection unit can prioritize collecting highly relevant information by taking into consideration the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, location information services, etc. For example, if the user lives in a specific area, the collection unit can collect information on ingredients available in that area. Furthermore, if the user is traveling, the collection unit can also collect information on ingredients available at the travel destination. Furthermore, if the user is interested in cuisine from a specific area, the collection unit can also collect recipe information for that area. In this way, highly relevant information can be prioritized by taking into consideration the user's geographical location information.

[0082] In the menu suggestion system, when the collection unit collects information, the collection unit analyzes the user's social media activity and collects related information. For example, when collecting information, the collection unit can analyze the user's social media activity and collect related information. Social media activity includes, but is not limited to, the content of posts, the number of likes, the number of followers, etc. For example, the collection unit collects recipe information shared by the user on social media. The collection unit can also analyze the content of the user's social media posts and collect related ingredient information. The collection unit can also refer to the activities of the user's friends on social media to collect related recipe information. In this way, the user's social media activity can be analyzed and related information can be collected.

[0083] In the menu suggestion system, when the collection unit collects information, the collection method is customized by reflecting the user's past feedback. For example, the collection unit can customize the collection method by reflecting the user's past feedback when collecting information. Feedback includes, but is not limited to, survey results, reviews, comments, etc. For example, the collection unit prioritizes the collection method that the user has previously preferred. The collection unit can also select the optimal collection timing based on the user's past feedback. The collection unit can also adjust the type of information to be collected based on the user's past feedback. This allows the collection method to be customized by reflecting the user's past feedback.

[0084] In the menu suggestion system, an analysis unit estimates a user's emotion and adjusts the presentation of the analysis based on the estimated user emotion. The analysis unit can, for example, estimate the user's emotion and adjust the presentation of the analysis based on the estimated user emotion. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and self-reporting. For example, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can also provide concise analysis results when the user is in a hurry. The analysis unit can also provide visually stimulating analysis results when the user is excited. This allows the presentation of the analysis to be adjusted based on the user's emotion. 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.

[0085] In the menu suggestion system, the analysis unit adjusts the level of detail of the analysis based on the importance of the information during analysis. The analysis unit can, for example, adjust the level of detail of the analysis based on the importance of the information during analysis. The importance of the information includes, but is not limited to, the urgency, impact, and relevance of the information. For example, the analysis unit performs a detailed analysis of important allergy information. The analysis unit can also perform a concise analysis of general ingredient information. The analysis unit can also perform an appropriate level of detail analysis of the user's mood information. This makes it possible to adjust the level of detail of the analysis based on the importance of the information.

[0086] In the menu suggestion system, the analysis unit applies different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply different analysis algorithms depending on the category of information during analysis. Information categories include, but are not limited to, health information, ingredient information, and allergy information. For example, the analysis unit can apply an allergen detection algorithm to allergy information. The analysis unit can also apply a nutritional value analysis algorithm to ingredient information. The analysis unit can also apply an emotion analysis algorithm to mood information. This makes it possible to apply different analysis algorithms depending on the category of information.

[0087] In the menu suggestion system, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results during analysis. The analysis unit can, for example, improve the accuracy of the analysis by referring to the user's past analysis results during analysis. Past analysis results include, but are not limited to, past analysis data, evaluations of analysis results, and areas for improvement. For example, the analysis unit refers to the user's past analysis results of allergy information. The analysis unit can also refer to the user's past analysis results of ingredient information. The analysis unit can also refer to the user's past analysis results of mood information. This makes it possible to improve the accuracy of the analysis by referring to the user's past analysis results.

[0088] In the menu suggestion system, an analysis unit estimates a user's emotion and adjusts the length of the analysis based on the estimated user emotion. The analysis unit can, for example, estimate a user's emotion and adjust the length of the analysis based on the estimated user emotion. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and self-reporting. For example, the analysis unit can perform a short and to-the-point analysis when the user is in a hurry. The analysis unit can also perform a detailed analysis when the user is relaxed. The analysis unit can also perform a visually stimulating analysis when the user is excited. This allows the length of the analysis to be adjusted based on the user's emotion. 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.

[0089] In the menu suggestion system, the analysis unit determines the analysis priority based on the time of information submission during analysis. The analysis unit can, for example, determine the analysis priority based on the time of information submission during analysis. The time of information submission includes, but is not limited to, the submission date and time, the submission frequency, and the submission timing. For example, the analysis unit prioritizes analysis of allergy information with high urgency. The analysis unit can also prioritize analysis of ingredient information with an approaching deadline. The analysis unit can also analyze the user's mood information in real time. This allows the analysis priority to be determined based on the time of information submission.

[0090] In the menu suggestion system, the analysis unit adjusts the analysis order based on the relevance of information during analysis. The analysis unit can, for example, adjust the analysis order based on the relevance of information during analysis. The relevance of information includes, but is not limited to, interrelationships, commonalities, and degrees of association of information. For example, the analysis unit determines the analysis order taking into account the relevance of allergy information and ingredient information. The analysis unit can also determine the analysis order taking into account the relevance of ingredient information and mood information. The analysis unit can also determine the analysis order taking into account the relevance of allergy information and mood information. This makes it possible to adjust the analysis order based on the relevance of information.

[0091] In the menu suggestion system, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. Examples of the level of expertise include, but are not limited to, the level of understanding of technical terms, past learning history, and work experience. For example, the analysis unit uses a lot of technical terms when the user has specialized knowledge. Furthermore, the analysis unit can use simple terms when the user only has general knowledge. Furthermore, the analysis unit can avoid using technical terms when the user is a beginner. This allows the use of technical terms in the analysis to be adjusted according to the user's level of expertise.

[0092] In the menu suggestion system, a suggestion unit estimates a user's emotion and adjusts the way suggestions are presented based on the estimated user emotion. The suggestion unit can, for example, estimate the user's emotion and adjust the way suggestions are presented based on the estimated user emotion. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and self-reporting. For example, the suggestion unit can provide detailed suggestions when the user is relaxed. The suggestion unit can also provide concise suggestions when the user is in a hurry. The suggestion unit can also provide visually stimulating suggestions when the user is excited. This allows the way suggestions are presented to be adjusted based on the user's emotion. 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.

[0093] The menu suggestion system adjusts the level of detail of the suggestion based on the importance of the menu when the suggestion unit makes a suggestion. For example, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the menu when making a suggestion. Examples of the importance of a menu include, but are not limited to, nutritional value, calories, and allergy-friendly options. For example, the suggestion unit makes detailed suggestions for menus based on important allergy information. The suggestion unit can also make concise suggestions for menus based on general ingredient information. The suggestion unit can also make suggestions with an appropriate level of detail for menus based on the user's mood information. This makes it possible to adjust the level of detail of the suggestion based on the importance of the menu.

[0094] The menu suggestion system applies different suggestion algorithms depending on the category of the menu when the suggestion unit suggests the menu. For example, the suggestion unit can apply different suggestion algorithms depending on the category of the menu when suggesting the menu. Menu categories include, but are not limited to, breakfast, lunch, dinner, and dessert. For example, the suggestion unit can apply an allergen removal algorithm to a menu based on allergy information. The suggestion unit can also apply a nutritional value optimization algorithm to a menu based on ingredient information. The suggestion unit can also apply a sentiment analysis algorithm to a menu based on mood information. This makes it possible to apply different suggestion algorithms depending on the category of the menu.

[0095] In the menu suggestion system, when making a suggestion, the suggestion unit refers to the user's past suggestion results to improve the accuracy of the suggestion. For example, when making a suggestion, the suggestion unit can refer to the user's past suggestion results to improve the accuracy of the suggestion. Past suggestion results include, but are not limited to, past suggestion data, evaluations of suggestion results, and areas for improvement. For example, the suggestion unit refers to suggestion results based on the user's past allergy information. The suggestion unit can also refer to suggestion results based on the user's past ingredient information. The suggestion unit can also refer to suggestion results based on the user's past mood information. This allows the accuracy of the suggestion to be improved by referring to the user's past suggestion results.

[0096] In the menu suggestion system, a suggestion unit estimates a user's emotion and adjusts the length of the suggestion based on the estimated user emotion. The suggestion unit can, for example, estimate the user's emotion and adjust the length of the suggestion based on the estimated user emotion. Emotion estimation includes, for example, facial expression recognition, voice analysis, and self-reporting, but is not limited to these examples. For example, the suggestion unit can make short and to-the-point suggestions when the user is in a hurry. The suggestion unit can also make detailed suggestions when the user is relaxed. The suggestion unit can also make visually stimulating suggestions when the user is excited. This allows the length of the suggestion to be adjusted based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using 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 these examples.

[0097] The menu suggestion system determines the priority of suggestions based on the time of menu submission when the suggestion unit makes suggestions. For example, the suggestion unit can determine the priority of suggestions based on the time of menu submission when making suggestions. The time of menu submission includes, but is not limited to, the submission date and time, the frequency of submission, and the timing of submission. For example, the suggestion unit prioritizes suggesting menus based on allergy information with high urgency. The suggestion unit can also prioritize suggesting menus based on ingredient information with an approaching deadline. The suggestion unit can also analyze the user's mood information in real time and prioritize suggesting menus. This allows the priority of suggestions to be determined based on the time of menu submission.

[0098] The menu suggestion system adjusts the order of suggestions based on the relevance of the menus when the suggestion unit suggests the menus. For example, the suggestion unit can adjust the order of suggestions based on the relevance of the menus when suggesting the menus. Examples of the relevance of the menus include, but are not limited to, interrelationships, commonalities, and degrees of relevance between the menus. For example, the suggestion unit determines the order of suggestions taking into account the relevance between allergy information and ingredient information. The suggestion unit can also determine the order of suggestions taking into account the relevance between ingredient information and mood information. The suggestion unit can also determine the order of suggestions taking into account the relevance between allergy information and mood information. This makes it possible to adjust the order of suggestions based on the relevance of the menus.

[0099] The menu suggestion system adjusts the use of technical terms in the suggestions made by the suggestion unit according to the user's level of expertise. For example, the suggestion unit can adjust the use of technical terms in the suggestions made according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, the user's level of understanding of technical terms, past learning history, and work experience. For example, the suggestion unit uses a lot of technical terms when the user has specialized knowledge. Furthermore, the suggestion unit can use simple terms when the user only has general knowledge. Furthermore, the suggestion unit can avoid using technical terms when the user is a beginner. This allows the use of technical terms in the suggestions to be adjusted according to the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and suggestion unit described above may be realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit may be realized by the control unit 46A of the smart device 14, and the user may input information about family members' food allergies, likes and dislikes, ingredients in the refrigerator, and their mood that day. The analysis unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information. The suggestion unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal menu based on the analysis results. Some or all of the collection unit, analysis unit, and suggestion unit may be realized, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and suggestion unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214, and allows the user to input information about family members' food allergies, likes and dislikes, ingredients in the refrigerator, and their mood that day. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests an optimal menu based on the analysis results. Some or all of the collection unit, analysis unit, and suggestion unit may be realized, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and suggestion unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset-type terminal 314, and allows the user to input information about family members' food allergies, likes and dislikes, ingredients in the refrigerator, and their mood that day. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests an optimal menu based on the analysis results. Some or all of the collection unit, analysis unit, and suggestion unit may be realized, for example, by the control unit 46A of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and suggestion unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414, and allows the user to input information about family members' food allergies, likes and dislikes, ingredients in the refrigerator, and their mood that day. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests an optimal menu based on the analysis results. Some or all of the collection unit, analysis unit, and suggestion unit may be realized, for example, by the control unit 46A of the robot 414.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The menu suggestion system can also collect the user's meal history, and the analysis unit can analyze the user's eating patterns based on that history. For example, it can identify the menus the user liked in the past and the ingredients they eat frequently, and make suggestions based on that. It can also take into account the ingredients and dishes the user likes in specific seasons. It can also detect imbalances in nutritional balance from the user's meal history and suggest menus that supplement the necessary nutrients. This makes it possible to make more personalized menu suggestions by utilizing the user's meal history.

[0102] The menu suggestion system also collects the user's exercise history, and the analysis unit can suggest menus based on the user's exercise volume. For example, it can suggest a high-calorie, nutritious menu on days when the user exercised heavily, and a low-calorie, light menu on days when the user exercised less. It can also suggest menus containing nutrients suitable for after a specific exercise session. It can also provide a meal plan tailored to the user's exercise goals. This makes it possible to suggest healthy meals that take the user's exercise history into consideration.

[0103] The menu suggestion system also collects the user's sleep data, and the analysis unit can use that data to suggest menus based on the quality of sleep. For example, if the user did not get enough sleep, the system can suggest a menu suitable for replenishing energy, and conversely, if the user got good quality sleep, the system can suggest a lighter menu. It can also suggest menus that include ingredients that improve sleep quality. Furthermore, it can suggest appropriate meal timings based on the user's sleep patterns. This makes it possible to suggest healthy meals using the user's sleep data.

[0104] The menu suggestion system also collects the user's health checkup data, and the analysis unit can suggest menus based on that data according to the user's health condition. For example, it can suggest menus to improve the user's health condition based on data such as the user's blood pressure, blood sugar level, and cholesterol level. It can also suggest menus that include ingredients to reduce risks for users with specific health risks. It can also provide meal plans tailored to the user's health goals. This makes it possible to suggest healthy meals that utilize the user's health checkup data.

[0105] The menu suggestion system can also collect a user's purchasing history, and the analysis unit can analyze purchasing patterns based on that history. For example, it can identify ingredients that a user has purchased in the past and frequently purchased items and make suggestions based on that. It can also take into account ingredients and dishes that a user purchases in specific seasons. It can also suggest menus that reduce food waste based on the user's purchasing history. This makes it possible to make more personalized menu suggestions by utilizing the user's purchasing history.

[0106] The menu suggestion system uses an analysis unit to estimate the user's emotions and adjust the menu suggestion method based on the estimated user's emotions. For example, if the user is feeling stressed, a menu using ingredients with a relaxing effect can be suggested. If the user is tired, a menu suitable for replenishing energy can be suggested. Furthermore, if the user is happy, a special dish or dessert can be suggested. This makes it possible to suggest more appropriate menus based on the user's emotions.

[0107] The menu suggestion system uses an analysis unit to estimate the user's emotions and adjust the menu variations based on the estimated user emotions. For example, if the user is bored, new or exotic dishes can be suggested. If the user is excited, easy-to-prepare dishes can be suggested. Furthermore, if the user is relaxed, dishes that take time to prepare can be suggested. This makes it possible to adjust the menu variations based on the user's emotions.

[0108] The menu suggestion system uses an analysis unit to estimate the user's emotions and adjust the menu presentation method based on the estimated user's emotions. For example, if the user is tired, a simple and easy-to-read presentation can be made. If the user is excited, a visually stimulating presentation can be made. Furthermore, if the user is relaxed, a presentation including detailed explanations and background information can be made. This makes it possible to adjust the menu presentation method based on the user's emotions.

[0109] The menu suggestion system uses an analysis unit to estimate the user's emotions and adjust menu options based on the estimated user emotions. For example, if the user is feeling stressed, multiple menus using ingredients with a relaxing effect can be suggested. If the user is tired, multiple menus suitable for replenishing energy can be suggested. Furthermore, if the user is happy, multiple special dishes or desserts can be suggested. This makes it possible to adjust menu options based on the user's emotions.

[0110] The menu suggestion system allows the analysis unit to estimate the user's emotions and adjust the frequency of menu suggestions based on the estimated user emotions. For example, if the user is feeling stressed, menus with a relaxing effect can be frequently suggested. Also, if the user is tired, menus suitable for replenishing energy can be frequently suggested. Furthermore, if the user is happy, special dishes or desserts can be frequently suggested. This makes it possible to adjust the frequency of menu suggestions based on the user's emotions.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The collection unit collects information. This information includes family members' food allergies, likes and dislikes, ingredients in the refrigerator, and their mood for the day. The collection unit allows users to register their family members' food allergies and likes and dislikes in advance, input information about ingredients in the refrigerator, and even input their mood for the day. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis is carried out using methods such as data mining techniques, statistical analysis, and machine learning algorithms. The analysis unit analyzes the information to make the most of the ingredients in the refrigerator, taking into account the family's food allergies and likes and dislikes, and to suggest the optimal menu to suit that day's mood. Step 3: The suggestion unit suggests an appropriate menu based on the information analyzed by the analysis unit. Suggestions are made based on criteria such as nutritional balance, calorie restrictions, and allergy-friendly options. If someone has a specific allergy, the suggestion unit will suggest a menu that does not include that allergy. Also, if there are specific ingredients in the refrigerator, the suggestion unit can suggest dishes that use those ingredients. Furthermore, if the user inputs, "I want to eat something light today," the suggestion unit can suggest light dishes.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

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

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The 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.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] Fig. 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.

[0141] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the 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.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0147] The data processing system 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.

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

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0152] The 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.

[0153] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] [Explanation of symbols]

[0185] 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 collection unit that collects information; an analysis unit that analyzes the information collected by the collection unit; a suggestion unit that suggests an appropriate menu based on the information analyzed by the analysis unit; Equipped with A system characterized by:

2. Have a department to collect allergy information 2. The system of claim 1.

3. Equipped with a department for collecting food ingredient information 2. The system of claim 1.

4. Equipped with a section for collecting mood information 2. The system of claim 1.

5. Equipped with a unit that analyzes allergy information 2. The system of claim 1.

6. Equipped with a unit that analyzes food ingredient information 2. The system of claim 1.

7. Equipped with a part that analyzes mood information 2. The system of claim 1.

8. We have a department that proposes menus that take allergies into consideration.

2. The system of claim 1.

9. We have a department that proposes menus that utilize ingredients.

2. The system of claim 1.

10. Equipped with a department that suggests menus to suit your mood 2. The system of claim 1.

Images