system

Abstract

The system according to this embodiment aims to provide safe meals by suggesting food shapes and hardness suitable for infants and toddlers. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, and a support unit. The reception unit receives information about ingredients. The analysis unit analyzes the information received by the reception unit and proposes the shape and hardness of ingredients suitable for infants. The support unit assists with cooking based on the cooking method proposed by the analysis 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, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to the description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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, knowledge and confirmation work for ensuring the safety of infants' diets have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to propose the shape and hardness of foods suitable for infants and provide a safe diet.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a support unit. The reception unit receives information on food ingredients. The analysis unit analyzes the information received by the reception unit and proposes the shape and hardness of food ingredients suitable for infants. The support unit supports the cooking work based on the cooking method proposed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can propose food shapes and hardness suitable for infants and young children, and can provide safe meals. [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 interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

[0016] [First Embodiment] 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 system for supporting infant meal management according to an embodiment of the present invention is a system that uses an AI agent to support infant meal management. This system addresses the fact that infants cannot eat the same food as adults because their esophagi are narrow or their teeth have not yet fully grown, and therefore food needs to be cut into small pieces or softened. Tragic accidents of choking and death continue to occur even in modern times, but these accidents can be prevented with appropriate knowledge and verification procedures. Therefore, we propose a system that allows anyone to safely prepare meals for infants, regardless of the environment, by using an AI agent that excels at knowledge and verification procedures. First, the user inputs information about the ingredients. For example, they input information such as the type, shape, and hardness of the ingredients. This information is input to the AI ​​agent. Next, the AI ​​agent analyzes the input information and proposes the appropriate shape and hardness of the ingredients for infants. For example, it proposes methods for cutting ingredients into small pieces or cooking methods to soften them. This allows infants to eat safely. Furthermore, the AI ​​agent supports the actual cooking work based on the proposed cooking methods. For example, it provides instructions on the procedure for cutting ingredients to the appropriate size and the heating time for softening them. This allows users to easily prepare meals suitable for infants and toddlers. This system streamlines infant feeding management and helps prevent choking accidents. For example, the AI ​​agent can appropriately adjust the shape and hardness of ingredients, allowing infants and toddlers to eat safely. Furthermore, users can easily prepare meals suitable for infants and toddlers simply by following the instructions of the AI ​​agent. In this way, the system that supports infant feeding management can safely manage infants and toddlers' meals and prevent choking accidents.

[0029] The system for supporting the management of infant meals according to this embodiment comprises a reception unit, an analysis unit, and a support unit. The reception unit receives information about ingredients. This information includes, but is not limited to, the type, shape, and hardness of the ingredients. The reception unit receives, for example, information about ingredients entered by the user. The analysis unit analyzes the information received by the reception unit and proposes an appropriate shape and hardness for the ingredients for infants. The analysis unit analyzes, for example, the type, shape, and hardness of the ingredients and proposes an appropriate shape and hardness for infants. The analysis unit proposes an appropriate shape and hardness for infants based on the size and hardness of the ingredients. The support unit assists with cooking based on the cooking method proposed by the analysis unit. The support unit provides, for example, instructions on how to cut the ingredients to the appropriate size. The support unit provides, for example, instructions on how to heat the ingredients to soften them. The support unit provides, for example, instructions on how to operate cooking utensils. This system, which supports the management of infants' and toddlers' meals, makes the management of infants' and toddlers' meals more efficient and safer by suggesting suitable shapes and hardnesses for ingredients and assisting with cooking. Some or all of the above processes in the reception, analysis, and support departments may be performed using AI, for example, or without AI. For example, the reception department can input ingredient information entered by the user into the AI, which can then analyze the ingredient information. The analysis department can suggest suitable shapes and hardnesses for ingredients based on the results of the AI's analysis. The support department can assist with cooking based on the cooking method suggested by the AI.

[0030] The reception unit receives information about ingredients. This information includes, but is not limited to, the type, shape, and hardness of the ingredients. The reception unit also receives information about ingredients entered by users. Specifically, users can enter information about ingredients using devices such as smartphones or tablets. The entered information is sent to a cloud server and stored in a database. The reception unit receives this information in real time and sends it to the analysis unit. Furthermore, the reception unit can also automatically read information about ingredients using barcode scanners or QR code (registered trademark) readers. For example, by scanning a barcode printed on the ingredient package, the system can automatically obtain information such as the type of ingredient and nutritional content. This allows users to enter information about ingredients without effort, improving the convenience of the system. The reception unit also has a voice input function, allowing users to enter information about ingredients by voice. For example, if a user voice-inputs "carrot, julienned, normal hardness," the system recognizes the information and sends it to the analysis unit. This allows users to enter information about ingredients without using their hands, making cooking easier. By offering these diverse input methods, the reception desk can enhance user convenience and receive information about ingredients accurately and quickly.

[0031] The analysis unit analyzes the information received by the reception unit and proposes suitable food shapes and hardnesses for infants and toddlers. For example, the analysis unit analyzes the type, shape, and hardness of food and proposes shapes and hardnesses suitable for infants and toddlers. Specifically, the analysis unit uses AI to analyze food information. Based on past data and expert knowledge, the AI ​​learns shapes and hardnesses that infants and toddlers can eat safely. For example, based on the size and hardness of the food, the AI ​​proposes shapes and hardnesses that are easy for infants and toddlers to chew and that are less likely to cause choking. Furthermore, the AI ​​can propose appropriate food shapes and hardnesses according to the age and developmental stage of the infant or toddler. For example, it proposes soft, small, bite-sized foods for a 6-month-old infant and slightly harder foods that are easy to hold for a 12-month-old infant. The analysis unit provides these suggestions to users to support the management of infants' and toddlers' diets. In addition, the analysis unit can also analyze the nutritional components and allergy information of food and propose foods suitable for infants and toddlers. For example, if an infant has allergies, the AI ​​takes that information into account and suggests foods that will not trigger allergies. This improves the safety of infants and makes dietary management more efficient. Through these functions, the analysis unit suggests the appropriate shape and hardness of foods for infants and supports the user's dietary management.

[0032] The support unit assists with cooking based on the cooking methods proposed by the analysis unit. For example, the support unit provides instructions on how to cut ingredients to the appropriate size. Specifically, the support unit provides detailed instructions to the user on how to cut the ingredients. For example, when julienning carrots, the support unit shows the procedure of cutting the carrots to a certain length and then julienning them along that length. Furthermore, the support unit provides instructions on the heating time required to soften the ingredients. For example, to soften carrots, it provides specific instructions on the heating time in the microwave or the boiling time in a pot. By providing these instructions to the user, the support unit can streamline the cooking process and provide ingredients suitable for infants and toddlers. The support unit also provides instructions on how to operate cooking utensils. For example, when finely chopping ingredients using a food processor, the support unit provides detailed instructions on how to use the food processor and how to operate it safely. This allows the user to use the cooking utensils correctly and cook the ingredients properly. Furthermore, the support unit also provides instructions on precautions and safety measures during cooking. For example, when handling hot ingredients, the system provides instructions to prevent burns and offers safety measures for handling cooking utensils. This allows users to perform cooking tasks safely. Through these functions, the support unit can assist users in their cooking tasks and provide ingredients suitable for infants and toddlers.

[0033] The support unit can provide instructions on how to cut ingredients to the appropriate size. For example, the support unit can present the user with instructions on how to cut ingredients to the appropriate size. For example, the support unit can provide instructions on how to cut ingredients into small pieces. For example, the support unit can provide instructions on how to finely chop ingredients. For example, the support unit can provide instructions on how to thinly slice ingredients. By providing instructions on how to cut ingredients to the appropriate size, infants can eat safely. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the instructions for cutting ingredients to the appropriate size into AI, and the AI ​​can analyze the instructions and present them to the user.

[0034] The support unit can instruct the heating time required to soften the food. For example, the support unit can present the heating time required to soften the food to the user. For example, the support unit can instruct the steaming time required to steam the food. For example, the support unit can instruct the boiling time required to boil the food. For example, the support unit can instruct the microwave heating time required to soften the food. This allows infants and young children to eat safely by instructing the heating time required to soften the food. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the heating time required to soften the food into the AI, which can then analyze the heating time and present it to the user.

[0035] The analysis unit can analyze information such as the type, shape, and hardness of food ingredients and propose shapes and hardness suitable for infants. For example, the analysis unit can analyze the type, shape, and hardness of food ingredients and propose shapes and hardness suitable for infants. For example, the analysis unit can propose shapes and hardness that infants can safely eat based on the size and hardness of the food ingredients. For example, the analysis unit can propose appropriate shapes and hardness depending on the type of food ingredient. For example, the analysis unit can measure the hardness of food ingredients and propose hardness suitable for infants. In this way, by analyzing the type, shape, and hardness of food ingredients and proposing shapes and hardness suitable for infants, infants can eat safely. 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 information on the type, shape, and hardness of food ingredients into the AI, which can analyze the information and propose shapes and hardness suitable for infants.

[0036] The reception desk can analyze the user's past ingredient input history and suggest the optimal input method. For example, the reception desk can automatically display ingredient information that the user has frequently entered in the past as a suggestion. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest ingredient information to be used at a specific time of day based on the user's past input history. In this way, by analyzing past input history, the reception desk can suggest the optimal input method to the user. 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 ingredient input history into AI, and the AI ​​can analyze the history and suggest the optimal input method.

[0037] The reception unit can filter ingredient information based on the user's current meal preparation status and areas of interest when the user inputs ingredient information. For example, the reception unit can prioritize displaying ingredient information relevant to the meal the user is currently preparing. For example, the reception unit can suggest relevant ingredient information based on the user's areas of interest (e.g., a specific cuisine genre). For example, the reception unit can suggest new relevant ingredient information based on ingredient information the user has previously searched for. In this way, by filtering ingredient information based on the user's current situation and areas of interest, highly relevant information can be provided. 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 information about the user's current meal preparation status and areas of interest into the AI, which can then analyze the information and filter the relevant ingredient information.

[0038] The reception unit can prioritize inputting highly relevant ingredient information by considering the user's geographical location when inputting ingredient information. For example, the reception unit can prioritize displaying ingredient information available at nearby supermarkets based on the user's current location. For example, the reception unit can suggest region-specific ingredient information based on the user's geographical location. For example, the reception unit can prioritize displaying seasonal ingredient information by considering the user's location. In this way, highly relevant ingredient information can be provided by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into AI, and the AI ​​can analyze the information and prioritize displaying relevant ingredient information.

[0039] The reception unit can analyze the user's social media activity when inputting ingredient information and input relevant ingredient information. For example, the reception unit can suggest relevant ingredient information based on recipes the user has shared on social media. For example, the reception unit can prioritize displaying ingredient information frequently used by the user's social media followers. For example, the reception unit can suggest relevant ingredient information based on posts the user has "liked" on social media. In this way, by analyzing the user's social media activity, it is possible to provide highly relevant ingredient information. 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 data on the user's social media activity into AI, which can then analyze the data and suggest relevant ingredient information.

[0040] The analysis unit can adjust the level of detail of the analysis based on the importance of the ingredients. For example, the analysis unit can perform a detailed analysis for major ingredients and a simplified analysis for secondary ingredients. For example, the analysis unit can perform a detailed analysis for ingredients containing important nutrients based on their nutritional value. For example, the analysis unit can perform a detailed analysis for high-risk ingredients based on their allergy risk. This allows for the priority provision of important information 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 information into AI to adjust the level of detail of the analysis based on the importance of the ingredients, and the AI ​​can analyze the information and adjust the level of detail.

[0041] The analysis unit can apply different analysis algorithms depending on the category of food ingredient during analysis. For example, the analysis unit can apply different analysis algorithms to vegetables and fruits and perform analysis according to their respective characteristics. For example, the analysis unit can apply different analysis algorithms to meats and fish and perform analysis according to their respective cooking methods. For example, the analysis unit can apply different analysis algorithms to grains and dairy products and perform analysis according to their respective nutritional values. By applying different analysis algorithms depending on the category of food ingredient, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, in order to apply different analysis algorithms depending on the category of food ingredient, the analysis unit can input information about the food ingredient into the AI, and the AI ​​can analyze the information and apply an appropriate algorithm.

[0042] The analysis unit can determine the priority of analysis based on when the ingredients were submitted. For example, the analysis unit may prioritize the analysis of recently submitted ingredient information. For example, the analysis unit may determine the priority of analysis based on a deadline specified by the user. For example, the analysis unit may prioritize the analysis of ingredients that should be consumed quickly based on their freshness. By determining the priority of analysis based on when the ingredients were submitted, important information can be provided preferentially. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, in order to determine the priority of analysis based on when the ingredients were submitted, the analysis unit may input ingredient information into AI, and the AI ​​may analyze the information and determine the priority.

[0043] The analysis unit can adjust the order of analysis based on the relationships between ingredients during the analysis process. For example, the analysis unit may group together ingredients used in the same dish. For example, the analysis unit may prioritize the analysis of highly related ingredients based on ingredients the user has used together in the past. For example, the analysis unit may prioritize the analysis of highly related ingredients based on their nutritional value or allergy risk. By adjusting the order of analysis based on the relationships between ingredients, the analysis unit can prioritize the provision of highly relevant information. 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 may input ingredient information into AI to adjust the order of analysis based on the relationships between ingredients, and the AI ​​may analyze the information and adjust the order.

[0044] The support unit can adjust the level of detail of support based on the importance of the cooking task. For example, the support unit can provide detailed support for major cooking tasks and simplified support for secondary cooking tasks. For example, the support unit can provide detailed support for important tasks based on the difficulty of the cooking task. For example, the support unit can provide detailed support for time-consuming tasks based on the time it takes to cook. This allows for priority support for important tasks by adjusting the level of detail of support based on the importance of the cooking task. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input information about the cooking task into the AI ​​to adjust the level of detail of support based on the importance of the cooking task, and the AI ​​can analyze the information and adjust the level of detail.

[0045] The support unit can apply different support algorithms depending on the category of cooking task during the support process. For example, the support unit can apply different support algorithms to vegetable cooking and meat cooking, providing support tailored to the characteristics of each. For example, the support unit can apply different support algorithms to grilling and boiling, providing support tailored to each cooking method. For example, the support unit can apply different support algorithms to dessert cooking and main dish cooking, providing support tailored to each category. By applying different support algorithms depending on the category of cooking task, more appropriate support can be provided. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, in order to apply different support algorithms depending on the category of cooking task, the support unit can input information about the cooking task into the AI, which can then analyze the information and apply the appropriate algorithm.

[0046] The support department can determine the priority of support based on the submission date of the cooking tasks. For example, the support department will prioritize support for cooking tasks submitted most recently. For example, the support department will determine the priority of support based on the deadline specified by the user. For example, the support department will determine the priority based on the importance of the cooking tasks. This allows important tasks to be prioritized by determining the priority of support based on the submission date of the cooking tasks. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, in order to determine the priority of support based on the submission date of the cooking tasks, the support department can input information about the cooking tasks into an AI, and the AI ​​can analyze the information and determine the priority.

[0047] The support unit can adjust the order of support based on the relevance of the cooking tasks during the support process. For example, the support unit may group together cooking tasks used in the same dish. For example, the support unit may prioritize support for highly relevant tasks based on cooking tasks the user has performed together in the past. For example, the support unit may prioritize support for highly relevant tasks based on the difficulty and time required for each cooking task. This allows for prioritizing support for highly relevant tasks by adjusting the order of support based on the relevance of the cooking tasks. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit may input information about the cooking tasks into the AI ​​to adjust the order of support based on the relevance of the cooking tasks, and the AI ​​may analyze the information and adjust the order.

[0048] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0049] The reception desk can receive user ingredient information and, by considering the user's past ingredient selection history, can provide optimal ingredient suggestions. For example, by prioritizing the display of ingredients the user has frequently selected in the past, it can suggest ingredients that match the user's preferences. It can also support safe ingredient selection by automatically excluding ingredients that the user has previously shown allergic reactions to. Furthermore, based on the user's past ingredient selection history, it can also suggest ingredients appropriate for the season and region. This enables ingredient suggestions that take into account the user's preferences and safety, improving the efficiency and safety of ingredient selection.

[0050] The analysis unit can analyze the nutritional value of ingredients and propose a nutritional balance appropriate to the growth stage of infants and toddlers. For example, by prioritizing the suggestion of ingredients containing vitamins and minerals necessary for infants and toddlers' growth, it can provide a nutritionally balanced diet. Furthermore, if a particular nutrient is deficient, it can suggest ingredients to supplement that nutrient. In addition, based on the nutritional value of ingredients, it can support the selection of ingredients to reduce the risk of allergies in infants and toddlers. This makes it possible to provide a nutritionally balanced diet that takes the health of infants and toddlers into consideration.

[0051] The support department can provide assistance tailored to the user's cooking skills during the cooking process. For example, it can explain basic cooking procedures in detail to novice users and suggest efficient cooking methods to experienced users. It can also provide guidance on selecting and using cooking utensils according to the user's skill level. Furthermore, by adjusting cooking time and difficulty based on the user's skill level, it can support users in performing cooking tasks without difficulty. This makes it possible to provide appropriate support according to the user's cooking skills.

[0052] The analysis unit can analyze food storage methods and propose storage methods suitable for infants and toddlers. For example, by suggesting refrigeration or freezing methods depending on the type and characteristics of the food, it can maintain the freshness of the food. It can also analyze the shelf life of food and propose an appropriate expiration date. Furthermore, it can provide instructions on pre-cooking preparation and thawing methods based on the food storage method. In this way, by properly managing food storage methods, it becomes possible to provide safe and fresh food to infants and toddlers.

[0053] The reception desk can analyze the user's past ingredient input history and suggest the optimal input method. For example, it can automatically display ingredient information that the user has frequently entered in the past as a suggestion. The reception desk can, for example, prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can, for example, predict and suggest ingredient information to be used at a specific time of day based on the user's past input history. In this way, by analyzing past input history, the reception desk can suggest the optimal input method to the user. 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 ingredient input history into AI, and the AI ​​can analyze the history and suggest the optimal input method.

[0054] The reception unit can filter ingredient information based on the user's current meal preparation status and areas of interest when the user inputs ingredient information. For example, the reception unit can prioritize displaying ingredient information relevant to the meal the user is currently preparing. For example, the reception unit can suggest relevant ingredient information based on the user's areas of interest (e.g., a specific cuisine genre). For example, the reception unit can suggest new relevant ingredient information based on ingredient information the user has previously searched for. In this way, by filtering ingredient information based on the user's current situation and areas of interest, highly relevant information can be provided. 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 information about the user's current meal preparation status and areas of interest into the AI, which can then analyze the information and filter the relevant ingredient information.

[0055] The reception unit can prioritize inputting highly relevant ingredient information by considering the user's geographical location when inputting ingredient information. For example, the reception unit can prioritize displaying ingredient information available at nearby supermarkets based on the user's current location. For example, the reception unit can suggest region-specific ingredient information based on the user's geographical location. For example, the reception unit can prioritize displaying seasonal ingredient information by considering the user's location. In this way, highly relevant ingredient information can be provided by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into AI, and the AI ​​can analyze the information and prioritize displaying relevant ingredient information.

[0056] The reception unit can analyze the user's social media activity when inputting ingredient information and input relevant ingredient information. For example, the reception unit can suggest relevant ingredient information based on recipes the user has shared on social media. For example, the reception unit can prioritize displaying ingredient information frequently used by the user's social media followers. For example, the reception unit can suggest relevant ingredient information based on posts the user has "liked" on social media. In this way, by analyzing the user's social media activity, it is possible to provide highly relevant ingredient information. 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 data on the user's social media activity into AI, which can then analyze the data and suggest relevant ingredient information.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The reception desk receives information about the ingredients. This information includes, for example, the type of ingredient, its shape, and its texture. The reception desk receives the ingredient information entered by the user. Step 2: The analysis unit analyzes the information received by the reception unit and proposes suitable food shapes and hardness for infants. The analysis unit analyzes the type, shape, and hardness of the food and proposes shapes and hardness that infants can safely eat. Step 3: The support unit assists with the cooking process based on the cooking method proposed by the analysis unit. The support unit provides instructions on how to cut ingredients to the appropriate size, the cooking time required to tenderize the ingredients, and how to operate the cooking equipment.

[0059] (Example of form 2) The system for supporting infant meal management according to an embodiment of the present invention is a system that uses an AI agent to support infant meal management. This system addresses the fact that infants cannot eat the same food as adults because their esophagi are narrow or their teeth have not yet fully grown, and therefore food needs to be cut into small pieces or softened. Tragic accidents of choking and death continue to occur even in modern times, but these accidents can be prevented with appropriate knowledge and verification procedures. Therefore, we propose a system that allows anyone to safely prepare meals for infants, regardless of the environment, by using an AI agent that excels at knowledge and verification procedures. First, the user inputs information about the ingredients. For example, they input information such as the type, shape, and hardness of the ingredients. This information is input to the AI ​​agent. Next, the AI ​​agent analyzes the input information and proposes the appropriate shape and hardness of the ingredients for infants. For example, it proposes methods for cutting ingredients into small pieces or cooking methods to soften them. This allows infants to eat safely. Furthermore, the AI ​​agent supports the actual cooking work based on the proposed cooking methods. For example, it provides instructions on the procedure for cutting ingredients to the appropriate size and the heating time for softening them. This allows users to easily prepare meals suitable for infants and toddlers. This system streamlines infant feeding management and helps prevent choking accidents. For example, the AI ​​agent can appropriately adjust the shape and hardness of ingredients, allowing infants and toddlers to eat safely. Furthermore, users can easily prepare meals suitable for infants and toddlers simply by following the instructions of the AI ​​agent. In this way, the system that supports infant feeding management can safely manage infants and toddlers' meals and prevent choking accidents.

[0060] The system for supporting the management of infant meals according to this embodiment comprises a reception unit, an analysis unit, and a support unit. The reception unit receives information about ingredients. This information includes, but is not limited to, the type, shape, and hardness of the ingredients. The reception unit receives, for example, information about ingredients entered by the user. The analysis unit analyzes the information received by the reception unit and proposes an appropriate shape and hardness for the ingredients for infants. The analysis unit analyzes, for example, the type, shape, and hardness of the ingredients and proposes an appropriate shape and hardness for infants. The analysis unit proposes an appropriate shape and hardness for infants based on the size and hardness of the ingredients. The support unit assists with cooking based on the cooking method proposed by the analysis unit. The support unit provides, for example, instructions on how to cut the ingredients to the appropriate size. The support unit provides, for example, instructions on how to heat the ingredients to soften them. The support unit provides, for example, instructions on how to operate cooking utensils. This system, which supports the management of infants' and toddlers' meals, makes the management of infants' and toddlers' meals more efficient and safer by suggesting suitable shapes and hardnesses for ingredients and assisting with cooking. Some or all of the above processes in the reception, analysis, and support departments may be performed using AI, for example, or without AI. For example, the reception department can input ingredient information entered by the user into the AI, which can then analyze the ingredient information. The analysis department can suggest suitable shapes and hardnesses for ingredients based on the results of the AI's analysis. The support department can assist with cooking based on the cooking method suggested by the AI.

[0061] The reception unit receives information about ingredients. This information includes, but is not limited to, the type, shape, and hardness of the ingredients. The reception unit also receives information about ingredients entered by users. Specifically, users can enter information about ingredients using devices such as smartphones or tablets. The entered information is sent to a cloud server and stored in a database. The reception unit receives this information in real time and sends it to the analysis unit. Furthermore, the reception unit can also automatically read information about ingredients using barcode scanners or QR code readers. For example, by scanning a barcode printed on the ingredient package, the system can automatically obtain information such as the type of ingredient and nutritional content. This allows users to enter information about ingredients without effort, improving the convenience of the system. The reception unit also has a voice input function, allowing users to enter information about ingredients by voice. For example, if a user voice-inputs "carrot, julienned, normal hardness," the system recognizes the information and sends it to the analysis unit. This allows users to enter information about ingredients without using their hands, making cooking easier. By offering these diverse input methods, the reception desk can enhance user convenience and receive information about ingredients accurately and quickly.

[0062] The analysis unit analyzes the information received by the reception unit and proposes suitable food shapes and hardnesses for infants and toddlers. For example, the analysis unit analyzes the type, shape, and hardness of food and proposes shapes and hardnesses suitable for infants and toddlers. Specifically, the analysis unit uses AI to analyze food information. Based on past data and expert knowledge, the AI ​​learns shapes and hardnesses that infants and toddlers can eat safely. For example, based on the size and hardness of the food, the AI ​​proposes shapes and hardnesses that are easy for infants and toddlers to chew and that are less likely to cause choking. Furthermore, the AI ​​can propose appropriate food shapes and hardnesses according to the age and developmental stage of the infant or toddler. For example, it proposes soft, small, bite-sized foods for a 6-month-old infant and slightly harder foods that are easy to hold for a 12-month-old infant. The analysis unit provides these suggestions to users to support the management of infants' and toddlers' diets. In addition, the analysis unit can also analyze the nutritional components and allergy information of food and propose foods suitable for infants and toddlers. For example, if an infant has allergies, the AI ​​takes that information into account and suggests foods that will not trigger allergies. This improves the safety of infants and makes dietary management more efficient. Through these functions, the analysis unit suggests the appropriate shape and hardness of foods for infants and supports the user's dietary management.

[0063] The support unit assists with cooking based on the cooking methods proposed by the analysis unit. For example, the support unit provides instructions on how to cut ingredients to the appropriate size. Specifically, the support unit provides detailed instructions to the user on how to cut the ingredients. For example, when julienning carrots, the support unit shows the procedure of cutting the carrots to a certain length and then julienning them along that length. Furthermore, the support unit provides instructions on the heating time required to soften the ingredients. For example, to soften carrots, it provides specific instructions on the heating time in the microwave or the boiling time in a pot. By providing these instructions to the user, the support unit can streamline the cooking process and provide ingredients suitable for infants and toddlers. The support unit also provides instructions on how to operate cooking utensils. For example, when finely chopping ingredients using a food processor, the support unit provides detailed instructions on how to use the food processor and how to operate it safely. This allows the user to use the cooking utensils correctly and cook the ingredients properly. Furthermore, the support unit also provides instructions on precautions and safety measures during cooking. For example, when handling hot ingredients, the system provides instructions to prevent burns and offers safety measures for handling cooking utensils. This allows users to perform cooking tasks safely. Through these functions, the support unit can assist users in their cooking tasks and provide ingredients suitable for infants and toddlers.

[0064] The support unit can provide instructions on how to cut ingredients to the appropriate size. For example, the support unit can present the user with instructions on how to cut ingredients to the appropriate size. For example, the support unit can provide instructions on how to cut ingredients into small pieces. For example, the support unit can provide instructions on how to finely chop ingredients. For example, the support unit can provide instructions on how to thinly slice ingredients. By providing instructions on how to cut ingredients to the appropriate size, infants can eat safely. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the instructions for cutting ingredients to the appropriate size into AI, and the AI ​​can analyze the instructions and present them to the user.

[0065] The support unit can instruct the heating time required to soften the food. For example, the support unit can present the heating time required to soften the food to the user. For example, the support unit can instruct the steaming time required to steam the food. For example, the support unit can instruct the boiling time required to boil the food. For example, the support unit can instruct the microwave heating time required to soften the food. This allows infants and young children to eat safely by instructing the heating time required to soften the food. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the heating time required to soften the food into the AI, which can then analyze the heating time and present it to the user.

[0066] The analysis unit can analyze information such as the type, shape, and hardness of food ingredients and propose shapes and hardness suitable for infants. For example, the analysis unit can analyze the type, shape, and hardness of food ingredients and propose shapes and hardness suitable for infants. For example, the analysis unit can propose shapes and hardness that infants can safely eat based on the size and hardness of the food ingredients. For example, the analysis unit can propose appropriate shapes and hardness depending on the type of food ingredient. For example, the analysis unit can measure the hardness of food ingredients and propose hardness suitable for infants. In this way, by analyzing the type, shape, and hardness of food ingredients and proposing shapes and hardness suitable for infants, infants can eat safely. 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 information on the type, shape, and hardness of food ingredients into the AI, which can analyze the information and propose shapes and hardness suitable for infants.

[0067] The reception desk can estimate the user's emotions and adjust the timing of ingredient information input based on the estimated emotions. For example, if the user is stressed, the reception desk provides a simple interface and minimizes the input steps. For example, if the user is relaxed, the reception desk provides detailed input options and suggests a customizable input method. For example, if the user is in a hurry, the reception desk prioritizes voice input to allow for quick input of ingredient information. This reduces the user's burden by adjusting the timing of ingredient information input 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 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 using AI. For example, the reception desk can input the user's facial expression data into a generative AI to estimate the user's emotions, and the generative AI can estimate the emotions.

[0068] The reception desk can analyze the user's past ingredient input history and suggest the optimal input method. For example, the reception desk can automatically display ingredient information that the user has frequently entered in the past as a suggestion. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest ingredient information to be used at a specific time of day based on the user's past input history. In this way, by analyzing past input history, the reception desk can suggest the optimal input method to the user. 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 ingredient input history into AI, and the AI ​​can analyze the history and suggest the optimal input method.

[0069] The reception unit can filter ingredient information based on the user's current meal preparation status and areas of interest when the user inputs ingredient information. For example, the reception unit can prioritize displaying ingredient information relevant to the meal the user is currently preparing. For example, the reception unit can suggest relevant ingredient information based on the user's areas of interest (e.g., a specific cuisine genre). For example, the reception unit can suggest new relevant ingredient information based on ingredient information the user has previously searched for. In this way, by filtering ingredient information based on the user's current situation and areas of interest, highly relevant information can be provided. 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 information about the user's current meal preparation status and areas of interest into the AI, which can then analyze the information and filter the relevant ingredient information.

[0070] The reception unit can estimate the user's emotions and determine the priority of ingredient information to be entered based on the estimated emotions. For example, if the user is tired, the reception unit will prioritize displaying ingredient information that can be easily prepared. For example, if the user is relaxed, the reception unit will suggest ingredient information that can be prepared over a longer period of time. For example, if the user is in a hurry, the reception unit will prioritize displaying ingredient information that can be prepared in a short time. This reduces the user's burden by prioritizing ingredient information 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 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 the generative AI to estimate the user's emotions, and the generative AI can estimate the emotions.

[0071] The reception unit can prioritize inputting highly relevant ingredient information by considering the user's geographical location when inputting ingredient information. For example, the reception unit can prioritize displaying ingredient information available at nearby supermarkets based on the user's current location. For example, the reception unit can suggest region-specific ingredient information based on the user's geographical location. For example, the reception unit can prioritize displaying seasonal ingredient information by considering the user's location. In this way, highly relevant ingredient information can be provided by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into AI, and the AI ​​can analyze the information and prioritize displaying relevant ingredient information.

[0072] The reception unit can analyze the user's social media activity when inputting ingredient information and input relevant ingredient information. For example, the reception unit can suggest relevant ingredient information based on recipes the user has shared on social media. For example, the reception unit can prioritize displaying ingredient information frequently used by the user's social media followers. For example, the reception unit can suggest relevant ingredient information based on posts the user has "liked" on social media. In this way, by analyzing the user's social media activity, it is possible to provide highly relevant ingredient information. 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 data on the user's social media activity into AI, which can then analyze the data and suggest relevant ingredient information.

[0073] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit provides simple and easily understandable analysis results. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is in a hurry, the analysis unit provides concise analysis results that get straight to the point. By adjusting the presentation of the analysis results according to the user's emotions, the analysis unit can provide results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative 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-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI to estimate the user's emotions, and the generative AI can estimate the emotions.

[0074] The analysis unit can adjust the level of detail of the analysis based on the importance of the ingredients. For example, the analysis unit can perform a detailed analysis for major ingredients and a simplified analysis for secondary ingredients. For example, the analysis unit can perform a detailed analysis for ingredients containing important nutrients based on their nutritional value. For example, the analysis unit can perform a detailed analysis for high-risk ingredients based on their allergy risk. This allows for the priority provision of important information 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 information into AI to adjust the level of detail of the analysis based on the importance of the ingredients, and the AI ​​can analyze the information and adjust the level of detail.

[0075] The analysis unit can apply different analysis algorithms depending on the category of food ingredient during analysis. For example, the analysis unit can apply different analysis algorithms to vegetables and fruits and perform analysis according to their respective characteristics. For example, the analysis unit can apply different analysis algorithms to meats and fish and perform analysis according to their respective cooking methods. For example, the analysis unit can apply different analysis algorithms to grains and dairy products and perform analysis according to their respective nutritional values. By applying different analysis algorithms depending on the category of food ingredient, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, in order to apply different analysis algorithms depending on the category of food ingredient, the analysis unit can input information about the food ingredient into the AI, and the AI ​​can analyze the information and apply an appropriate algorithm.

[0076] The analysis unit can estimate the user's emotions and adjust the length of the analysis results based on the estimated emotions. For example, if the user is in a hurry, the analysis unit provides a short, concise analysis result. If the user is relaxed, the analysis unit provides a longer analysis result with detailed explanations. If the user is excited, the analysis unit provides an analysis result with visually stimulating effects. By adjusting the length of the analysis results according to the user's emotions, the analysis results can be made easier for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative 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 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 a generative AI to estimate the user's emotions, and the generative AI can estimate the emotions.

[0077] The analysis unit can determine the priority of analysis based on when the ingredients were submitted. For example, the analysis unit may prioritize the analysis of recently submitted ingredient information. For example, the analysis unit may determine the priority of analysis based on a deadline specified by the user. For example, the analysis unit may prioritize the analysis of ingredients that should be consumed quickly based on their freshness. By determining the priority of analysis based on when the ingredients were submitted, important information can be provided preferentially. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, in order to determine the priority of analysis based on when the ingredients were submitted, the analysis unit may input ingredient information into AI, and the AI ​​may analyze the information and determine the priority.

[0078] The analysis unit can adjust the order of analysis based on the relationships between ingredients during the analysis process. For example, the analysis unit may group together ingredients used in the same dish. For example, the analysis unit may prioritize the analysis of highly related ingredients based on ingredients the user has used together in the past. For example, the analysis unit may prioritize the analysis of highly related ingredients based on their nutritional value or allergy risk. By adjusting the order of analysis based on the relationships between ingredients, the analysis unit can prioritize the provision of highly relevant information. 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 may input ingredient information into AI to adjust the order of analysis based on the relationships between ingredients, and the AI ​​may analyze the information and adjust the order.

[0079] The support unit can estimate the user's emotions and adjust the way it presents support based on those emotions. For example, if the user is nervous, the support unit provides simple and easily visible support. If the user is relaxed, the support unit provides detailed support. If the user is in a hurry, the support unit provides concise support that gets straight to the point. By adjusting the way it presents support according to the user's emotions, it can provide support that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The 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 support unit may be performed using AI, or not using AI. For example, the support unit can input the user's facial expression data into the generative AI to estimate the user's emotions, and the generative AI can estimate the emotions.

[0080] The support unit can adjust the level of detail of support based on the importance of the cooking task. For example, the support unit can provide detailed support for major cooking tasks and simplified support for secondary cooking tasks. For example, the support unit can provide detailed support for important tasks based on the difficulty of the cooking task. For example, the support unit can provide detailed support for time-consuming tasks based on the time it takes to cook. This allows for priority support for important tasks by adjusting the level of detail of support based on the importance of the cooking task. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input information about the cooking task into the AI ​​to adjust the level of detail of support based on the importance of the cooking task, and the AI ​​can analyze the information and adjust the level of detail.

[0081] The support unit can apply different support algorithms depending on the category of cooking task during the support process. For example, the support unit can apply different support algorithms to vegetable cooking and meat cooking, providing support tailored to the characteristics of each. For example, the support unit can apply different support algorithms to grilling and boiling, providing support tailored to each cooking method. For example, the support unit can apply different support algorithms to dessert cooking and main dish cooking, providing support tailored to each category. By applying different support algorithms depending on the category of cooking task, more appropriate support can be provided. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, in order to apply different support algorithms depending on the category of cooking task, the support unit can input information about the cooking task into the AI, which can then analyze the information and apply the appropriate algorithm.

[0082] The support unit can estimate the user's emotions and adjust the length of the support content based on the estimated emotions. For example, if the user is in a hurry, the support unit will provide short, concise support content. If the user is relaxed, the support unit will provide longer support content that includes detailed explanations. If the user is excited, the support unit will provide support content with visually stimulating effects. By adjusting the length of the support content according to the user's emotions, the support unit can provide support content that is easy for the user to understand. 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 support unit may be performed using AI or not using AI. For example, the support unit can input the user's facial expression data into the generative AI to estimate the user's emotions, and the generative AI can estimate the emotions.

[0083] The support department can determine the priority of support based on the submission date of the cooking tasks. For example, the support department will prioritize support for cooking tasks submitted most recently. For example, the support department will determine the priority of support based on the deadline specified by the user. For example, the support department will determine the priority based on the importance of the cooking tasks. This allows important tasks to be prioritized by determining the priority of support based on the submission date of the cooking tasks. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, in order to determine the priority of support based on the submission date of the cooking tasks, the support department can input information about the cooking tasks into an AI, and the AI ​​can analyze the information and determine the priority.

[0084] The support unit can adjust the order of support based on the relevance of the cooking tasks during the support process. For example, the support unit may group together cooking tasks used in the same dish. For example, the support unit may prioritize support for highly relevant tasks based on cooking tasks the user has performed together in the past. For example, the support unit may prioritize support for highly relevant tasks based on the difficulty and time required for each cooking task. This allows for prioritizing support for highly relevant tasks by adjusting the order of support based on the relevance of the cooking tasks. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit may input information about the cooking tasks into the AI ​​to adjust the order of support based on the relevance of the cooking tasks, and the AI ​​may analyze the information and adjust the order.

[0085] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0086] The reception desk can receive user ingredient information and, by considering the user's past ingredient selection history, can provide optimal ingredient suggestions. For example, by prioritizing the display of ingredients the user has frequently selected in the past, it can suggest ingredients that match the user's preferences. It can also support safe ingredient selection by automatically excluding ingredients that the user has previously shown allergic reactions to. Furthermore, based on the user's past ingredient selection history, it can also suggest ingredients appropriate for the season and region. This enables ingredient suggestions that take into account the user's preferences and safety, improving the efficiency and safety of ingredient selection.

[0087] The analysis unit can analyze the nutritional value of ingredients and propose a nutritional balance appropriate to the growth stage of infants and toddlers. For example, by prioritizing the suggestion of ingredients containing vitamins and minerals necessary for infants and toddlers' growth, it can provide a nutritionally balanced diet. Furthermore, if a particular nutrient is deficient, it can suggest ingredients to supplement that nutrient. In addition, based on the nutritional value of ingredients, it can support the selection of ingredients to reduce the risk of allergies in infants and toddlers. This makes it possible to provide a nutritionally balanced diet that takes the health of infants and toddlers into consideration.

[0088] The support department can provide assistance tailored to the user's cooking skills during the cooking process. For example, it can explain basic cooking procedures in detail to novice users and suggest efficient cooking methods to experienced users. It can also provide guidance on selecting and using cooking utensils according to the user's skill level. Furthermore, by adjusting cooking time and difficulty based on the user's skill level, it can support users in performing cooking tasks without difficulty. This makes it possible to provide appropriate support according to the user's cooking skills.

[0089] The analysis unit can analyze food storage methods and propose storage methods suitable for infants and toddlers. For example, by suggesting refrigeration or freezing methods depending on the type and characteristics of the food, it can maintain the freshness of the food. It can also analyze the shelf life of food and propose an appropriate expiration date. Furthermore, it can provide instructions on pre-cooking preparation and thawing methods based on the food storage method. In this way, by properly managing food storage methods, it becomes possible to provide safe and fresh food to infants and toddlers.

[0090] The support unit can estimate the user's emotions, monitor the progress of the cooking process in real time based on those emotions, and provide support at the appropriate time. For example, if the user is feeling stressed, it can slow down the cooking process and provide advice to help them relax. If the user is in a hurry, it can provide advice to improve the efficiency of the cooking process. Furthermore, if the user is relaxed, it can offer suggestions to help them enjoy the cooking process. This makes it possible to provide appropriate support tailored to the user's emotions.

[0091] The reception desk can analyze the user's past ingredient input history and suggest the optimal input method. For example, it can automatically display ingredient information that the user has frequently entered in the past as a suggestion. The reception desk can, for example, prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can, for example, predict and suggest ingredient information to be used at a specific time of day based on the user's past input history. In this way, by analyzing past input history, the reception desk can suggest the optimal input method to the user. 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 ingredient input history into AI, and the AI ​​can analyze the history and suggest the optimal input method.

[0092] The reception unit can filter ingredient information based on the user's current meal preparation status and areas of interest when the user inputs ingredient information. For example, the reception unit can prioritize displaying ingredient information relevant to the meal the user is currently preparing. For example, the reception unit can suggest relevant ingredient information based on the user's areas of interest (e.g., a specific cuisine genre). For example, the reception unit can suggest new relevant ingredient information based on ingredient information the user has previously searched for. In this way, by filtering ingredient information based on the user's current situation and areas of interest, highly relevant information can be provided. 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 information about the user's current meal preparation status and areas of interest into the AI, which can then analyze the information and filter the relevant ingredient information.

[0093] The reception unit can estimate the user's emotions and determine the priority of ingredient information to be entered based on the estimated emotions. For example, if the user is tired, the reception unit will prioritize displaying ingredient information that can be easily prepared. For example, if the user is relaxed, the reception unit will suggest ingredient information that can be prepared over a longer period of time. For example, if the user is in a hurry, the reception unit will prioritize displaying ingredient information that can be prepared in a short time. This reduces the user's burden by prioritizing ingredient information 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 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 the generative AI to estimate the user's emotions, and the generative AI can estimate the emotions.

[0094] The reception unit can prioritize inputting highly relevant ingredient information by considering the user's geographical location when inputting ingredient information. For example, the reception unit can prioritize displaying ingredient information available at nearby supermarkets based on the user's current location. For example, the reception unit can suggest region-specific ingredient information based on the user's geographical location. For example, the reception unit can prioritize displaying seasonal ingredient information by considering the user's location. In this way, highly relevant ingredient information can be provided by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into AI, and the AI ​​can analyze the information and prioritize displaying relevant ingredient information.

[0095] The reception unit can analyze the user's social media activity when inputting ingredient information and input relevant ingredient information. For example, the reception unit can suggest relevant ingredient information based on recipes the user has shared on social media. For example, the reception unit can prioritize displaying ingredient information frequently used by the user's social media followers. For example, the reception unit can suggest relevant ingredient information based on posts the user has "liked" on social media. In this way, by analyzing the user's social media activity, it is possible to provide highly relevant ingredient information. 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 data on the user's social media activity into AI, which can then analyze the data and suggest relevant ingredient information.

[0096] The following briefly describes the processing flow for example form 2.

[0097] Step 1: The reception desk receives information about the ingredients. This information includes, for example, the type of ingredient, its shape, and its texture. The reception desk receives the ingredient information entered by the user. Step 2: The analysis unit analyzes the information received by the reception unit and proposes suitable food shapes and hardness for infants. The analysis unit analyzes the type, shape, and hardness of the food and proposes shapes and hardness that infants can safely eat. Step 3: The support unit assists with the cooking process based on the cooking method proposed by the analysis unit. The support unit provides instructions on how to cut ingredients to the appropriate size, the cooking time required to tenderize the ingredients, and how to operate the cooking equipment.

[0098] 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.

[0099] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0100] 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.

[0101] Each of the multiple elements described above, including the reception unit, analysis unit, and support 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 reception device 38 of the smart device 14 and receives information about ingredients entered by the user. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the information about the ingredients to suggest a shape and hardness suitable for infants. The support unit is implemented, for example, by the control unit 46A of the smart device 14 and provides instructions to support the cooking process. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0102] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.).

[0114] 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.

[0115] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0116] 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.

[0117] Each of the multiple elements described above, including the reception unit, analysis unit, and support unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives information about ingredients entered by the user. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the information about the ingredients, suggesting a shape and hardness suitable for infants. The support unit is implemented by the control unit 46A of the smart glasses 214 and provides instructions to support the cooking process. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0118] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] 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.

[0133] Each of the multiple elements described above, including the reception unit, analysis unit, and support unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives information about ingredients entered by the user. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the information about the ingredients and suggests a shape and hardness suitable for infants. The support unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides instructions to support the cooking process. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0134] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] 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.

[0150] Each of the multiple elements described above, including the reception unit, analysis unit, and support 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 microphone 238 of the robot 414 and receives information about ingredients entered by the user. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the information about the ingredients and suggests a shape and hardness suitable for infants. The support unit is implemented by, for example, the control unit 46A of the robot 414 and gives instructions to support the cooking work. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] (Note 1) A reception desk that accepts information about ingredients, The analysis unit analyzes the information received by the reception unit and proposes the shape and hardness of food suitable for infants and toddlers, The system includes a support unit that assists in cooking operations based on the cooking method proposed by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned support unit, Instructions for cutting ingredients to the appropriate size. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned support unit, Instructs the cooking time required to soften the ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, The system analyzes information such as the type, shape, and hardness of ingredients and suggests shapes and hardness suitable for infants and toddlers. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of ingredient information input based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is We analyze the user's past ingredient input history and suggest the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When entering ingredient information, filtering is performed based on the user's current meal preparation status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system estimates the user's emotions and determines the priority of the food ingredient information to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering ingredient information, the system prioritizes inputting highly relevant ingredient information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When entering ingredient information, the system analyzes the user's social media activity and inputs relevant ingredient information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts the way the analysis results are presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of the ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the priority of analysis is determined based on when the ingredients were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relationships between the ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned support unit, The system estimates the user's emotions and adjusts the way support is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned support unit, When providing support, adjust the level of detail based on the importance of the cooking task. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned support unit, When providing assistance, different assistance algorithms are applied depending on the category of the cooking task. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned support unit, The system estimates the user's emotions and adjusts the length of the support content based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned support unit, When providing support, we determine the priority of assistance based on the timing of submission of cooking work. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned support unit, When providing assistance, adjust the order of assistance based on the relevance of the cooking tasks. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0170] 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 accepts information about ingredients, The analysis unit analyzes the information received by the reception unit and proposes the shape and hardness of food suitable for infants and toddlers, The system includes a support unit that assists in cooking operations based on the cooking method proposed by the analysis unit. A system characterized by the following features.

2. The aforementioned support unit, Instructions for cutting ingredients to the appropriate size. The system according to feature 1.

3. The aforementioned support unit, Instructs the cooking time required to soften the ingredients. The system according to feature 1.

4. The aforementioned analysis unit, The system analyzes information such as the type, shape, and hardness of ingredients and suggests shapes and hardness suitable for infants and toddlers. The system according to feature 1.

5. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of ingredient information input based on the estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is We analyze the user's past ingredient input history and suggest the optimal input method. The system according to feature 1.

7. The aforementioned reception unit is When entering ingredient information, filtering is performed based on the user's current meal preparation status and areas of interest. The system according to feature 1.

8. The aforementioned reception unit is The system estimates the user's emotions and determines the priority of the food ingredient information to be entered based on the estimated user emotions. The system according to feature 1.

9. The aforementioned reception unit is When entering ingredient information, the system prioritizes inputting highly relevant ingredient information, taking into account the user's geographical location. The system according to feature 1.

10. The aforementioned reception unit is When entering ingredient information, the system analyzes the user's social media activity and inputs relevant ingredient information. The system according to feature 1.

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