system

Abstract

The system according to this embodiment aims to efficiently manage food items in a refrigerator and suggest the optimal meal menu. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a proposal unit, a recognition unit, a recommendation unit, and a generation unit. The reception unit receives a photograph of the inside of the refrigerator from the user. The analysis unit analyzes the photograph received by the reception unit and recognizes the food items. The proposal unit proposes a meal menu based on the food items recognized by the analysis unit. The recognition unit recognizes any missing items or food items that need to be restocked based on the menu proposed by the proposal unit. The recommendation unit recommends necessary food items or daily necessities based on the information recognized by the recognition unit. The generation unit generates a purchase method and shopping list for the food items and daily necessities recommended by the recommendation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to efficiently manage the food ingredients in the refrigerator and propose an optimal diet menu.

[0005] The system according to the embodiment aims to efficiently manage the food ingredients in the refrigerator and propose an optimal diet menu.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a proposal unit, a recognition unit, a recommendation unit, and a generation unit. The reception unit receives a photograph of the inside of a refrigerator from the user. The analysis unit analyzes the photograph received by the reception unit and recognizes the food items. The proposal unit proposes a meal menu based on the food items recognized by the analysis unit. The recognition unit identifies any missing items or food items that need to be restocked based on the menu proposed by the proposal unit. The recommendation unit recommends necessary food items or daily necessities based on the information identified by the recognition unit. The generation unit generates a shopping list and instructions on how to purchase the food items and daily necessities recommended by the recommendation unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently manage food items in a refrigerator and suggest the optimal meal menu. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The meal suggestion system according to an embodiment of the present invention is a system in which a user provides a photograph of the inside of their refrigerator, and an AI analyzes the user's consumption behavior, eating patterns, lifestyle, and nutritional needs, and creates a healthy and balanced meal menu based on available ingredients. In this system, the user provides a photograph of the inside of their refrigerator, the AI ​​analyzes the photograph and recognizes the ingredients. Based on the recognized ingredients, the AI ​​suggests a meal menu. Furthermore, the AI ​​analyzes the contents of the refrigerator to identify missing items and ingredients that need to be restocked. It also analyzes the balance of nutrients and recommends necessary ingredients and daily necessities. Finally, the AI ​​generates and provides the user with the optimal purchasing method and shopping list. For example, the user provides a photograph of the inside of their refrigerator. In this case, the user only needs to take a photograph of the entire inside of the refrigerator. For example, the user opens the refrigerator door and takes a photograph of the inside. This information is input to the AI. Next, the AI ​​analyzes the input photograph and recognizes the ingredients. The AI ​​uses image analysis technology to identify each ingredient in the refrigerator. For example, it recognizes ingredients such as vegetables, fruits, meats, and dairy products. This creates a list of ingredients in the refrigerator. Based on the recognized ingredients, the AI ​​suggests a meal menu. The AI, combined with a recipe database, creates healthy and balanced meal plans based on available ingredients. For example, it suggests recipes for salads and soups using vegetables and meat already in the refrigerator. Furthermore, the AI ​​analyzes the contents of the refrigerator to identify missing items and ingredients that need replenishing. For instance, if the amount of milk in the refrigerator is low, it will recognize that it needs to be refilled. It also analyzes nutritional balance and recommends necessary ingredients and household items. The AI ​​considers the user's eating habits and nutritional needs to recommend ingredients that supplement necessary nutrients. For example, if there is a vitamin C deficiency, it will recommend ingredients such as oranges and broccoli. Finally, the AI ​​generates and provides the user with the optimal purchasing method and shopping list. For example, it can generate purchase links to online shopping sites or shopping lists for nearby supermarkets. This allows the user to efficiently purchase the necessary ingredients.This allows the meal suggestion system to analyze photos of the user's refrigerator, recognize ingredients, suggest meal menus, identify missing items and ingredients that need replenishing, recommend necessary ingredients and daily necessities, and generate purchasing instructions and shopping lists.

[0029] The meal suggestion system according to this embodiment comprises a reception unit, an analysis unit, a suggestion unit, a recognition unit, a recommendation unit, and a generation unit. The reception unit receives a photograph of the inside of a refrigerator from the user. The photograph provided by the user may include, for example, a photograph of the entire inside of the refrigerator, but is not limited to such an example. The reception unit can accept photographs in, for example, JPEG or PNG format. The reception unit can also convert the photograph to a format suitable for analysis, taking into account the resolution of the photograph. The analysis unit analyzes the photograph received by the reception unit and recognizes the ingredients. The analysis unit identifies each ingredient in the refrigerator using image analysis technology. For example, it recognizes ingredients such as vegetables, fruits, meats, and dairy products. The analysis unit can improve the accuracy of ingredient recognition and the type of ingredients it can identify using an image recognition algorithm. The suggestion unit proposes a meal menu based on the ingredients recognized by the analysis unit. The suggestion unit combines this with a recipe database to create a healthy and balanced meal menu based on the available ingredients. For example, it proposes recipes for salads and soups using vegetables and meat found in the refrigerator. The suggestion unit can propose an optimal menu, taking into account nutritional balance and the user's preferences. The information gathering unit identifies missing items and ingredients that need to be replenished based on the menu proposed by the suggestion unit. The information gathering unit can identify ingredients that need to be replenished by considering the inventory levels and frequency of use of ingredients in the refrigerator. The recommendation unit recommends necessary ingredients and daily necessities based on the information gathered by the information gathering unit. The recommendation unit considers the user's eating habits and nutritional needs and recommends ingredients to supplement necessary nutrients. For example, if there is a vitamin C deficiency, it will recommend ingredients such as oranges and broccoli. The generation unit generates purchasing methods and shopping lists for the ingredients and daily necessities recommended by the recommendation unit. The generation unit can generate purchase links to online shopping sites or purchase lists to nearby supermarkets. For example, the generation unit suggests the optimal purchasing method so that the user can efficiently purchase the necessary ingredients. Thus, the meal suggestion system according to the embodiment can analyze a photo of the user's refrigerator, recognize ingredients, propose meal menus, identify missing items and ingredients that need to be replenished, recommend necessary ingredients and daily necessities, and generate purchasing methods and shopping lists.

[0030] The reception desk can analyze a user's past photo submission history and select an appropriate submission method. For example, the reception desk can analyze the time periods when a user frequently submitted photos in the past and prompt them to submit photos during those times. The reception desk can also prioritize suggesting photo submission methods (smartphone, tablet, etc.) that the user has used in the past. Furthermore, the reception desk can prompt submissions on specific days of the week or time periods based on the user's past photo submission history. In this way, the optimal submission method can be selected by analyzing the user's past photo submission history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past photo submission history data into a generating AI and have the generating AI select the optimal submission method.

[0031] The reception unit can filter photos based on the user's current eating habits and areas of interest when they are received. For example, if the user is health-conscious, the reception unit can prioritize receiving photos related to healthy foods. It can also prioritize receiving photos of foods related to a specific diet if the user is following one. Furthermore, if the user has an allergy to a particular food, the reception unit can prioritize receiving photos that do not contain that food. This allows for the reception of more relevant photos by filtering based on the user's current eating habits and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input data on the user's eating habits and areas of interest into a generating AI and have the generating AI perform the filtering.

[0032] The reception unit can prioritize receiving photos that are highly relevant based on the user's geographical location information. For example, if the user lives in a specific region, the reception unit can prioritize receiving photos of ingredients available in that region. If the user is traveling, the reception unit can also prioritize receiving photos of ingredients available at their travel destination. Furthermore, if the user shops at a specific store, the reception unit can prioritize receiving photos of ingredients available at that store. This allows for the prioritization of highly relevant photos by considering the user's geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI select highly relevant photos.

[0033] The reception unit can analyze the user's social media activity when receiving photos and accept relevant photos. For example, the reception unit can prioritize accepting photos of ingredients that the user has shared on social media. It can also prioritize accepting photos of cooking accounts that the user follows on social media. Furthermore, it can prioritize accepting photos of ingredients that the user has "liked" on social media. In this way, by analyzing the user's social media activity, it is possible to prioritize accepting relevant photos. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI select relevant photos.

[0034] The analysis unit can adjust the level of detail of the analysis based on the importance of the ingredients during the analysis. For example, the analysis unit can perform a detailed analysis for major ingredients and a simplified analysis for secondary ingredients. It can also perform a detailed analysis for ingredients with high nutritional value and a simplified analysis for ingredients with low nutritional value. Furthermore, the analysis unit can perform a detailed analysis for ingredients of high importance according to the user's preference. This allows for more detailed analysis of important ingredients by adjusting the level of detail of the analysis based on the importance of the ingredients. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input ingredient importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0035] The analysis unit can apply different analysis algorithms depending on the food category during analysis. For example, it can apply different analysis algorithms to vegetables and fruits. It can also apply different analysis algorithms to meats and seafood. Furthermore, it can apply different analysis algorithms to dairy products and grains. By applying different analysis algorithms depending on the food category, a more appropriate analysis can be performed. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input food category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0036] The analysis unit can determine the priority of analysis based on the timing of ingredient provision during the analysis. For example, the analysis unit can prioritize the analysis of ingredients that are nearing their expiration date. It can also prioritize the analysis of seasonal ingredients. Furthermore, it can prioritize the analysis of ingredients that users frequently use. By determining the priority of analysis based on the timing of ingredient provision, more important ingredients can be analyzed preferentially. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input ingredient provision timing data into a generating AI and have the generating AI perform the determination of analysis priorities.

[0037] The analysis unit can adjust the order of analysis based on the relationships between ingredients during the analysis. For example, the analysis unit can analyze ingredients of the same category together. The analysis unit can also adjust the order of analysis considering the balance of nutrients. Furthermore, the analysis unit can adjust the order of analysis according to the user's preferences. This allows for more efficient analysis by adjusting the order of analysis based on the relationships between ingredients. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input ingredient relationship data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0038] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the meal menu items. For example, it can provide detailed suggestions for main meal menu items and concise suggestions for secondary menu items. It can also provide detailed suggestions for highly nutritious menu items and concise suggestions for less nutritious menu items. Furthermore, it can provide detailed suggestions for highly important menu items according to the user's preferences. This allows for more detailed suggestions for more important menu items by adjusting the level of detail based on the importance of the meal menu items. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input meal menu importance data into a generating AI and have the generating AI adjust the level of detail in the suggestions.

[0039] The suggestion unit can apply different suggestion algorithms depending on the category of the meal menu when making suggestions. For example, the suggestion unit can apply different suggestion algorithms for breakfast and lunch. It can also apply different suggestion algorithms for dinner and dessert. Furthermore, it can apply different suggestion algorithms for snacks and drinks. By applying different suggestion algorithms depending on the category of the meal menu, it is possible to make more appropriate suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input meal menu category data into a generating AI and have the generating AI execute the application of different suggestion algorithms.

[0040] The suggestion unit can determine the priority of suggestions based on the timing of meal menu delivery. For example, the suggestion unit can prioritize suggesting menus that use ingredients that need to be consumed immediately. It can also prioritize suggesting menus that use seasonal ingredients. Furthermore, it can prioritize suggesting menus that use ingredients that users frequently consume. By prioritizing suggestions based on the timing of meal menu delivery, more important menus can be suggested preferentially. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input meal menu delivery timing data into a generating AI and have the generating AI perform the determination of suggestion priorities.

[0041] The suggestion unit can adjust the order of suggestions based on the relationships between meal menus. For example, it can suggest menus from the same category together. It can also adjust the order of suggestions considering the balance of nutrients. Furthermore, it can adjust the order of suggestions according to the user's preferences. This allows for more efficient suggestions by adjusting the order of suggestions based on the relationships between meal menus. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input meal menu relationship data into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0042] The data acquisition unit can analyze the user's past consumption behavior and select the optimal data acquisition method. For example, the data acquisition unit can prioritize identifying ingredients that the user has frequently consumed in the past. It can also identify ingredients consumed at specific times of day based on the user's past consumption behavior. Furthermore, the data acquisition unit can analyze the user's past consumption behavior and select the most efficient data acquisition method. In this way, the optimal data acquisition method can be selected by analyzing the user's past consumption behavior. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's past consumption behavior data into a generating AI and have the generating AI select the optimal data acquisition method.

[0043] The information gathering unit can customize its methods of gathering information based on the user's current living situation. For example, if the user is busy, the information gathering unit can provide a concise method of gathering information. If the user is relaxed, the information gathering unit can provide a detailed method of gathering information. Furthermore, if the user is stressed, the information gathering unit can provide a visually easy-to-understand method of gathering information. This allows for more appropriate gathering by customizing the methods of gathering information based on the user's current living situation. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input the user's living situation data into a generating AI and have the generating AI perform the customization of the methods of gathering information.

[0044] The information gathering unit can select an appropriate gathering method based on the user's geographical location information when gathering information. For example, if the user lives in a specific area, the information gathering unit can prioritize gathering information on ingredients available in that area. If the user is traveling, the information gathering unit can also prioritize gathering information on ingredients available at the travel destination. Furthermore, if the user is shopping at a specific store, the information gathering unit can also prioritize gathering information on ingredients available at that store. In this way, the optimal gathering method can be selected by considering the user's geographical location information. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input the user's geographical location information data into a generating AI and have the generating AI select an appropriate gathering method.

[0045] The tracking unit can analyze the user's social media activity and propose tracking methods during the tracking process. For example, the tracking unit can prioritize tracking ingredients shared by the user on social media. It can also prioritize tracking ingredients from cooking accounts followed by the user on social media. Furthermore, it can prioritize tracking ingredients that the user "liked" on social media. By analyzing the user's social media activity, it can propose more appropriate tracking methods. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's social media activity data into a generating AI and have the generating AI propose tracking methods.

[0046] The recommendation system can analyze the user's past consumption behavior to select the optimal recommendation method. For example, the recommendation system can prioritize recommending ingredients that the user has frequently consumed in the past. It can also recommend ingredients that the user consumes at specific times of day based on their past consumption behavior. Furthermore, the recommendation system can analyze the user's past consumption behavior to select the most efficient recommendation method. In this way, the optimal recommendation method can be selected by analyzing the user's past consumption behavior. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input the user's past consumption behavior data into a generating AI and have the generating AI select the optimal recommendation method.

[0047] The recommendation system can customize its recommendation methods based on the user's current lifestyle. For example, if the user is busy, the recommendation system can provide a concise recommendation method. If the user is relaxed, it can provide a detailed recommendation method. Furthermore, if the user is stressed, it can provide a visually easy-to-understand recommendation method. By customizing the recommendation methods based on the user's current lifestyle, the system can provide more appropriate recommendations. Some or all of the above processing in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system can input the user's lifestyle data into a generating AI and have the generating AI perform the customization of the recommendation methods.

[0048] The recommendation system can select the optimal recommendation method by considering the user's geographical location. For example, if the user lives in a specific region, the recommendation system can prioritize recommending ingredients available in that region. Similarly, if the user is traveling, the recommendation system can prioritize recommending ingredients available at their travel destination. Furthermore, if the user shops at a specific store, the recommendation system can prioritize recommending ingredients available at that store. This allows the system to select the optimal recommendation method by considering the user's geographical location. Some or all of the above processing in the recommendation system may be performed using AI, or without AI. For example, the recommendation system can input the user's geographical location data into a generating AI and have the generating AI select the optimal recommendation method.

[0049] The recommendation system can analyze the user's social media activity and suggest recommendation methods when making recommendations. For example, the recommendation system can prioritize recommending ingredients that the user has shared on social media. It can also prioritize recommending ingredients from cooking accounts that the user follows on social media. Furthermore, it can prioritize recommending ingredients that the user has "liked" on social media. By analyzing the user's social media activity, it can suggest more appropriate recommendation methods. Some or all of the above processing in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system can input the user's social media activity data into a generating AI and have the generating AI suggest recommendation methods.

[0050] The generation unit can analyze the user's past purchase history during generation to generate an optimal list. For example, the generation unit can prioritize including ingredients that the user has frequently purchased in the past in the list. It can also include ingredients that the user purchases at specific times of day based on their past purchase history. Furthermore, the generation unit can analyze the user's past purchase history to generate the most efficient list. In this way, an optimal list can be generated by analyzing the user's past purchase history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past purchase history data into a generation AI and have the generation AI perform the generation of an optimal list.

[0051] The generation unit can customize the list content based on the user's current lifestyle during generation. For example, if the user is busy, the generation unit can generate a concise list. If the user is relaxed, the generation unit can generate a detailed list. Furthermore, if the user is stressed, the generation unit can generate a visually easy-to-understand list. This allows for the generation of a more appropriate list by customizing the list content based on the user's current lifestyle. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's lifestyle data into a generation AI and have the generation AI customize the list content.

[0052] The generation unit can generate an optimal list by considering the user's geographical location information during generation. For example, if the user lives in a specific region, the generation unit can include ingredients available in that region in the list. It can also include ingredients available at the user's travel destination if the user is traveling. Furthermore, if the user shops at a specific store, the generation unit can include ingredients available at that store in the list. This allows for the generation of an optimal list by considering the user's geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location data into a generation AI and have the generation AI generate the optimal list.

[0053] The generation unit can analyze the user's social media activity during generation to suggest list contents. For example, the generation unit can include ingredients that the user has shared on social media. It can also include ingredients from cooking accounts that the user follows on social media. Furthermore, it can include ingredients that the user has "liked" on social media. This allows the generation unit to suggest more appropriate list contents by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI suggest list contents.

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

[0055] The reception desk can adjust the timing of photo submissions, taking into account the expiration dates of the user's ingredients. For example, if there are many ingredients nearing their expiration date, the reception desk can promptly encourage the submission of photos. Conversely, if there are many ingredients far from their expiration date, the reception desk can encourage regular photo submissions. Furthermore, the reception desk can notify users of a list of ingredients nearing their expiration date and emphasize the importance of providing photos. This allows users to manage their ingredients while being mindful of expiration dates.

[0056] The analysis unit can determine the priority of analysis by considering the user's frequency of using ingredients. For example, it can perform a detailed analysis on frequently used ingredients and a concise analysis on less frequently used ingredients. The analysis unit can also analyze the user's past usage history and prioritize the analysis of frequently used ingredients. Furthermore, the analysis unit can prioritize the analysis of specific ingredients according to the user's preferences. This allows for more efficient analysis by prioritizing analysis based on the user's usage frequency.

[0057] The suggestion function can customize the menu suggestions based on the user's dietary preferences and allergy information. For example, if a user is allergic to a specific ingredient, the system can suggest a menu that does not contain that ingredient. Similarly, if a user likes a particular ingredient, the system can suggest a menu that contains a high amount of that ingredient. Furthermore, the suggestion function can analyze the user's past eating history and suggest menus that match their preferences. This enables the system to provide menu suggestions tailored to the individual needs of each user.

[0058] The tracking unit can identify ingredients that need replenishment, taking into account the user's food storage methods. For example, it can prioritize tracking the inventory of ingredients that need to be refrigerated in the refrigerator. It can also track the inventory of ingredients that can be stored at room temperature in the pantry. Furthermore, based on the user's storage methods, the tracking unit can consider the appropriate storage period and determine when replenishment is necessary. This allows users to manage their food inventory appropriately according to their storage methods.

[0059] The recommendation system can suggest foods that supplement the user's necessary nutrients, taking into account the nutritional value of the food items the user is looking for. For example, if the user wants to consume more vitamin C, the system can recommend foods that are rich in vitamin C. Similarly, if the user wants to consume more protein, the system can recommend foods that are rich in protein. Furthermore, the recommendation system can also recommend balanced foods based on the user's health condition and nutritional needs. This allows users to select appropriate foods to maintain a healthy diet.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The reception desk receives photos of the inside of the refrigerator from the user. The photos provided by the user include a picture of the entire inside of the refrigerator. The reception desk can accept photos in JPEG or PNG format and can also convert them to a format suitable for analysis, taking into account the photo's resolution. Step 2: The analysis unit analyzes the photos received by the reception unit and recognizes the food items. The analysis unit uses image analysis technology to identify each food item in the refrigerator and recognizes food items such as vegetables, fruits, meats, and dairy products. Image recognition algorithms can be used to improve the accuracy of food item identification and recognition. Step 3: The suggestion unit proposes meal menus based on the ingredients recognized by the analysis unit. The suggestion unit combines this with a recipe database to create healthy and balanced meal menus based on available ingredients. For example, it may suggest recipes for salads and soups using vegetables and meat found in the refrigerator. It can propose optimal menus considering nutritional balance and user preferences. Step 4: The tracking unit identifies any missing items or ingredients that need to be restocked based on the menu proposed by the suggestion unit. The tracking unit can identify ingredients that need to be restocked by considering the inventory levels and usage frequency of ingredients in the refrigerator. Step 5: The recommendation team recommends necessary food items and daily necessities based on the information gathered by the information gathering team. The recommendation team considers the user's eating habits and nutritional needs and recommends food items to supplement necessary nutrients. For example, if there is a vitamin C deficiency, they would recommend foods such as oranges and broccoli. Step 6: The generation unit generates purchasing methods and shopping lists for groceries and daily necessities recommended by the recommendation unit. The generation unit can generate purchase links to online shopping sites or shopping lists for nearby supermarkets. For example, the generation unit suggests the optimal purchasing method so that the user can efficiently purchase the necessary groceries.

[0062] (Example of form 2) The meal suggestion system according to an embodiment of the present invention is a system in which a user provides a photograph of the inside of their refrigerator, and an AI analyzes the user's consumption behavior, eating patterns, lifestyle, and nutritional needs, and creates a healthy and balanced meal menu based on available ingredients. In this system, the user provides a photograph of the inside of their refrigerator, the AI ​​analyzes the photograph and recognizes the ingredients. Based on the recognized ingredients, the AI ​​suggests a meal menu. Furthermore, the AI ​​analyzes the contents of the refrigerator to identify missing items and ingredients that need to be restocked. It also analyzes the balance of nutrients and recommends necessary ingredients and daily necessities. Finally, the AI ​​generates and provides the user with the optimal purchasing method and shopping list. For example, the user provides a photograph of the inside of their refrigerator. In this case, the user only needs to take a photograph of the entire inside of the refrigerator. For example, the user opens the refrigerator door and takes a photograph of the inside. This information is input to the AI. Next, the AI ​​analyzes the input photograph and recognizes the ingredients. The AI ​​uses image analysis technology to identify each ingredient in the refrigerator. For example, it recognizes ingredients such as vegetables, fruits, meats, and dairy products. This creates a list of ingredients in the refrigerator. Based on the recognized ingredients, the AI ​​suggests a meal menu. The AI, combined with a recipe database, creates healthy and balanced meal plans based on available ingredients. For example, it suggests recipes for salads and soups using vegetables and meat already in the refrigerator. Furthermore, the AI ​​analyzes the contents of the refrigerator to identify missing items and ingredients that need replenishing. For instance, if the amount of milk in the refrigerator is low, it will recognize that it needs to be refilled. It also analyzes nutritional balance and recommends necessary ingredients and household items. The AI ​​considers the user's eating habits and nutritional needs to recommend ingredients that supplement necessary nutrients. For example, if there is a vitamin C deficiency, it will recommend ingredients such as oranges and broccoli. Finally, the AI ​​generates and provides the user with the optimal purchasing method and shopping list. For example, it can generate purchase links to online shopping sites or shopping lists for nearby supermarkets. This allows the user to efficiently purchase the necessary ingredients.This allows the meal suggestion system to analyze photos of the user's refrigerator, recognize ingredients, suggest meal menus, identify missing items and ingredients that need replenishing, recommend necessary ingredients and daily necessities, and generate purchasing instructions and shopping lists.

[0063] The meal suggestion system according to this embodiment comprises a reception unit, an analysis unit, a suggestion unit, a recognition unit, a recommendation unit, and a generation unit. The reception unit receives a photograph of the inside of a refrigerator from the user. The photograph provided by the user may include, for example, a photograph of the entire inside of the refrigerator, but is not limited to such an example. The reception unit can accept photographs in, for example, JPEG or PNG format. The reception unit can also convert the photograph to a format suitable for analysis, taking into account the resolution of the photograph. The analysis unit analyzes the photograph received by the reception unit and recognizes the ingredients. The analysis unit identifies each ingredient in the refrigerator using image analysis technology. For example, it recognizes ingredients such as vegetables, fruits, meats, and dairy products. The analysis unit can improve the accuracy of ingredient recognition and the type of ingredients it can identify using an image recognition algorithm. The suggestion unit proposes a meal menu based on the ingredients recognized by the analysis unit. The suggestion unit combines this with a recipe database to create a healthy and balanced meal menu based on the available ingredients. For example, it proposes recipes for salads and soups using vegetables and meat found in the refrigerator. The suggestion unit can propose an optimal menu, taking into account nutritional balance and the user's preferences. The information gathering unit identifies missing items and ingredients that need to be replenished based on the menu proposed by the suggestion unit. The information gathering unit can identify ingredients that need to be replenished by considering the inventory levels and frequency of use of ingredients in the refrigerator. The recommendation unit recommends necessary ingredients and daily necessities based on the information gathered by the information gathering unit. The recommendation unit considers the user's eating habits and nutritional needs and recommends ingredients to supplement necessary nutrients. For example, if there is a vitamin C deficiency, it will recommend ingredients such as oranges and broccoli. The generation unit generates purchasing methods and shopping lists for the ingredients and daily necessities recommended by the recommendation unit. The generation unit can generate purchase links to online shopping sites or purchase lists to nearby supermarkets. For example, the generation unit suggests the optimal purchasing method so that the user can efficiently purchase the necessary ingredients. Thus, the meal suggestion system according to the embodiment can analyze a photo of the user's refrigerator, recognize ingredients, propose meal menus, identify missing items and ingredients that need to be replenished, recommend necessary ingredients and daily necessities, and generate purchasing methods and shopping lists.

[0064] The reception desk can estimate the user's emotions and adjust the timing of photo submission based on the estimated emotions. For example, if the user is feeling stressed, the reception desk can encourage them to submit photos during a time when they can relax. The reception desk can also set reminders for users who are busy, allowing them to submit photos quickly. Furthermore, if the user is relaxed, the reception desk can encourage them to submit more detailed photos. By adjusting the timing of photo submission according to the user's emotions, photos can be received at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0065] The reception desk can analyze a user's past photo submission history and select an appropriate submission method. For example, the reception desk can analyze the time periods when a user frequently submitted photos in the past and prompt them to submit photos during those times. The reception desk can also prioritize suggesting photo submission methods (smartphone, tablet, etc.) that the user has used in the past. Furthermore, the reception desk can prompt submissions on specific days of the week or time periods based on the user's past photo submission history. In this way, the optimal submission method can be selected by analyzing the user's past photo submission history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past photo submission history data into a generating AI and have the generating AI select the optimal submission method.

[0066] The reception unit can filter photos based on the user's current eating habits and areas of interest when they are received. For example, if the user is health-conscious, the reception unit can prioritize receiving photos related to healthy foods. It can also prioritize receiving photos of foods related to a specific diet if the user is following one. Furthermore, if the user has an allergy to a particular food, the reception unit can prioritize receiving photos that do not contain that food. This allows for the reception of more relevant photos by filtering based on the user's current eating habits and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input data on the user's eating habits and areas of interest into a generating AI and have the generating AI perform the filtering.

[0067] The reception unit can estimate the user's emotions and determine the priority of photos to accept based on the estimated emotions. For example, if the user is stressed, the reception unit can prioritize accepting photos that can be easily taken. If the user is relaxed, the reception unit can prioritize accepting detailed photos. Furthermore, if the user is in a hurry, the reception unit can prioritize accepting photos that can be taken quickly. In this way, by prioritizing photos according to the user's emotions, more appropriate photos can be accepted. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, or not using AI. For example, the reception unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0068] The reception unit can prioritize receiving photos that are highly relevant based on the user's geographical location information. For example, if the user lives in a specific region, the reception unit can prioritize receiving photos of ingredients available in that region. If the user is traveling, the reception unit can also prioritize receiving photos of ingredients available at their travel destination. Furthermore, if the user shops at a specific store, the reception unit can prioritize receiving photos of ingredients available at that store. This allows for the prioritization of highly relevant photos by considering the user's geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI select highly relevant photos.

[0069] The reception unit can analyze the user's social media activity when receiving photos and accept relevant photos. For example, the reception unit can prioritize accepting photos of ingredients that the user has shared on social media. It can also prioritize accepting photos of cooking accounts that the user follows on social media. Furthermore, it can prioritize accepting photos of ingredients that the user has "liked" on social media. In this way, by analyzing the user's social media activity, it is possible to prioritize accepting relevant photos. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI select relevant photos.

[0070] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results. Furthermore, if the user is stressed, the analysis unit can provide visually easy-to-understand analysis results. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0071] The analysis unit can adjust the level of detail of the analysis based on the importance of the ingredients during the analysis. For example, the analysis unit can perform a detailed analysis for major ingredients and a simplified analysis for secondary ingredients. It can also perform a detailed analysis for ingredients with high nutritional value and a simplified analysis for ingredients with low nutritional value. Furthermore, the analysis unit can perform a detailed analysis for ingredients of high importance according to the user's preference. This allows for more detailed analysis of important ingredients by adjusting the level of detail of the analysis based on the importance of the ingredients. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input ingredient importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0072] The analysis unit can apply different analysis algorithms depending on the food category during analysis. For example, it can apply different analysis algorithms to vegetables and fruits. It can also apply different analysis algorithms to meats and seafood. Furthermore, it can apply different analysis algorithms to dairy products and grains. By applying different analysis algorithms depending on the food category, a more appropriate analysis can be performed. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input food category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0073] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can perform a short, concise analysis. If the user is relaxed, the analysis unit can perform a detailed analysis. Furthermore, if the user is stressed, the analysis unit can perform a visually easy-to-understand analysis. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0074] The analysis unit can determine the priority of analysis based on the timing of ingredient provision during the analysis. For example, the analysis unit can prioritize the analysis of ingredients that are nearing their expiration date. It can also prioritize the analysis of seasonal ingredients. Furthermore, it can prioritize the analysis of ingredients that users frequently use. By determining the priority of analysis based on the timing of ingredient provision, more important ingredients can be analyzed preferentially. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input ingredient provision timing data into a generating AI and have the generating AI perform the determination of analysis priorities.

[0075] The analysis unit can adjust the order of analysis based on the relationships between ingredients during the analysis. For example, the analysis unit can analyze ingredients of the same category together. The analysis unit can also adjust the order of analysis considering the balance of nutrients. Furthermore, the analysis unit can adjust the order of analysis according to the user's preferences. This allows for more efficient analysis by adjusting the order of analysis based on the relationships between ingredients. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input ingredient relationship data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0076] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, it can provide concise suggestions. Furthermore, if the user is stressed, it can provide visually clear suggestions. By adjusting the way suggestions are presented according to the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0077] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the meal menu items. For example, it can provide detailed suggestions for main meal menu items and concise suggestions for secondary menu items. It can also provide detailed suggestions for highly nutritious menu items and concise suggestions for less nutritious menu items. Furthermore, it can provide detailed suggestions for highly important menu items according to the user's preferences. This allows for more detailed suggestions for more important menu items by adjusting the level of detail based on the importance of the meal menu items. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input meal menu importance data into a generating AI and have the generating AI adjust the level of detail in the suggestions.

[0078] The suggestion unit can apply different suggestion algorithms depending on the category of the meal menu when making suggestions. For example, the suggestion unit can apply different suggestion algorithms for breakfast and lunch. It can also apply different suggestion algorithms for dinner and dessert. Furthermore, it can apply different suggestion algorithms for snacks and drinks. By applying different suggestion algorithms depending on the category of the meal menu, it is possible to make more appropriate suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input meal menu category data into a generating AI and have the generating AI execute the application of different suggestion algorithms.

[0079] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide a short, concise suggestion. If the user is relaxed, the suggestion unit can provide a more detailed suggestion. Furthermore, if the user is stressed, the suggestion unit can provide a visually easy-to-understand suggestion. By adjusting the length of the suggestion according to the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0080] The suggestion unit can determine the priority of suggestions based on the timing of meal menu delivery. For example, the suggestion unit can prioritize suggesting menus that use ingredients that need to be consumed immediately. It can also prioritize suggesting menus that use seasonal ingredients. Furthermore, it can prioritize suggesting menus that use ingredients that users frequently consume. By prioritizing suggestions based on the timing of meal menu delivery, more important menus can be suggested preferentially. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input meal menu delivery timing data into a generating AI and have the generating AI perform the determination of suggestion priorities.

[0081] The suggestion unit can adjust the order of suggestions based on the relationships between meal menus. For example, it can suggest menus from the same category together. It can also adjust the order of suggestions considering the balance of nutrients. Furthermore, it can adjust the order of suggestions according to the user's preferences. This allows for more efficient suggestions by adjusting the order of suggestions based on the relationships between meal menus. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input meal menu relationship data into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0082] The sensing unit can estimate the user's emotions and adjust its sensing method based on the estimated emotions. For example, if the user is relaxed, the sensing unit can provide a detailed sensing method. If the user is in a hurry, it can provide a concise sensing method. Furthermore, if the user is stressed, it can provide a visually easy-to-understand sensing method. By adjusting the sensing method according to the user's emotions, a more appropriate sensing can be achieved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sensing unit may be performed using AI, for example, or without AI. For example, the sensing unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0083] The data acquisition unit can analyze the user's past consumption behavior and select the optimal data acquisition method. For example, the data acquisition unit can prioritize identifying ingredients that the user has frequently consumed in the past. It can also identify ingredients consumed at specific times of day based on the user's past consumption behavior. Furthermore, the data acquisition unit can analyze the user's past consumption behavior and select the most efficient data acquisition method. In this way, the optimal data acquisition method can be selected by analyzing the user's past consumption behavior. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's past consumption behavior data into a generating AI and have the generating AI select the optimal data acquisition method.

[0084] The information gathering unit can customize its methods of gathering information based on the user's current living situation. For example, if the user is busy, the information gathering unit can provide a concise method of gathering information. If the user is relaxed, the information gathering unit can provide a detailed method of gathering information. Furthermore, if the user is stressed, the information gathering unit can provide a visually easy-to-understand method of gathering information. This allows for more appropriate gathering by customizing the methods of gathering information based on the user's current living situation. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input the user's living situation data into a generating AI and have the generating AI perform the customization of the methods of gathering information.

[0085] The perception unit can estimate the user's emotions and determine the priority of perception based on the estimated emotions. For example, if the user is relaxed, the perception unit can prioritize detailed perception. If the user is in a hurry, the perception unit can prioritize concise perception. Furthermore, if the user is stressed, the perception unit can prioritize visually easy-to-understand perception. This allows for more appropriate perception by determining the priority of perception according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the perception unit may be performed using AI, or not using AI. For example, the perception unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0086] The information gathering unit can select an appropriate gathering method based on the user's geographical location information when gathering information. For example, if the user lives in a specific area, the information gathering unit can prioritize gathering information on ingredients available in that area. If the user is traveling, the information gathering unit can also prioritize gathering information on ingredients available at the travel destination. Furthermore, if the user is shopping at a specific store, the information gathering unit can also prioritize gathering information on ingredients available at that store. In this way, the optimal gathering method can be selected by considering the user's geographical location information. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input the user's geographical location information data into a generating AI and have the generating AI select an appropriate gathering method.

[0087] The tracking unit can analyze the user's social media activity and propose tracking methods during the tracking process. For example, the tracking unit can prioritize tracking ingredients shared by the user on social media. It can also prioritize tracking ingredients from cooking accounts followed by the user on social media. Furthermore, it can prioritize tracking ingredients that the user "liked" on social media. By analyzing the user's social media activity, it can propose more appropriate tracking methods. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's social media activity data into a generating AI and have the generating AI propose tracking methods.

[0088] The recommendation system can estimate the user's emotions and adjust its recommendation method based on those emotions. For example, if the user is relaxed, the recommendation system can provide detailed recommendations. If the user is in a hurry, it can provide concise recommendations. Furthermore, if the user is stressed, it can provide visually easy-to-understand recommendations. By adjusting the recommendation method according to the user's emotions, more appropriate recommendations can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation system may be performed using AI or not. For example, the recommendation system can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0089] The recommendation system can analyze the user's past consumption behavior to select the optimal recommendation method. For example, the recommendation system can prioritize recommending ingredients that the user has frequently consumed in the past. It can also recommend ingredients that the user consumes at specific times of day based on their past consumption behavior. Furthermore, the recommendation system can analyze the user's past consumption behavior to select the most efficient recommendation method. In this way, the optimal recommendation method can be selected by analyzing the user's past consumption behavior. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input the user's past consumption behavior data into a generating AI and have the generating AI select the optimal recommendation method.

[0090] The recommendation system can customize its recommendation methods based on the user's current lifestyle. For example, if the user is busy, the recommendation system can provide a concise recommendation method. If the user is relaxed, it can provide a detailed recommendation method. Furthermore, if the user is stressed, it can provide a visually easy-to-understand recommendation method. By customizing the recommendation methods based on the user's current lifestyle, the system can provide more appropriate recommendations. Some or all of the above processing in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system can input the user's lifestyle data into a generating AI and have the generating AI perform the customization of the recommendation methods.

[0091] The recommendation system can estimate the user's emotions and determine recommendation priorities based on those emotions. For example, if the user is relaxed, the recommendation system can prioritize detailed recommendations. If the user is in a hurry, it can prioritize concise recommendations. Furthermore, if the user is stressed, it can prioritize visually easy-to-understand recommendations. This allows for more appropriate recommendations by prioritizing recommendations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation system may be performed using AI or not. For example, the recommendation system can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0092] The recommendation system can select the optimal recommendation method by considering the user's geographical location. For example, if the user lives in a specific region, the recommendation system can prioritize recommending ingredients available in that region. Similarly, if the user is traveling, the recommendation system can prioritize recommending ingredients available at their travel destination. Furthermore, if the user shops at a specific store, the recommendation system can prioritize recommending ingredients available at that store. This allows the system to select the optimal recommendation method by considering the user's geographical location. Some or all of the above processing in the recommendation system may be performed using AI, or without AI. For example, the recommendation system can input the user's geographical location data into a generating AI and have the generating AI select the optimal recommendation method.

[0093] The recommendation system can analyze the user's social media activity and suggest recommendation methods when making recommendations. For example, the recommendation system can prioritize recommending ingredients that the user has shared on social media. It can also prioritize recommending ingredients from cooking accounts that the user follows on social media. Furthermore, it can prioritize recommending ingredients that the user has "liked" on social media. By analyzing the user's social media activity, it can suggest more appropriate recommendation methods. Some or all of the above processing in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system can input the user's social media activity data into a generating AI and have the generating AI suggest recommendation methods.

[0094] The generation unit can estimate the user's emotions and adjust the content of the generated list based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a detailed list. If the user is in a hurry, the generation unit can generate a concise list. Furthermore, if the user is stressed, the generation unit can generate a visually easy-to-understand list. By adjusting the list content according to the user's emotions, a more appropriate list can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0095] The generation unit can analyze the user's past purchase history during generation to generate an optimal list. For example, the generation unit can prioritize including ingredients that the user has frequently purchased in the past in the list. It can also include ingredients that the user purchases at specific times of day based on their past purchase history. Furthermore, the generation unit can analyze the user's past purchase history to generate the most efficient list. In this way, an optimal list can be generated by analyzing the user's past purchase history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past purchase history data into a generation AI and have the generation AI perform the generation of an optimal list.

[0096] The generation unit can customize the list content based on the user's current lifestyle during generation. For example, if the user is busy, the generation unit can generate a concise list. If the user is relaxed, the generation unit can generate a detailed list. Furthermore, if the user is stressed, the generation unit can generate a visually easy-to-understand list. This allows for the generation of a more appropriate list by customizing the list content based on the user's current lifestyle. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's lifestyle data into a generation AI and have the generation AI customize the list content.

[0097] The generation unit can estimate the user's emotions and determine the priority of the lists to be generated based on the estimated emotions. For example, if the user is relaxed, the generation unit can prioritize generating a detailed list. If the user is in a hurry, the generation unit can prioritize generating a concise list. Furthermore, if the user is stressed, the generation unit can prioritize generating a visually easy-to-understand list. In this way, by prioritizing lists according to the user's emotions, a more appropriate list can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0098] The generation unit can generate an optimal list by considering the user's geographical location information during generation. For example, if the user lives in a specific region, the generation unit can include ingredients available in that region in the list. It can also include ingredients available at the user's travel destination if the user is traveling. Furthermore, if the user shops at a specific store, the generation unit can include ingredients available at that store in the list. This allows for the generation of an optimal list by considering the user's geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location data into a generation AI and have the generation AI generate the optimal list.

[0099] The generation unit can analyze the user's social media activity during generation to suggest list contents. For example, the generation unit can include ingredients that the user has shared on social media. It can also include ingredients from cooking accounts that the user follows on social media. Furthermore, it can include ingredients that the user has "liked" on social media. This allows the generation unit to suggest more appropriate list contents by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI suggest list contents. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, understanding unit, recommendation unit, and generation unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives a photo of the inside of the refrigerator from the user. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the received photo to recognize the ingredients. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes a meal menu based on the recognized ingredients. The understanding unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies missing items and ingredients that need to be restocked. The recommendation unit is implemented by the identification processing unit 290 of the data processing unit 12 and recommends necessary ingredients and daily necessities. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates a purchase method and a shopping list. The reception unit can estimate the user's emotions and adjust the timing of photo reception based on the estimated user emotions. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, understanding unit, recommendation unit, and generation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives a photo of the inside of the refrigerator from the user. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the received photo to recognize the ingredients. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes a meal menu based on the recognized ingredients. The understanding unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies missing items and ingredients that need to be restocked. The recommendation unit is implemented by the identification processing unit 290 of the data processing unit 12 and recommends necessary ingredients and daily necessities. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates a purchase method and a shopping list. The reception unit can estimate the user's emotions and adjust the timing of photo reception based on the estimated user emotions. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, understanding unit, recommendation unit, and generation unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives a photo of the inside of the refrigerator from the user. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the received photo to recognize the ingredients. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes a meal menu based on the recognized ingredients. The understanding unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies missing items and ingredients that need to be restocked. The recommendation unit is implemented by the identification processing unit 290 of the data processing unit 12 and recommends necessary ingredients and daily necessities. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates a purchase method and a shopping list. The reception unit can estimate the user's emotions and adjust the timing of photo reception based on the estimated user emotions. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, identification unit, recommendation unit, and generation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives a photograph of the inside of the refrigerator from the user. The analysis unit is implemented by the identification unit 290 of the data processing unit 12 and analyzes the received photograph to recognize the ingredients. The proposal unit is implemented by the identification unit 290 of the data processing unit 12 and proposes a meal menu based on the recognized ingredients. The identification unit is implemented by the identification unit 290 of the data processing unit 12 and identifies missing items and ingredients that need to be restocked. The recommendation unit is implemented by the identification unit 290 of the data processing unit 12 and recommends necessary ingredients and daily necessities. The generation unit is implemented by the identification unit 290 of the data processing unit 12 and generates a purchase method and a shopping list. The reception unit can estimate the user's emotions and adjust the timing of receiving photographs based on the estimated user emotions.

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

[0101] The reception desk can adjust the timing of photo submissions, taking into account the expiration dates of the user's ingredients. For example, if there are many ingredients nearing their expiration date, the reception desk can promptly encourage the submission of photos. Conversely, if there are many ingredients far from their expiration date, the reception desk can encourage regular photo submissions. Furthermore, the reception desk can notify users of a list of ingredients nearing their expiration date and emphasize the importance of providing photos. This allows users to manage their ingredients while being mindful of expiration dates.

[0102] The analysis unit can determine the priority of analysis by considering the user's frequency of using ingredients. For example, it can perform a detailed analysis on frequently used ingredients and a concise analysis on less frequently used ingredients. The analysis unit can also analyze the user's past usage history and prioritize the analysis of frequently used ingredients. Furthermore, the analysis unit can prioritize the analysis of specific ingredients according to the user's preferences. This allows for more efficient analysis by prioritizing analysis based on the user's usage frequency.

[0103] The suggestion function can customize the menu suggestions based on the user's dietary preferences and allergy information. For example, if a user is allergic to a specific ingredient, the system can suggest a menu that does not contain that ingredient. Similarly, if a user likes a particular ingredient, the system can suggest a menu that contains a high amount of that ingredient. Furthermore, the suggestion function can analyze the user's past eating history and suggest menus that match their preferences. This enables the system to provide menu suggestions tailored to the individual needs of each user.

[0104] The tracking unit can identify ingredients that need replenishment, taking into account the user's food storage methods. For example, it can prioritize tracking the inventory of ingredients that need to be refrigerated in the refrigerator. It can also track the inventory of ingredients that can be stored at room temperature in the pantry. Furthermore, based on the user's storage methods, the tracking unit can consider the appropriate storage period and determine when replenishment is necessary. This allows users to manage their food inventory appropriately according to their storage methods.

[0105] The recommendation system can suggest foods that supplement the user's necessary nutrients, taking into account the nutritional value of the food items the user is looking for. For example, if the user wants to consume more vitamin C, the system can recommend foods that are rich in vitamin C. Similarly, if the user wants to consume more protein, the system can recommend foods that are rich in protein. Furthermore, the recommendation system can also recommend balanced foods based on the user's health condition and nutritional needs. This allows users to select appropriate foods to maintain a healthy diet.

[0106] The reception system can estimate the user's emotions and customize the photo submission process based on those estimates. For example, if the user is stressed, it can suggest an easy way to take photos. If the user is relaxed, it can encourage them to provide more detailed photos. Furthermore, if the user is busy, it can set a reminder to help them submit photos quickly. By customizing the photo submission process according to the user's emotions, photos can be received at a more appropriate time.

[0107] The analysis unit can estimate the user's emotions and adjust the level of detail in the analysis based on the estimated emotions. For example, if the user is relaxed, it can provide detailed analysis results. If the user is in a hurry, it can provide concise analysis results. Furthermore, if the user is stressed, it can provide visually easy-to-understand analysis results. In this way, by adjusting the level of detail in the analysis according to the user's emotions, more appropriate analysis results can be provided.

[0108] The suggestion function can estimate the user's emotions and adjust the types of menus suggested based on those emotions. For example, if the user is relaxed, it can suggest elaborate dishes. If the user is in a hurry, it can suggest easy-to-prepare dishes. Furthermore, if the user is stressed, it can suggest dishes using ingredients that have a relaxing effect. By adjusting the types of menus suggested according to the user's emotions, more appropriate menu suggestions become possible.

[0109] The grasping unit can estimate the user's emotions and determine the priority of ingredients to grasp based on those emotions. For example, if the user is relaxed, detailed grasping can be prioritized. If the user is in a hurry, concise grasping can be prioritized. Furthermore, if the user is stressed, visually easy-to-understand grasping can be prioritized. By determining the priority of ingredients to grasp according to the user's emotions, a more appropriate grasping can be achieved.

[0110] The recommendation system can estimate the user's emotions and adjust the types of ingredients recommended based on those emotions. For example, if the user is relaxed, it can recommend ingredients with relaxing effects. If the user is in a hurry, it can recommend ingredients that are easy to cook. Furthermore, if the user is stressed, it can recommend ingredients that have stress-reducing effects. By adjusting the types of ingredients recommended according to the user's emotions, more appropriate ingredient recommendations become possible.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The reception desk receives photos of the inside of the refrigerator from the user. The photos provided by the user include a picture of the entire inside of the refrigerator. The reception desk can accept photos in JPEG or PNG format and can also convert them to a format suitable for analysis, taking into account the photo's resolution. Step 2: The analysis unit analyzes the photos received by the reception unit and recognizes the food items. The analysis unit uses image analysis technology to identify each food item in the refrigerator and recognizes food items such as vegetables, fruits, meats, and dairy products. Image recognition algorithms can be used to improve the accuracy of food item identification and recognition. Step 3: The suggestion unit proposes meal menus based on the ingredients recognized by the analysis unit. The suggestion unit combines this with a recipe database to create healthy and balanced meal menus based on available ingredients. For example, it may suggest recipes for salads and soups using vegetables and meat found in the refrigerator. It can propose optimal menus considering nutritional balance and user preferences. Step 4: The tracking unit identifies any missing items or ingredients that need to be restocked based on the menu proposed by the suggestion unit. The tracking unit can identify ingredients that need to be restocked by considering the inventory levels and usage frequency of ingredients in the refrigerator. Step 5: The recommendation team recommends necessary food items and daily necessities based on the information gathered by the information gathering team. The recommendation team considers the user's eating habits and nutritional needs and recommends food items to supplement necessary nutrients. For example, if there is a vitamin C deficiency, they would recommend foods such as oranges and broccoli. Step 6: The generation unit generates purchasing methods and shopping lists for groceries and daily necessities recommended by the recommendation unit. The generation unit can generate purchase links to online shopping sites or shopping lists for nearby supermarkets. For example, the generation unit suggests the optimal purchasing method so that the user can efficiently purchase the necessary groceries.

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

[0114] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0115] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

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

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

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

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

[0136] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

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

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0143] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

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

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

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

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

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0157] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

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

[0163] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0166] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0168] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0169] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0170] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0174] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

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

[0177] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0178] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0179] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0181] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0182] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0183] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0184] [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives photos of the inside of the refrigerator from users, An analysis unit analyzes the photograph received by the reception unit and recognizes the ingredients, A proposal unit that proposes a meal menu based on the ingredients recognized by the analysis unit, A grasping unit that identifies missing items or ingredients that need to be replenished based on the menu proposed by the aforementioned proposal unit, A recommendation unit recommends necessary food ingredients or daily necessities based on the information gathered by the aforementioned information gathering unit, The system includes a generation unit that generates purchasing methods and shopping lists for food ingredients and daily necessities recommended by the recommendation unit. A system characterized by the following features.

2. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of photo submissions based on those emotions. The system according to feature 1.

3. The aforementioned reception unit is Analyze the user's past photo submission history and select the appropriate submission method. The system according to feature 1.

4. The aforementioned reception unit is When receiving photos, the system filters them based on the user's current eating habits and areas of interest. The system according to feature 1.

5. The aforementioned reception unit is The system estimates the user's emotions and prioritizes the photos to be accepted based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is When receiving photos, the system prioritizes accepting photos that are highly relevant based on the user's geographical location. The system according to feature 1.

7. The aforementioned reception unit is When receiving photos, the system analyzes the user's social media activity and accepts relevant photos. The system according to feature 1.

8. The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system according to feature 1.

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