system

Abstract

The system according to this embodiment aims to shorten cooking time and reduce food costs. [Solution] The system according to the embodiment comprises a recipe analysis unit, a time-saving suggestion unit, a cost-saving suggestion unit, and a feedback collection unit. The recipe analysis unit analyzes a recipe and identifies points where time can be saved. The time-saving suggestion unit proposes a time-saving recipe based on the time-saving points identified by the recipe analysis unit. The cost-saving suggestion unit proposes alternative ingredients based on the recipe proposed by the time-saving suggestion unit. The feedback collection unit collects feedback from the user and improves the next suggestion.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that cooking takes time and effort and there is no alternative for suppressing food material costs.

[0005] The system according to the embodiment aims to shorten the cooking time and suppress the food material costs.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a recipe analysis unit, a time-saving suggestion unit, a cost-saving suggestion unit, and a feedback collection unit. The recipe analysis unit analyzes a recipe and identifies points where time can be saved. The time-saving suggestion unit proposes a time-saving recipe based on the time-saving points identified by the recipe analysis unit. The cost-saving suggestion unit proposes alternative ingredients based on the recipe proposed by the time-saving suggestion unit. The feedback collection unit collects feedback from the user and improves the next suggestion. [Effects of the Invention]

[0007] The system according to this embodiment can reduce cooking time and lower food costs. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The cooking assistant system according to an embodiment of the present invention is a system that utilizes AI to reduce cooking time and effort, making it possible to easily prepare delicious meals. First, the cooking assistant system uses AI to analyze existing recipes and identify points where time can be saved in each step. Next, the generating AI generates time-saving and cost-saving recipes based on the recipe data and suggests alternative ingredients. Furthermore, it collects feedback from users, which the AI ​​analyzes to improve future suggestions. This mechanism makes it possible to prepare delicious meals quickly and easily, even when busy, efficiently utilize ingredients, and reduce costs. It also improves satisfaction with daily meals by making cooking a more enjoyable habit. For example, the cooking assistant system uses AI to analyze existing recipes and identify points where time can be saved in each step. In this process, each step of the recipe is broken down into smaller parts to identify which parts can be made more efficient. For example, efficiency can be improved by utilizing simpler preparation and cooking methods, such as how to cut ingredients or how to use cooking utensils. Next, the generating AI generates time-saving and cost-saving recipes based on the recipe data. Specifically, it suggests alternative ingredients to reduce costs while maintaining flavor. For example, ingredient costs can be reduced by replacing expensive ingredients with cheaper alternatives. Furthermore, alternative ingredients offer healthy and eco-friendly options. Additionally, user feedback is collected and analyzed by the AI ​​to improve future suggestions. This enhances the quality of suggestions and provides better-quality meals. For example, users can try suggested recipes and provide feedback, allowing the AI ​​to learn from that information and incorporate it into future suggestions. This system enables users to prepare delicious meals quickly and easily, even when busy. Users can utilize ingredients efficiently and reduce costs. They can also improve their satisfaction with daily meals by enjoying the cooking habit. This is effective for a variety of target groups, such as busy homemakers, cooking beginners, and budget-conscious individuals. In this way, AI-powered cooking assistants support users in making cooking easier and smarter.This allows the system to accommodate the efficiency and economical lifestyle demanded in modern society, and to support people who are busy with meal preparation and cooking. As a result, the cooking assistant system enables users to prepare delicious meals quickly and easily.

[0029] The cooking assistant system according to this embodiment comprises a recipe analysis unit, a time-saving suggestion unit, a cost-saving suggestion unit, and a feedback collection unit. The recipe analysis unit analyzes recipes and identifies points where time can be saved. For example, the recipe analysis unit analyzes existing recipes, breaks down each step into smaller parts, and identifies which parts can be made more efficient. For example, the recipe analysis unit can analyze simple preparations and cooking methods, such as how to cut ingredients or how to use cooking utensils. The recipe analysis unit can use AI to analyze each step of a recipe and identify points for efficiency improvements. The time-saving suggestion unit proposes time-saving recipes based on the time-saving points identified by the recipe analysis unit. For example, the time-saving suggestion unit can use generation AI to generate time-saving recipes based on recipe data. For example, the time-saving suggestion unit can generate specific time-saving recipes and propose them to the user. The time-saving suggestion unit can use generation AI to streamline the steps of a recipe and shorten cooking time. The cost-saving suggestion unit proposes alternative ingredients based on the recipe proposed by the time-saving suggestion unit. The cost-saving suggestion unit, for example, uses generative AI to suggest replacing expensive ingredients with cheaper alternatives. For example, the cost-saving suggestion unit can suggest alternative ingredients, reducing costs while maintaining flavor. The cost-saving suggestion unit can also use generative AI to suggest healthy and eco-friendly alternative ingredients. The feedback collection unit collects feedback from users and improves future suggestions. For example, the feedback collection unit learns from users who actually try the suggested recipes and provide feedback on the results, and then incorporates that information into future suggestions. The feedback collection unit can use AI to analyze user feedback and improve the quality of suggestions. As a result, the cooking assistant system according to this embodiment allows users to prepare delicious meals quickly and easily.

[0030] The Recipe Analysis Department analyzes recipes and identifies points where time can be saved. For example, it analyzes existing recipes, breaks down each step into smaller parts, and identifies which parts can be made more efficient. Specifically, the Recipe Analysis Department can analyze simple preparation and cooking methods, such as how to cut ingredients and how to use cooking utensils. For example, regarding how to cut ingredients, it can identify methods for cutting more vegetables at once or cutting them into specific shapes to shorten cooking time. Regarding the use of cooking utensils, it can analyze how to utilize time-saving cooking equipment such as microwave ovens and pressure cookers. The Recipe Analysis Department can use AI to analyze each step of a recipe and identify points for efficiency improvements. The AI ​​uses natural language processing technology to analyze the text of the recipe and evaluate the time and effort required for each step. Furthermore, it can use image recognition technology to analyze images and videos of the cooking process and identify areas where efficiency can be improved. As a result, the Recipe Analysis Department can provide users with specific advice on how to cook efficiently in a short amount of time.

[0031] The Time-Saving Suggestion Department proposes time-saving recipes based on time-saving points identified by the Recipe Analysis Department. For example, the Time-Saving Suggestion Department generates time-saving recipes based on recipe data, using a generative AI. Specifically, the generative AI reconstructs each step of a recipe and proposes the optimal procedure for shortening cooking time. For example, it can suggest methods for preparing ingredients simultaneously or for efficiently reusing cooking utensils. The Time-Saving Suggestion Department can streamline recipe steps and shorten cooking time using the generative AI. The generative AI learns from past recipe data and user feedback to generate optimal time-saving recipes. Furthermore, the Time-Saving Suggestion Department can also customize recipes according to user preferences and cooking environments. For example, for users who do not have a specific cooking utensil, it will suggest an alternative procedure that does not require that utensil. The Time-Saving Suggestion Department can also provide advice to further shorten cooking time according to the user's schedule. In this way, the Time-Saving Suggestion Department helps users cook efficiently and create delicious meals in a short amount of time.

[0032] The Cost-Saving Suggestion Department proposes alternative ingredients based on recipes suggested by the Time-Saving Suggestion Department. For example, the Cost-Saving Suggestion Department uses generative AI to suggest replacing expensive ingredients with cheaper alternatives. Specifically, the generative AI suggests alternative ingredients that reduce costs while maintaining the nutritional value and flavor of the recipe. For example, it can suggest replacing expensive meats with cheaper beans and vegetables, or substituting certain seasonings with other cheaper seasonings. The Cost-Saving Suggestion Department can also use generative AI to suggest healthy and eco-friendly alternative ingredients. The generative AI considers the user's food preferences and allergy information to select the most suitable alternative ingredients. Furthermore, the Cost-Saving Suggestion Department can also suggest ways to further reduce costs by utilizing seasonal ingredients. In addition, the Cost-Saving Suggestion Department also suggests recipes that utilize the ingredients the user already has. This allows users to use ingredients without waste, achieving economical and environmentally friendly cooking.

[0033] The feedback collection unit collects user feedback to improve future suggestions. For example, the feedback collection unit allows users to try suggested recipes and provide feedback on the results, which the AI ​​then learns and incorporates into future suggestions. Specifically, it collects photos and comments of dishes cooked by users, and the AI ​​analyzes them. Based on user feedback, the AI ​​identifies areas for improvement in recipes and points for new suggestions. For example, if a user provides feedback that "this recipe took too long," the AI ​​learns this information and suggests a more efficient recipe next time. Also, if a user provides feedback that "this substitute ingredient lacked flavor," the AI ​​adjusts its suggestions to propose other substitute ingredients. The feedback collection unit can use AI to analyze user feedback and improve the quality of suggestions. Furthermore, the feedback collection unit can provide personalized suggestions tailored to the user's preferences and cooking skills. This allows the feedback collection unit to increase user satisfaction and continuously improve the system.

[0034] The recipe analysis unit can analyze simple preparation and cooking methods, such as how to cut ingredients and how to use cooking utensils. For example, the recipe analysis unit can analyze how to cut ingredients and suggest efficient cutting methods. For instance, it can suggest a method for cutting ingredients evenly. Furthermore, the recipe analysis unit can analyze how to use cooking utensils and suggest efficient usage methods. For example, it can suggest a method to optimize the use of cooking utensils and shorten cooking time. In this way, by analyzing simple preparation and cooking methods, the efficiency of cooking can be improved.

[0035] The time-saving suggestion unit can generate specific time-saving recipes. For example, it can use generation AI to generate time-saving recipes based on recipe data. For example, the time-saving suggestion unit can suggest specific steps to shorten cooking time. It can also suggest efficient cooking methods. For example, it can suggest ways to shorten cooking time by optimizing the use of cooking utensils. In this way, cooking time can be shortened by generating specific time-saving recipes.

[0036] The cost-saving suggestion department can propose replacing expensive ingredients with cheaper alternatives. For example, it can use generative AI to suggest replacements for expensive ingredients with cheaper alternatives. For instance, the cost-saving suggestion department can suggest alternative ingredients, reducing costs while maintaining flavor. Furthermore, the cost-saving suggestion department can also suggest healthy and eco-friendly alternative ingredients. For example, it can suggest highly nutritious alternative ingredients. This allows for a reduction in ingredient costs by replacing expensive ingredients with cheaper alternatives.

[0037] The feedback collection unit can analyze user feedback and incorporate it into future suggestions. For example, the AI ​​learns from users who actually try the suggested recipes and provide feedback on the results, and then incorporates that information into future suggestions. For example, the feedback collection unit can collect user feedback and improve the quality of suggestions. The feedback collection unit can also analyze user feedback and incorporate it into future suggestions. For example, the feedback collection unit can improve the content of suggestions based on user feedback. In this way, the quality of suggestions can be improved by analyzing user feedback.

[0038] The recipe analysis unit can select the optimal analysis method by referring to the user's past cooking history during recipe analysis. For example, the recipe analysis unit can prioritize analyzing similar cooking methods based on recipes the user has successfully made in the past. For example, the recipe analysis unit can analyze different cooking methods to avoid recipes the user has failed at in the past. Furthermore, the recipe analysis unit can prioritize analyzing recipes that use specific cooking utensils based on the user's past cooking history. In this way, the optimal analysis method can be selected by referring to the user's past cooking history.

[0039] The recipe analysis unit can perform recipe analysis while considering the nutritional value of ingredients and allergy information. For example, based on the user's allergy information, the recipe analysis unit can analyze recipes that do not contain ingredients that cause allergies. For example, the recipe analysis unit can consider the user's nutritional balance and prioritize the analysis of recipes that contain a lot of specific nutrients. In addition, the recipe analysis unit can analyze low-calorie or high-protein recipes according to the user's health condition. In this way, by considering the nutritional value of ingredients and allergy information, it can provide recipes that are mindful of the user's health.

[0040] The recipe analysis unit can prioritize the analysis of region-specific ingredients by considering the user's geographical location during recipe analysis. For example, the recipe analysis unit can prioritize the analysis of ingredients that are readily available in the user's region. For instance, the recipe analysis unit can analyze recipes based on traditional dishes from the user's region. Furthermore, the recipe analysis unit can also prioritize the analysis of recipes that use seasonal ingredients from the user's region. This allows the system to provide recipes using region-specific ingredients by considering the user's geographical location.

[0041] The recipe analysis unit can analyze users' social media activity and identify related recipes during recipe analysis. For example, it can analyze related recipes based on recipes shared by users on social media. It can also prioritize analyzing recipes from cooking accounts that users follow. Furthermore, it can analyze similar recipes based on recipes that users have "liked." This allows the system to provide recipes tailored to users' interests by analyzing their social media activity.

[0042] The time-saving suggestion function can select the most suitable time-saving method by referring to the user's past cooking history when making suggestions. For example, the time-saving suggestion function can suggest similar time-saving methods based on time-saving recipes that the user has successfully used in the past. For example, the time-saving suggestion function can suggest different time-saving methods to avoid time-saving recipes that the user has failed at in the past. Furthermore, the time-saving suggestion function can also suggest time-saving methods that use specific cooking utensils based on the user's past cooking history. In this way, the optimal time-saving method can be selected by referring to the user's past cooking history.

[0043] The Time-Saving Proposal Department can propose time-saving methods while considering the frequency and efficiency of use of cooking utensils. For example, the Time-Saving Proposal Department can propose the optimal time-saving method based on the cooking utensils that the user frequently uses. For example, the Time-Saving Proposal Department can propose the optimal time-saving method by considering the efficiency of the cooking utensils that the user owns. In addition, the Time-Saving Proposal Department can propose time-saving methods that utilize cooking utensils that the user has newly purchased. In this way, by considering the frequency and efficiency of use of cooking utensils, the optimal time-saving method can be proposed.

[0044] The time-saving suggestion department can propose time-saving methods specific to the user's region, taking into account the user's geographical location. For example, the department can suggest time-saving methods using ingredients readily available in the user's region. For example, it can suggest time-saving methods based on traditional dishes from the user's region. It can also suggest time-saving methods using seasonal ingredients from the user's region. In this way, by considering the user's geographical location, it can provide time-saving methods specific to the region.

[0045] The Time-Saving Recipe Department can analyze a user's social media activity and suggest relevant time-saving recipes when making time-saving suggestions. For example, the Time-Saving Recipe Department can suggest relevant time-saving recipes based on recipes that the user has shared on social media. For instance, the Time-Saving Recipe Department can prioritize suggesting time-saving recipes from cooking accounts that the user follows. Furthermore, the Time-Saving Recipe Department can suggest similar time-saving recipes based on recipes that the user has "liked." This allows the department to provide time-saving recipes tailored to the user's interests by analyzing their social media activity.

[0046] The savings suggestion function can select the most suitable alternative ingredients by referring to the user's past purchase history when making savings suggestions. For example, the savings suggestion function can suggest the most suitable alternative ingredients based on the ingredients the user has purchased in the past. For instance, the savings suggestion function can consider the price of ingredients the user has purchased in the past and suggest alternative ingredients that can reduce costs. Furthermore, the savings suggestion function can also suggest savings recipes that use specific ingredients based on the user's past purchase history. In this way, the optimal alternative ingredients can be selected by referring to the user's past purchase history.

[0047] The savings suggestion function can make suggestions while considering the nutritional value and allergy information of ingredients. For example, based on the user's allergy information, the savings suggestion function can suggest alternative ingredients that do not contain ingredients that cause allergies. For example, the savings suggestion function can consider the user's nutritional balance and suggest alternative ingredients that are rich in specific nutrients. Furthermore, the savings suggestion function can suggest low-calorie or high-protein alternative ingredients depending on the user's health condition. In this way, by considering the nutritional value and allergy information of ingredients, it can make suggestions that take the user's health into consideration.

[0048] The cost-saving suggestion function can suggest regionally specific alternative ingredients, taking into account the user's geographical location when making cost-saving suggestions. For example, it can suggest alternative ingredients that are readily available in the user's area. For example, it can suggest alternative ingredients based on traditional dishes from the user's region. It can also suggest alternative ingredients using seasonal ingredients from the user's region. In this way, by considering the user's geographical location, it can provide regionally specific alternative ingredients.

[0049] The savings suggestion department can analyze a user's social media activity and suggest relevant alternative ingredients when making savings suggestions. For example, it can suggest relevant alternative ingredients based on recipes the user has shared on social media. For instance, it can prioritize suggesting alternative ingredients from cooking accounts the user follows. It can also suggest similar alternative ingredients based on recipes the user has "liked." In this way, by analyzing the user's social media activity, it can provide alternative ingredients that match the user's interests.

[0050] The feedback collection unit can select the optimal collection method by referring to the user's past feedback history when collecting feedback. For example, the feedback collection unit can collect feedback in a similar format to the feedback the user has provided in the past. For example, the feedback collection unit can analyze the content of feedback the user has provided in the past and prioritize collecting specific questions. The feedback collection unit can also request feedback at specific time periods based on the user's past feedback history. In this way, the optimal collection method can be selected by referring to the user's past feedback history.

[0051] The feedback collection unit can select the optimal collection method when collecting feedback, taking into account the user's device information. For example, if the user is using a smartphone, the feedback collection unit can provide a feedback collection method that is adapted to the screen size. For example, if the user is using a tablet, the feedback collection unit can provide a feedback collection method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the feedback collection unit can provide a concise and highly visible feedback collection method. In this way, the optimal collection method can be provided by taking into account the user's device information.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The recipe analysis unit can select the optimal analysis method by referring to the user's past cooking history during recipe analysis. For example, it can prioritize analyzing similar cooking methods based on recipes the user has successfully made in the past. It can also analyze different cooking methods to avoid recipes the user has failed at in the past. Furthermore, it can prioritize analyzing recipes that use specific cooking utensils based on the user's past cooking history. In this way, the optimal analysis method can be selected by referring to the user's past cooking history.

[0054] The recipe analysis unit can analyze recipes while considering the nutritional value of ingredients and allergy information. For example, based on the user's allergy information, it can analyze recipes that do not contain ingredients that cause allergies. It can also prioritize the analysis of recipes that are rich in specific nutrients, taking into account the user's nutritional balance. Furthermore, it can analyze low-calorie or high-protein recipes according to the user's health condition. In this way, by considering the nutritional value of ingredients and allergy information, it can provide recipes that are mindful of the user's health.

[0055] The recipe analysis unit can prioritize analyzing regionally specific ingredients by considering the user's geographical location during recipe analysis. For example, it can prioritize analyzing ingredients that are readily available in the user's area. It can also analyze recipes based on traditional dishes from the user's region. Furthermore, it can prioritize analyzing recipes that use seasonal ingredients from the user's region. In this way, by considering the user's geographical location, it can provide recipes that utilize regionally specific ingredients.

[0056] The time-saving suggestion function can select the most suitable time-saving method by referring to the user's past cooking history when making suggestions. For example, it can suggest a similar time-saving method based on a time-saving recipe that the user has successfully used in the past. It can also suggest a different time-saving method to avoid time-saving recipes that the user has failed at in the past. Furthermore, it can suggest time-saving methods that use specific cooking utensils based on the user's past cooking history. In this way, the optimal time-saving method can be selected by referring to the user's past cooking history.

[0057] The time-saving proposal department can propose time-saving methods while considering the frequency and efficiency of use of cooking utensils. For example, it can propose the most suitable time-saving methods based on the cooking utensils that the user frequently uses. It can also propose the most suitable time-saving methods while considering the efficiency of the cooking utensils the user already owns. Furthermore, it can propose time-saving methods that utilize newly purchased cooking utensils. In this way, by considering the frequency and efficiency of use of cooking utensils, it can propose the most suitable time-saving methods.

[0058] The time-saving suggestion department can propose time-saving methods specific to the user's region, taking into account the user's geographical location. For example, it can suggest time-saving methods using ingredients readily available in the user's area. It can also suggest time-saving methods based on traditional dishes from the user's region. Furthermore, it can suggest time-saving methods using seasonal ingredients from the user's region. In this way, by considering the user's geographical location, it can provide time-saving methods specific to the user's region.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The recipe analysis unit analyzes the recipe and identifies points where time can be saved. The recipe analysis unit analyzes existing recipes, breaks down each step into detail, and identifies which parts can be made more efficient. For example, it can analyze simple preparation and cooking methods such as how to cut ingredients or how to use cooking utensils. The recipe analysis unit can use AI to analyze each step of the recipe and identify points for efficiency improvements. Step 2: The Time-Saving Suggestion Unit proposes time-saving recipes based on the time-saving points identified by the Recipe Analysis Unit. The Time-Saving Suggestion Unit uses a generation AI to generate time-saving recipes based on recipe data. It can generate specific time-saving recipes and propose them to users. The Time-Saving Suggestion Unit can use the generation AI to streamline recipe steps and reduce cooking time. Step 3: The Cost-Saving Suggestion Department suggests alternative ingredients based on the recipes proposed by the Time-Saving Suggestion Department. The Cost-Saving Suggestion Department uses generative AI to suggest replacing expensive ingredients with cheaper alternatives. By suggesting alternative ingredients, it is possible to reduce costs while maintaining flavor. The Cost-Saving Suggestion Department can also use generative AI to suggest healthy and eco-friendly alternative ingredients. Step 4: The feedback collection unit collects user feedback and improves future suggestions. The feedback collection unit learns from users who actually try the suggested recipes and provide feedback on the results, and then incorporates this information into future suggestions. The feedback collection unit can use AI to analyze user feedback and improve the quality of suggestions.

[0061] (Example of form 2) The cooking assistant system according to an embodiment of the present invention is a system that utilizes AI to reduce cooking time and effort, making it possible to easily prepare delicious meals. First, the cooking assistant system uses AI to analyze existing recipes and identify points where time can be saved in each step. Next, the generating AI generates time-saving and cost-saving recipes based on the recipe data and suggests alternative ingredients. Furthermore, it collects feedback from users, which the AI ​​analyzes to improve future suggestions. This mechanism makes it possible to prepare delicious meals quickly and easily, even when busy, efficiently utilize ingredients, and reduce costs. It also improves satisfaction with daily meals by making cooking a more enjoyable habit. For example, the cooking assistant system uses AI to analyze existing recipes and identify points where time can be saved in each step. In this process, each step of the recipe is broken down into smaller parts to identify which parts can be made more efficient. For example, efficiency can be improved by utilizing simpler preparation and cooking methods, such as how to cut ingredients or how to use cooking utensils. Next, the generating AI generates time-saving and cost-saving recipes based on the recipe data. Specifically, it suggests alternative ingredients to reduce costs while maintaining flavor. For example, ingredient costs can be reduced by replacing expensive ingredients with cheaper alternatives. Furthermore, alternative ingredients offer healthy and eco-friendly options. Additionally, user feedback is collected and analyzed by the AI ​​to improve future suggestions. This enhances the quality of suggestions and provides better-quality meals. For example, users can try suggested recipes and provide feedback, allowing the AI ​​to learn from that information and incorporate it into future suggestions. This system enables users to prepare delicious meals quickly and easily, even when busy. Users can utilize ingredients efficiently and reduce costs. They can also improve their satisfaction with daily meals by enjoying the cooking habit. This is effective for a variety of target groups, such as busy homemakers, cooking beginners, and budget-conscious individuals. In this way, AI-powered cooking assistants support users in making cooking easier and smarter.This allows the system to accommodate the efficiency and economical lifestyle demanded in modern society, and to support people who are busy with meal preparation and cooking. As a result, the cooking assistant system enables users to prepare delicious meals quickly and easily.

[0062] The cooking assistant system according to this embodiment comprises a recipe analysis unit, a time-saving suggestion unit, a cost-saving suggestion unit, and a feedback collection unit. The recipe analysis unit analyzes recipes and identifies points where time can be saved. For example, the recipe analysis unit analyzes existing recipes, breaks down each step into smaller parts, and identifies which parts can be made more efficient. For example, the recipe analysis unit can analyze simple preparations and cooking methods, such as how to cut ingredients or how to use cooking utensils. The recipe analysis unit can use AI to analyze each step of a recipe and identify points for efficiency improvements. The time-saving suggestion unit proposes time-saving recipes based on the time-saving points identified by the recipe analysis unit. For example, the time-saving suggestion unit can use generation AI to generate time-saving recipes based on recipe data. For example, the time-saving suggestion unit can generate specific time-saving recipes and propose them to the user. The time-saving suggestion unit can use generation AI to streamline the steps of a recipe and shorten cooking time. The cost-saving suggestion unit proposes alternative ingredients based on the recipe proposed by the time-saving suggestion unit. The cost-saving suggestion unit, for example, uses generative AI to suggest replacing expensive ingredients with cheaper alternatives. For example, the cost-saving suggestion unit can suggest alternative ingredients, reducing costs while maintaining flavor. The cost-saving suggestion unit can also use generative AI to suggest healthy and eco-friendly alternative ingredients. The feedback collection unit collects feedback from users and improves future suggestions. For example, the feedback collection unit learns from users who actually try the suggested recipes and provide feedback on the results, and then incorporates that information into future suggestions. The feedback collection unit can use AI to analyze user feedback and improve the quality of suggestions. As a result, the cooking assistant system according to this embodiment allows users to prepare delicious meals quickly and easily.

[0063] The Recipe Analysis Department analyzes recipes and identifies points where time can be saved. For example, it analyzes existing recipes, breaks down each step into smaller parts, and identifies which parts can be made more efficient. Specifically, the Recipe Analysis Department can analyze simple preparation and cooking methods, such as how to cut ingredients and how to use cooking utensils. For example, regarding how to cut ingredients, it can identify methods for cutting more vegetables at once or cutting them into specific shapes to shorten cooking time. Regarding the use of cooking utensils, it can analyze how to utilize time-saving cooking equipment such as microwave ovens and pressure cookers. The Recipe Analysis Department can use AI to analyze each step of a recipe and identify points for efficiency improvements. The AI ​​uses natural language processing technology to analyze the text of the recipe and evaluate the time and effort required for each step. Furthermore, it can use image recognition technology to analyze images and videos of the cooking process and identify areas where efficiency can be improved. As a result, the Recipe Analysis Department can provide users with specific advice on how to cook efficiently in a short amount of time.

[0064] The Time-Saving Suggestion Department proposes time-saving recipes based on time-saving points identified by the Recipe Analysis Department. For example, the Time-Saving Suggestion Department generates time-saving recipes based on recipe data, using a generative AI. Specifically, the generative AI reconstructs each step of a recipe and proposes the optimal procedure for shortening cooking time. For example, it can suggest methods for preparing ingredients simultaneously or for efficiently reusing cooking utensils. The Time-Saving Suggestion Department can streamline recipe steps and shorten cooking time using the generative AI. The generative AI learns from past recipe data and user feedback to generate optimal time-saving recipes. Furthermore, the Time-Saving Suggestion Department can also customize recipes according to user preferences and cooking environments. For example, for users who do not have a specific cooking utensil, it will suggest an alternative procedure that does not require that utensil. The Time-Saving Suggestion Department can also provide advice to further shorten cooking time according to the user's schedule. In this way, the Time-Saving Suggestion Department helps users cook efficiently and create delicious meals in a short amount of time.

[0065] The Cost-Saving Suggestion Department proposes alternative ingredients based on recipes suggested by the Time-Saving Suggestion Department. For example, the Cost-Saving Suggestion Department uses generative AI to suggest replacing expensive ingredients with cheaper alternatives. Specifically, the generative AI suggests alternative ingredients that reduce costs while maintaining the nutritional value and flavor of the recipe. For example, it can suggest replacing expensive meats with cheaper beans and vegetables, or substituting certain seasonings with other cheaper seasonings. The Cost-Saving Suggestion Department can also use generative AI to suggest healthy and eco-friendly alternative ingredients. The generative AI considers the user's food preferences and allergy information to select the most suitable alternative ingredients. Furthermore, the Cost-Saving Suggestion Department can also suggest ways to further reduce costs by utilizing seasonal ingredients. In addition, the Cost-Saving Suggestion Department also suggests recipes that utilize the ingredients the user already has. This allows users to use ingredients without waste, achieving economical and environmentally friendly cooking.

[0066] The feedback collection unit collects user feedback to improve future suggestions. For example, the feedback collection unit allows users to try suggested recipes and provide feedback on the results, which the AI ​​then learns and incorporates into future suggestions. Specifically, it collects photos and comments of dishes cooked by users, and the AI ​​analyzes them. Based on user feedback, the AI ​​identifies areas for improvement in recipes and points for new suggestions. For example, if a user provides feedback that "this recipe took too long," the AI ​​learns this information and suggests a more efficient recipe next time. Also, if a user provides feedback that "this substitute ingredient lacked flavor," the AI ​​adjusts its suggestions to propose other substitute ingredients. The feedback collection unit can use AI to analyze user feedback and improve the quality of suggestions. Furthermore, the feedback collection unit can provide personalized suggestions tailored to the user's preferences and cooking skills. This allows the feedback collection unit to increase user satisfaction and continuously improve the system.

[0067] The recipe analysis unit can analyze simple preparation and cooking methods, such as how to cut ingredients and how to use cooking utensils. For example, the recipe analysis unit can analyze how to cut ingredients and suggest efficient cutting methods. For instance, it can suggest a method for cutting ingredients evenly. Furthermore, the recipe analysis unit can analyze how to use cooking utensils and suggest efficient usage methods. For example, it can suggest a method to optimize the use of cooking utensils and shorten cooking time. In this way, by analyzing simple preparation and cooking methods, the efficiency of cooking can be improved.

[0068] The time-saving suggestion unit can generate specific time-saving recipes. For example, it can use generation AI to generate time-saving recipes based on recipe data. For example, the time-saving suggestion unit can suggest specific steps to shorten cooking time. It can also suggest efficient cooking methods. For example, it can suggest ways to shorten cooking time by optimizing the use of cooking utensils. In this way, cooking time can be shortened by generating specific time-saving recipes.

[0069] The cost-saving suggestion department can propose replacing expensive ingredients with cheaper alternatives. For example, it can use generative AI to suggest replacements for expensive ingredients with cheaper alternatives. For instance, the cost-saving suggestion department can suggest alternative ingredients, reducing costs while maintaining flavor. Furthermore, the cost-saving suggestion department can also suggest healthy and eco-friendly alternative ingredients. For example, it can suggest highly nutritious alternative ingredients. This allows for a reduction in ingredient costs by replacing expensive ingredients with cheaper alternatives.

[0070] The feedback collection unit can analyze user feedback and incorporate it into future suggestions. For example, the AI ​​learns from users who actually try the suggested recipes and provide feedback on the results, and then incorporates that information into future suggestions. For example, the feedback collection unit can collect user feedback and improve the quality of suggestions. The feedback collection unit can also analyze user feedback and incorporate it into future suggestions. For example, the feedback collection unit can improve the content of suggestions based on user feedback. In this way, the quality of suggestions can be improved by analyzing user feedback.

[0071] The recipe analysis unit can estimate the user's emotions and determine the priority of recipe analysis based on the estimated emotions. For example, if the user is stressed, the recipe analysis unit will prioritize analyzing easy and quick recipes. For example, if the user is relaxed, the recipe analysis unit can prioritize analyzing recipes that can be enjoyed over time. Also, if the user is in a hurry, the recipe analysis unit can prioritize analyzing recipes that can be prepared in the shortest amount of time. In this way, by determining the priority of recipe analysis according to the user's emotions, recipes that meet the user's needs can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is not limited to, but may include, text generation AI (e.g., LLM) or multimodal generation AI.

[0072] The recipe analysis unit can select the optimal analysis method by referring to the user's past cooking history during recipe analysis. For example, the recipe analysis unit can prioritize analyzing similar cooking methods based on recipes the user has successfully made in the past. For example, the recipe analysis unit can analyze different cooking methods to avoid recipes the user has failed at in the past. Furthermore, the recipe analysis unit can prioritize analyzing recipes that use specific cooking utensils based on the user's past cooking history. In this way, the optimal analysis method can be selected by referring to the user's past cooking history.

[0073] The recipe analysis unit can perform recipe analysis while considering the nutritional value of ingredients and allergy information. For example, based on the user's allergy information, the recipe analysis unit can analyze recipes that do not contain ingredients that cause allergies. For example, the recipe analysis unit can consider the user's nutritional balance and prioritize the analysis of recipes that contain a lot of specific nutrients. In addition, the recipe analysis unit can analyze low-calorie or high-protein recipes according to the user's health condition. In this way, by considering the nutritional value of ingredients and allergy information, it can provide recipes that are mindful of the user's health.

[0074] The recipe analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is stressed, the recipe analysis unit can provide a simple and visually easy-to-understand display method. For example, if the user is relaxed, the recipe analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the recipe analysis unit can provide a concise display method that gets straight to the point. In this way, by adjusting the display method of the analysis results according to the user's emotions, it becomes possible to provide a display 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0075] The recipe analysis unit can prioritize the analysis of region-specific ingredients by considering the user's geographical location during recipe analysis. For example, the recipe analysis unit can prioritize the analysis of ingredients that are readily available in the user's region. For instance, the recipe analysis unit can analyze recipes based on traditional dishes from the user's region. Furthermore, the recipe analysis unit can also prioritize the analysis of recipes that use seasonal ingredients from the user's region. This allows the system to provide recipes using region-specific ingredients by considering the user's geographical location.

[0076] The recipe analysis unit can analyze users' social media activity and identify related recipes during recipe analysis. For example, it can analyze related recipes based on recipes shared by users on social media. It can also prioritize analyzing recipes from cooking accounts that users follow. Furthermore, it can analyze similar recipes based on recipes that users have "liked." This allows the system to provide recipes tailored to users' interests by analyzing their social media activity.

[0077] The time-saving suggestion function can estimate the user's emotions and adjust how it suggests time-saving recipes based on those emotions. For example, if the user is stressed, the time-saving suggestion function can suggest a simple and easy time-saving recipe. For example, if the user is relaxed, it can suggest a delicious time-saving recipe that requires a little more effort. Also, if the user is in a hurry, the time-saving suggestion function can suggest the quickest recipe to prepare. In this way, by adjusting how time-saving recipes are suggested according to the user's emotions, the system can provide the user with the most suitable recipe. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The time-saving suggestion function can select the most suitable time-saving method by referring to the user's past cooking history when making suggestions. For example, the time-saving suggestion function can suggest similar time-saving methods based on time-saving recipes that the user has successfully used in the past. For example, the time-saving suggestion function can suggest different time-saving methods to avoid time-saving recipes that the user has failed at in the past. Furthermore, the time-saving suggestion function can also suggest time-saving methods that use specific cooking utensils based on the user's past cooking history. In this way, the optimal time-saving method can be selected by referring to the user's past cooking history.

[0079] The Time-Saving Proposal Department can propose time-saving methods while considering the frequency and efficiency of use of cooking utensils. For example, the Time-Saving Proposal Department can propose the optimal time-saving method based on the cooking utensils that the user frequently uses. For example, the Time-Saving Proposal Department can propose the optimal time-saving method by considering the efficiency of the cooking utensils that the user owns. In addition, the Time-Saving Proposal Department can propose time-saving methods that utilize cooking utensils that the user has newly purchased. In this way, by considering the frequency and efficiency of use of cooking utensils, the optimal time-saving method can be proposed.

[0080] The time-saving suggestion section can estimate the user's emotions and adjust the display method of time-saving recipes based on the estimated emotions. For example, if the user is stressed, the time-saving suggestion section can provide a simple and visually easy-to-understand display method. For example, if the user is relaxed, the time-saving suggestion section can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the time-saving suggestion section can provide a concise display method that gets straight to the point. In this way, by adjusting the display method of time-saving recipes according to the user's emotions, it becomes possible to provide a display 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0081] The time-saving suggestion department can propose time-saving methods specific to the user's region, taking into account the user's geographical location. For example, the department can suggest time-saving methods using ingredients readily available in the user's region. For example, it can suggest time-saving methods based on traditional dishes from the user's region. It can also suggest time-saving methods using seasonal ingredients from the user's region. In this way, by considering the user's geographical location, it can provide time-saving methods specific to the region.

[0082] The Time-Saving Recipe Department can analyze a user's social media activity and suggest relevant time-saving recipes when making time-saving suggestions. For example, the Time-Saving Recipe Department can suggest relevant time-saving recipes based on recipes that the user has shared on social media. For instance, the Time-Saving Recipe Department can prioritize suggesting time-saving recipes from cooking accounts that the user follows. Furthermore, the Time-Saving Recipe Department can suggest similar time-saving recipes based on recipes that the user has "liked." This allows the department to provide time-saving recipes tailored to the user's interests by analyzing their social media activity.

[0083] The savings suggestion unit can estimate the user's emotions and prioritize savings suggestions based on those emotions. For example, if the user is stressed, the savings suggestion unit will prioritize easy and hassle-free savings suggestions. For example, if the user is relaxed, the savings suggestion unit can suggest savings that require a little more effort but still reduce costs. Also, if the user is in a hurry, the savings suggestion unit can suggest the savings that can reduce costs in the shortest amount of time. In this way, by prioritizing savings suggestions according to the user's emotions, the system can provide the most optimal suggestions for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The savings suggestion function can select the most suitable alternative ingredients by referring to the user's past purchase history when making savings suggestions. For example, the savings suggestion function can suggest the most suitable alternative ingredients based on the ingredients the user has purchased in the past. For instance, the savings suggestion function can consider the price of ingredients the user has purchased in the past and suggest alternative ingredients that can reduce costs. Furthermore, the savings suggestion function can also suggest savings recipes that use specific ingredients based on the user's past purchase history. In this way, the optimal alternative ingredients can be selected by referring to the user's past purchase history.

[0085] The savings suggestion function can make suggestions while considering the nutritional value and allergy information of ingredients. For example, based on the user's allergy information, the savings suggestion function can suggest alternative ingredients that do not contain ingredients that cause allergies. For example, the savings suggestion function can consider the user's nutritional balance and suggest alternative ingredients that are rich in specific nutrients. Furthermore, the savings suggestion function can suggest low-calorie or high-protein alternative ingredients depending on the user's health condition. In this way, by considering the nutritional value and allergy information of ingredients, it can make suggestions that take the user's health into consideration.

[0086] The savings suggestion section can estimate the user's emotions and adjust how savings suggestions are displayed based on those emotions. For example, if the user is stressed, the savings suggestion section can provide a simple and visually easy-to-understand display. For example, if the user is relaxed, the savings suggestion section can provide a display that includes detailed information. Furthermore, if the user is in a hurry, the savings suggestion section can provide a concise display that gets straight to the point. By adjusting how savings suggestions are displayed according to the user's emotions, a user-friendly display can be achieved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The cost-saving suggestion function can suggest regionally specific alternative ingredients, taking into account the user's geographical location when making cost-saving suggestions. For example, it can suggest alternative ingredients that are readily available in the user's area. For example, it can suggest alternative ingredients based on traditional dishes from the user's region. It can also suggest alternative ingredients using seasonal ingredients from the user's region. In this way, by considering the user's geographical location, it can provide regionally specific alternative ingredients.

[0088] The savings suggestion department can analyze a user's social media activity and suggest relevant alternative ingredients when making savings suggestions. For example, it can suggest relevant alternative ingredients based on recipes the user has shared on social media. For instance, it can prioritize suggesting alternative ingredients from cooking accounts the user follows. It can also suggest similar alternative ingredients based on recipes the user has "liked." In this way, by analyzing the user's social media activity, it can provide alternative ingredients that match the user's interests.

[0089] The feedback collection unit can estimate the user's emotions and adjust the feedback collection method based on the estimated emotions. For example, if the user is stressed, the feedback collection unit can collect feedback in the form of a simple questionnaire. For example, if the user is relaxed, the feedback collection unit can collect feedback in a format that requests more detailed feedback. Furthermore, if the user is in a hurry, the feedback collection unit can provide a feedback format that can be answered in a short time. In this way, by adjusting the feedback collection method according to the user's emotions, a format that is easy for the user to respond to can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The feedback collection unit can select the optimal collection method by referring to the user's past feedback history when collecting feedback. For example, the feedback collection unit can collect feedback in a similar format to the feedback the user has provided in the past. For example, the feedback collection unit can analyze the content of feedback the user has provided in the past and prioritize collecting specific questions. The feedback collection unit can also request feedback at specific time periods based on the user's past feedback history. In this way, the optimal collection method can be selected by referring to the user's past feedback history.

[0091] The feedback collection unit can estimate the user's emotions and adjust the way feedback is displayed based on the estimated emotions. For example, if the user is stressed, the feedback collection unit can provide a simple and visually easy-to-understand display. For example, if the user is relaxed, the feedback collection unit can provide a display that includes detailed information. Furthermore, if the user is in a hurry, the feedback collection unit can provide a concise display that gets straight to the point. By adjusting the way feedback is displayed according to the user's emotions, it becomes possible to provide a display 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.

[0092] The feedback collection unit can select the optimal collection method when collecting feedback, taking into account the user's device information. For example, if the user is using a smartphone, the feedback collection unit can provide a feedback collection method that is adapted to the screen size. For example, if the user is using a tablet, the feedback collection unit can provide a feedback collection method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the feedback collection unit can provide a concise and highly visible feedback collection method. In this way, the optimal collection method can be provided by taking into account the user's device information.

[0093] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0094] The recipe analysis unit can estimate the user's emotions and prioritize recipe analysis based on those emotions. For example, if the user is stressed, it can prioritize simple and easy recipes. If the user is relaxed, it can prioritize recipes that can be enjoyed at a leisurely pace. Furthermore, if the user is in a hurry, it can prioritize recipes that can be prepared in the shortest time. By prioritizing recipe analysis according to the user's emotions, the system can provide recipes that meet the user's needs.

[0095] The recipe analysis unit can select the optimal analysis method by referring to the user's past cooking history during recipe analysis. For example, it can prioritize analyzing similar cooking methods based on recipes the user has successfully made in the past. It can also analyze different cooking methods to avoid recipes the user has failed at in the past. Furthermore, it can prioritize analyzing recipes that use specific cooking utensils based on the user's past cooking history. In this way, the optimal analysis method can be selected by referring to the user's past cooking history.

[0096] The recipe analysis unit can analyze recipes while considering the nutritional value of ingredients and allergy information. For example, based on the user's allergy information, it can analyze recipes that do not contain ingredients that cause allergies. It can also prioritize the analysis of recipes that are rich in specific nutrients, taking into account the user's nutritional balance. Furthermore, it can analyze low-calorie or high-protein recipes according to the user's health condition. In this way, by considering the nutritional value of ingredients and allergy information, it can provide recipes that are mindful of the user's health.

[0097] The recipe analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on those emotions. For example, if the user is stressed, it can provide a simple and visually easy-to-understand display method. If the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a concise display method that gets straight to the point. In this way, by adjusting the display method of the analysis results according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand.

[0098] The recipe analysis unit can prioritize analyzing regionally specific ingredients by considering the user's geographical location during recipe analysis. For example, it can prioritize analyzing ingredients that are readily available in the user's area. It can also analyze recipes based on traditional dishes from the user's region. Furthermore, it can prioritize analyzing recipes that use seasonal ingredients from the user's region. In this way, by considering the user's geographical location, it can provide recipes that utilize regionally specific ingredients.

[0099] The time-saving recipe suggestion function can estimate the user's emotions and adjust the way time-saving recipes are suggested based on those emotions. For example, if the user is feeling stressed, it can suggest a simple and easy time-saving recipe. If the user is relaxed, it can suggest a delicious time-saving recipe that requires a little more effort. Furthermore, if the user is in a hurry, it can suggest the time-saving recipe that can be prepared in the shortest amount of time. In this way, by adjusting the way time-saving recipes are suggested according to the user's emotions, the system can provide the user with the most suitable recipe.

[0100] The time-saving suggestion function can select the most suitable time-saving method by referring to the user's past cooking history when making suggestions. For example, it can suggest a similar time-saving method based on a time-saving recipe that the user has successfully used in the past. It can also suggest a different time-saving method to avoid time-saving recipes that the user has failed at in the past. Furthermore, it can suggest time-saving methods that use specific cooking utensils based on the user's past cooking history. In this way, the optimal time-saving method can be selected by referring to the user's past cooking history.

[0101] The time-saving proposal department can propose time-saving methods while considering the frequency and efficiency of use of cooking utensils. For example, it can propose the most suitable time-saving methods based on the cooking utensils that the user frequently uses. It can also propose the most suitable time-saving methods while considering the efficiency of the cooking utensils the user already owns. Furthermore, it can propose time-saving methods that utilize newly purchased cooking utensils. In this way, by considering the frequency and efficiency of use of cooking utensils, it can propose the most suitable time-saving methods.

[0102] The time-saving suggestion function can estimate the user's emotions and adjust the display method of time-saving recipes based on those emotions. For example, if the user is stressed, it can provide a simple and visually easy-to-understand display method. If the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a concise display method that gets straight to the point. By adjusting the display method of time-saving recipes according to the user's emotions, it becomes possible to create a display that is easy for the user to understand.

[0103] The time-saving suggestion department can propose time-saving methods specific to the user's region, taking into account the user's geographical location. For example, it can suggest time-saving methods using ingredients readily available in the user's area. It can also suggest time-saving methods based on traditional dishes from the user's region. Furthermore, it can suggest time-saving methods using seasonal ingredients from the user's region. In this way, by considering the user's geographical location, it can provide time-saving methods specific to the user's region.

[0104] The following briefly describes the processing flow for example form 2.

[0105] Step 1: The recipe analysis unit analyzes the recipe and identifies points where time can be saved. The recipe analysis unit analyzes existing recipes, breaks down each step into detail, and identifies which parts can be made more efficient. For example, it can analyze simple preparation and cooking methods such as how to cut ingredients or how to use cooking utensils. The recipe analysis unit can use AI to analyze each step of the recipe and identify points for efficiency improvements. Step 2: The Time-Saving Suggestion Unit proposes time-saving recipes based on the time-saving points identified by the Recipe Analysis Unit. The Time-Saving Suggestion Unit uses a generation AI to generate time-saving recipes based on recipe data. It can generate specific time-saving recipes and propose them to users. The Time-Saving Suggestion Unit can use the generation AI to streamline recipe steps and reduce cooking time. Step 3: The Cost-Saving Suggestion Department suggests alternative ingredients based on the recipes proposed by the Time-Saving Suggestion Department. The Cost-Saving Suggestion Department uses generative AI to suggest replacing expensive ingredients with cheaper alternatives. By suggesting alternative ingredients, it is possible to reduce costs while maintaining flavor. The Cost-Saving Suggestion Department can also use generative AI to suggest healthy and eco-friendly alternative ingredients. Step 4: The feedback collection unit collects user feedback and improves future suggestions. The feedback collection unit learns from users who actually try the suggested recipes and provide feedback on the results, and then incorporates this information into future suggestions. The feedback collection unit can use AI to analyze user feedback and improve the quality of suggestions.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] Each of the multiple elements described above, including the recipe analysis unit, time-saving suggestion unit, cost-saving suggestion unit, and feedback collection unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the recipe analysis unit is implemented by the control unit 46A of the smart device 14, which analyzes existing recipes, breaks down each step into smaller steps, and identifies points for efficiency improvements. The time-saving suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates time-saving recipes using generation AI. The cost-saving suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12, which makes suggestions to replace expensive ingredients with inexpensive substitutes. The feedback collection unit is implemented by the control unit 46A of the smart device 14, which collects feedback from users and improves future suggestions. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0110] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] Each of the multiple elements described above, including the recipe analysis unit, time-saving suggestion unit, cost-saving suggestion unit, and feedback collection unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the recipe analysis unit is implemented by the control unit 46A of the smart glasses 214, which analyzes existing recipes, breaks down each step into smaller steps, and identifies points for efficiency improvements. The time-saving suggestion unit is implemented by the identification processing unit 290 of the data processing unit 12, which generates time-saving recipes using generation AI. The cost-saving suggestion unit is implemented by the identification processing unit 290 of the data processing unit 12, which makes suggestions to replace expensive ingredients with cheaper alternatives. The feedback collection unit is implemented by the control unit 46A of the smart glasses 214, which collects user feedback and improves future suggestions. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0126] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the recipe analysis unit, time-saving suggestion unit, cost-saving suggestion unit, and feedback collection unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the recipe analysis unit is implemented by the control unit 46A of the headset terminal 314, which analyzes existing recipes, breaks down each step into smaller parts, and identifies points for efficiency improvement. The time-saving suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates time-saving recipes using generation AI. The cost-saving suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12, which makes suggestions to replace expensive ingredients with inexpensive substitutes. The feedback collection unit is implemented by the control unit 46A of the headset terminal 314, which collects user feedback and improves future suggestions. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0142] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the recipe analysis unit, time-saving suggestion unit, cost-saving suggestion unit, and feedback collection unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the recipe analysis unit is implemented by the control unit 46A of the robot 414, which analyzes existing recipes, breaks down each process into smaller steps, and identifies points for efficiency improvements. The time-saving suggestion unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which generates time-saving recipes using generation AI. The cost-saving suggestion unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which makes suggestions to replace expensive ingredients with inexpensive substitutes. The feedback collection unit is implemented by, for example, the control unit 46A of the robot 414, which collects feedback from users and improves the next suggestion. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] (Note 1) The recipe analysis department analyzes recipes and identifies points where time can be saved, A time-saving suggestion unit proposes time-saving recipes based on the time-saving points identified by the aforementioned recipe analysis unit, Based on the recipes proposed by the aforementioned time-saving proposal department, the cost-saving proposal department proposes alternative ingredients, It includes a feedback collection unit that collects user feedback and uses it to improve future proposals. A system characterized by the following features. (Note 2) The aforementioned recipe analysis unit, This analyzes simple preparation and cooking methods, such as how to cut ingredients and how to use cooking utensils. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned time-saving proposal department, Generate specific time-saving recipes The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned cost-saving proposal section is, The proposal involves replacing expensive ingredients with cheaper alternatives. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned feedback collection unit is Analyze user feedback and incorporate it into future proposals. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned recipe analysis unit, The system estimates the user's emotions and prioritizes recipe analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned recipe analysis unit, During recipe analysis, the system selects the optimal analysis method by referring to the user's past cooking history. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned recipe analysis unit, When analyzing recipes, the nutritional value and allergy information of the ingredients are taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned recipe analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned recipe analysis unit, When analyzing recipes, the system prioritizes analyzing region-specific ingredients, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned recipe analysis unit, During recipe analysis, the system analyzes users' social media activity and identifies related recipes. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned time-saving proposal department, The system estimates the user's emotions and adjusts how time-saving recipes are suggested based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned time-saving proposal department, When suggesting time-saving methods, the system selects the most suitable method by referring to the user's past cooking history. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned time-saving proposal department, When proposing time-saving solutions, consider the frequency and efficiency of using cooking utensils. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned time-saving proposal department, The system estimates the user's emotions and adjusts how quick recipes are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned time-saving proposal department, When suggesting time-saving solutions, we take the user's geographical location into consideration and propose time-saving methods specific to their region. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned time-saving proposal department, When suggesting time-saving solutions, the system analyzes the user's social media activity and proposes relevant time-saving recipes. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned cost-saving proposal section is, It estimates the user's emotions and prioritizes savings suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned cost-saving proposal section is, When suggesting ways to save money, the system selects the most suitable alternative ingredients by referring to the user's past purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned cost-saving proposal section is, When suggesting ways to save money, take into account the nutritional value of ingredients and allergy information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned cost-saving proposal section is, It estimates the user's emotions and adjusts how savings suggestions are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned cost-saving proposal section is, When suggesting ways to save money, the system takes the user's geographical location into account and suggests alternative ingredients specific to their region. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned cost-saving proposal section is, When suggesting ways to save money, the system analyzes the user's social media activity and suggests relevant alternative ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned feedback collection unit is We estimate the user's emotions and adjust the feedback collection method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback collection unit is When collecting feedback, the system selects the optimal collection method by referring to the user's past feedback history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback collection unit is It estimates the user's emotions and adjusts how feedback is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback collection unit is When collecting feedback, the optimal collection method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0178] 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. The recipe analysis department analyzes recipes and identifies points where time can be saved, A time-saving suggestion unit proposes time-saving recipes based on the time-saving points identified by the aforementioned recipe analysis unit, Based on the recipes proposed by the aforementioned time-saving proposal department, the cost-saving proposal department proposes alternative ingredients, It includes a feedback collection unit that collects user feedback and uses it to improve future proposals. A system characterized by the following features.

2. The aforementioned recipe analysis unit, This analyzes simple preparation and cooking methods, such as how to cut ingredients and how to use cooking utensils. The system according to feature 1.

3. The aforementioned time-saving proposal department, Generate specific time-saving recipes The system according to feature 1.

4. The aforementioned cost-saving proposal section is, The proposal involves replacing expensive ingredients with cheaper alternatives. The system according to feature 1.

5. The aforementioned feedback collection unit is Analyze user feedback and incorporate it into future proposals. The system according to feature 1.

6. The aforementioned recipe analysis unit, The system estimates the user's emotions and prioritizes recipe analysis based on those estimated emotions. The system according to feature 1.

7. The aforementioned recipe analysis unit, During recipe analysis, the system selects the optimal analysis method by referring to the user's past cooking history. The system according to feature 1.

8. The aforementioned recipe analysis unit, When analyzing recipes, the nutritional value and allergy information of the ingredients are taken into consideration. The system according to feature 1.

9. The aforementioned recipe analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system according to feature 1.

10. The aforementioned recipe analysis unit, When analyzing recipes, the system prioritizes analyzing region-specific ingredients, taking into account the user's geographical location. The system according to feature 1.

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