System

Abstract

To provide a system which flexibly deals with failure and missing ingredients during cooking.SOLUTION: A system includes an acquisition part, an analysis part, a provision part, a registration part, a detection part, and a guidance part. The acquisition part acquires an image. The analysis part analyzes the image acquired by the acquisition part. The provision part provides advices based on a result analyzed by the analysis part. The registration part registers a user's preferences and allergy information. The detection part detects cooking failure. The guidance part performs voice guidance.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the disadvantage that it is difficult to respond flexibly to mistakes during cooking or shortages of ingredients.

[0005] The system according to the embodiment aims to flexibly respond to mistakes during cooking or shortages of ingredients. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, a provision unit, a registration unit, a detection unit, and a guidance unit. The acquisition unit acquires an image. The analysis unit analyzes the image acquired by the acquisition unit. The provision unit provides advice based on the results of the analysis by the analysis unit. The registration unit registers user preferences and allergy information. The detection unit detects cooking failures. The guidance unit provides audio guidance. Effect of the Invention

[0007] The system according to the embodiment can flexibly respond to mistakes during cooking or shortages of ingredients. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

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

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

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The cooking advice system according to the embodiment of the present invention is a system that uses image recognition and voice guidance to give advice on cooking. Unlike conventional cooking methods that require cooking according to a recipe, this system can flexibly respond to cases where ingredients are insufficient or different ingredients are used. It can also respond to constraints such as user preferences and allergies. First, when a user starts cooking, the system acquires an image of the cooking process through a camera. Using image recognition technology, the system analyzes the current cooking situation and provides necessary advice. For example, if ingredients are insufficient or different ingredients are used, alternative ingredients are suggested. In addition, by registering the user's preferences and allergy information in advance, advice based on the information can be provided. Furthermore, the system detects points where cooking may fail and issues a voice warning. For example, if the heat is too high or the ingredients are cut inappropriately, appropriate measures are provided by voice. In the unlikely event that cooking fails, the system contributes to reducing food waste by advising on recovery methods. This system eliminates the need for users to cook according to recipes, allowing for high-degree of freedom in cooking. In addition, the system prevents cooking failures and allows appropriate measures to be taken even if the cooking fails, reducing the stress of cooking. Furthermore, it is an environmentally friendly system because it contributes to reducing food waste. For example, when a user starts cooking, the system captures images of the cooking process through a camera. Using image recognition technology, the system analyzes the current cooking status and provides necessary advice. For example, if ingredients are in short supply or different ingredients are used, it will suggest alternative ingredients. In addition, by registering the user's preferences and allergy information in advance, it is possible to provide advice based on that. Furthermore, the system detects areas where cooking may fail and issues a voice warning. For example, if the heat is too high or the ingredients are cut inappropriately, it will provide voice guidance on the appropriate way to deal with the problem. In the unlikely event that cooking fails, it will contribute to reducing food waste by advising on how to recover. With this system, users no longer need to follow a recipe, allowing for greater freedom in cooking. In addition, the system prevents cooking failures and allows appropriate measures to be taken even if the cooking fails, reducing the stress of cooking.Furthermore, since it contributes to reducing food waste, it is an environmentally friendly system. As a result, the cooking advice system supports users in cooking, prevents mistakes before they happen, and contributes to reducing food waste.

[0029] The cooking advice system according to the embodiment includes an acquisition unit, an analysis unit, a provision unit, a registration unit, a detection unit, and a guidance unit. When a user starts cooking, the acquisition unit acquires an image of the cooking process through a camera. For example, the acquisition unit can acquire images of the cooking process in real time using a camera installed on the top of the cooking utensil. The acquisition unit can also use multiple cameras to simultaneously acquire images from different angles. For example, cameras are installed on the top and sides of the cooking table to simultaneously acquire images from different angles. The analysis unit analyzes the images acquired by the acquisition unit. For example, the analysis unit can analyze the state of ingredients and cooking utensils during cooking using image recognition technology. The analysis unit can identify ingredients during cooking and analyze their state using, for example, an object detection algorithm. The analysis unit can also analyze the state of cooking utensils using pattern recognition technology. The provision unit provides advice based on the results analyzed by the analysis unit. For example, the provision unit can suggest alternative ingredients when ingredients are in short supply or different ingredients are used. The provision unit can also provide appropriate advice based on the user's preferences and allergy information. The registration unit registers the user's preferences and allergy information. For example, the registration unit stores the preferences and allergy information input by the user in advance in a database, and provides advice based on the information. The detection unit detects cooking failures. For example, the detection unit can detect signs of failure during cooking using image recognition technology. For example, when the heat is too high or the ingredients are cut inappropriately, the detection unit detects it and provides an appropriate countermeasure. The guidance unit provides audio guidance. For example, the guidance unit can detect points where failure may occur during cooking and issue an audio warning. The guidance unit can also provide audio guidance on how to recover from cooking failures. As a result, the cooking advice system according to the embodiment can support the user's cooking, prevent failures before they occur, and contribute to reducing food waste.

[0030] When the user starts cooking, the acquisition unit acquires images during cooking through the camera. For example, the acquisition unit can acquire images during cooking in real time using a camera installed on the top of the cooking utensil. Specifically, the camera has high resolution and can capture detailed actions during cooking and the state of ingredients. The acquisition unit can also use multiple cameras to simultaneously acquire images from different angles. For example, cameras are installed on the top and sides of the cooking table to simultaneously acquire images from different angles. This allows the overall picture during cooking to be grasped and the actions the user is performing to be accurately recorded. Furthermore, the acquisition unit has a function of automatically adjusting the position and angle of the camera, and can provide the optimal viewpoint according to the user's movements. For example, even if the user moves to the edge of the cooking table, the camera automatically follows and can always acquire the optimal image. This allows the acquisition unit to record the user's cooking actions in detail and provide high-quality data to the analysis unit.

[0031] The analysis unit analyzes the image acquired by the acquisition unit. For example, the analysis unit can use image recognition technology to analyze the state of ingredients and cooking utensils during cooking. Specifically, the analysis unit uses an object detection algorithm using deep learning to identify ingredients during cooking and analyze their state. For example, it analyzes how vegetables are cut and how well meat is cooked to determine the appropriate cooking method. The analysis unit can also analyze the state of cooking utensils using pattern recognition technology. For example, it analyzes the temperature of a frying pan and the state of liquid in a pot to determine the appropriate heat and cooking time. Furthermore, the analysis unit can learn past cooking data and understand the user's cooking patterns and preferences. This allows the analysis unit to provide advice tailored to the user's cooking style. For example, it can suggest optimal recipes based on ingredients and cooking methods that the user has used in the past. This allows the analysis unit to support the user's cooking and achieve more efficient and effective cooking.

[0032] The providing unit provides advice based on the results of the analysis by the analyzing unit. For example, the providing unit can suggest alternative ingredients when ingredients are insufficient or different ingredients are used. Specifically, the providing unit analyzes the types and amounts of ingredients used by the user, and suggests alternative ingredients when necessary ingredients are insufficient. For example, even if an ingredient required for a recipe is not on hand, the providing unit suggests an alternative ingredient with a similar flavor or texture, and supports the user to continue cooking smoothly. The providing unit can also provide appropriate advice based on the user's preferences and allergy information. For example, if the user is allergic to a specific ingredient, a recipe that does not use that ingredient is suggested. The providing unit can also provide advice to adjust the amount of seasonings and cooking time according to the user's preferences. In this way, the providing unit can provide advice tailored to the individual needs of the user, achieving a more satisfying cooking experience.

[0033] The registration unit registers the user's preferences and allergy information. For example, the registration unit stores the preferences and allergy information input by the user in advance in a database, and provides advice based on the preferences and allergy information. Specifically, the user inputs his / her preferences and allergy information through an application, and the registration unit stores the information in a database. This allows the provision unit to provide advice tailored to the individual needs of the user. For example, if the user prefers to use a specific ingredient, the provision unit preferentially suggests recipes using that ingredient. Also, if the user is allergic to a specific ingredient, the provision unit suggests recipes that do not use that ingredient. Furthermore, the registration unit can record the user's cooking history and provide advice based on past cooking data. For example, the provision unit provides advice that is useful when the user makes a similar dish again based on the history of dishes made by the user in the past. This allows the registration unit to centrally manage the user's preferences and allergy information and provide more personalized advice.

[0034] The detection unit detects cooking failures. For example, the detection unit can detect signs of failure during cooking using image recognition technology. Specifically, the detection unit detects abnormal conditions during cooking using an anomaly detection algorithm using deep learning. For example, if the heat is too strong or the ingredients are cut inappropriately, the detection unit detects it and provides an appropriate countermeasure. Furthermore, the detection unit can analyze voice data during cooking and detect abnormal sounds. For example, it can detect burning sounds or boiling sounds and issue a warning before cooking fails. In this way, the detection unit prevents failures during cooking and supports the user to proceed with cooking smoothly. In addition, the detection unit can learn past cooking data and detect abnormalities based on the user's cooking pattern. In this way, the detection unit can detect abnormalities according to the individual cooking style of the user and provide more accurate support.

[0035] The guidance unit provides voice guidance. For example, the guidance unit can detect a part where cooking may fail and issue a voice warning. Specifically, the guidance unit provides voice guidance to the user in real time based on information from the analysis unit and the detection unit. For example, if the heat is too high, the guidance unit issues a voice warning saying "Turn down the heat" and guides the user on an appropriate method of dealing with the problem. The guidance unit can also provide voice guidance on a recovery method in the event of a cooking failure. For example, if an ingredient is burnt, the guidance unit provides voice guidance on a specific recovery method such as "Remove the burnt part". Furthermore, the guidance unit can also provide voice guidance on the next step according to the user's cooking progress. For example, the guidance unit provides voice guidance on specific instructions such as "Next, add vegetables" to support the user so that the user can proceed with cooking smoothly. In this way, the guidance unit can support the user's cooking in real time, prevent failures, and achieve more efficient and effective cooking.

[0036] The cooking advice system includes a suggestion unit that suggests alternative ingredients. The suggestion unit can suggest alternative ingredients when an ingredient is insufficient or when a different ingredient is used. For example, the suggestion unit suggests appropriate alternative ingredients based on similarity in nutritional value or taste. The suggestion unit can, for example, input a list of ingredients that the user has, and suggest alternative ingredients based on the list. The suggestion unit can also suggest alternative ingredients based on the user's preferences and allergy information. For example, the suggestion unit suggests alternative ingredients to avoid ingredients to which the user has an allergy. This can also be used when an ingredient is insufficient or when a different ingredient is used. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input a list of ingredients that the user has into AI, and the AI ​​can suggest appropriate alternative ingredients.

[0037] The cooking advice system includes a recovery unit that provides a recovery method when cooking fails. The recovery unit can provide a recovery method when cooking fails. For example, the recovery unit suggests a method for correcting the cooking process or adding ingredients. For example, the recovery unit can suggest a method for removing burnt food when burnt food occurs. The recovery unit can also suggest an appropriate method for dealing with excessive heating when cooking fails. For example, the recovery unit suggests a method for adjusting the heat when excessive heating occurs. This contributes to reducing food loss by providing a recovery method when cooking fails. Some or all of the above-mentioned processing in the recovery unit may be performed using, for example, AI, or may be performed without using AI. For example, the recovery unit can input the situation of the cooking failure to AI, which can then suggest an appropriate recovery method.

[0038] The cooking advice system includes a reduction unit that provides advice for reducing food waste. The reduction unit can provide advice for reducing food waste. For example, the reduction unit suggests a method for preserving ingredients and a recipe for reusing ingredients. The reduction unit can suggest, for example, a method for preserving leftover ingredients. The reduction unit can also suggest a recipe for reusing leftover ingredients. For example, the reduction unit suggests a recipe for soup using leftover vegetables. This contributes to reducing food waste. Some or all of the above-mentioned processing in the reduction unit may be performed using, for example, AI, or may be performed without using AI. For example, the reduction unit can input a list of leftover ingredients into AI, which can suggest appropriate preservation methods and recipes for reusing ingredients.

[0039] The acquisition unit can acquire images during cooking through a camera. The acquisition unit can acquire images during cooking in real time, for example, using a camera installed on the top of the cooking utensil. The acquisition unit can also use multiple cameras to simultaneously acquire images from different angles. For example, cameras are installed on the top and sides of the cooking table to simultaneously acquire images from different angles. This makes it possible to provide real-time advice by acquiring images during cooking. Some or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input images acquired by the camera into AI, which can then analyze the images.

[0040] The analysis unit can analyze the cooking status using image recognition technology. The analysis unit can, for example, use an object detection algorithm to identify ingredients being cooked and analyze their status. The analysis unit can also analyze the status of cooking utensils using pattern recognition technology. For example, the analysis unit identifies the type and amount of ingredients being cooked and analyzes their status. As a result, the analysis accuracy of the cooking status is improved by using image recognition technology. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input image data to AI, which analyzes the image.

[0041] The guidance unit can issue a voice warning when the heat is too strong or the ingredients are cut inappropriately. The guidance unit can issue a voice warning when the heat is too strong, for example. For example, the guidance unit monitors the heat during cooking, and when the heat is too strong, the guidance unit issues a voice warning saying "Turn down the heat." The guidance unit can also issue a voice warning when the ingredients are cut inappropriately. For example, when the ingredients are cut too large, the guidance unit issues a voice warning saying "Cut the ingredients a little finer." This can prevent mistakes during cooking. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the guidance unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the guidance unit can input data on the heat to an AI, which analyzes the heat and issues a warning.

[0042] The acquisition unit can simultaneously acquire images from different angles using multiple cameras, enabling more detailed analysis. The acquisition unit can, for example, install cameras on the top and sides of the cooking counter and simultaneously acquire images from different angles. The acquisition unit can also install cameras on the top and sides of a pot to check the state inside the pot and acquire images simultaneously. For example, the acquisition unit can use multiple cameras to acquire images from different angles to check the state of a cooking utensil. This makes it possible to perform detailed analysis by using multiple cameras. Some or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input image data acquired by multiple cameras into AI, and the AI ​​can analyze the images.

[0043] The acquisition unit can also simultaneously acquire environmental data such as temperature and humidity during cooking and provide it to the analysis unit. The acquisition unit can, for example, measure the temperature during cooking with a sensor and provide it to the analysis unit together with an image. The acquisition unit can also measure the humidity during cooking with a sensor and provide it to the analysis unit together with an image. For example, the acquisition unit can simultaneously acquire environmental data (temperature, humidity, illuminance, etc.) during cooking and provide it to the analysis unit. This allows for more accurate analysis by acquiring environmental data simultaneously. Some or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI or may be performed without using AI. For example, the acquisition unit can input temperature and humidity data to AI, which can analyze the environmental data.

[0044] The acquisition unit can acquire an image of a specific cooking process in response to a user's voice instruction. For example, when the user instructs "take a picture of the current process," the acquisition unit can acquire an image of the process. In addition, when the user instructs "take a picture of the next process," the acquisition unit can also acquire an image of the process. For example, when the user instructs "take a picture of the state of this ingredient," the acquisition unit can acquire an image of the ingredient. In this way, by acquiring an image in response to the user's voice instruction, necessary information can be accurately acquired. Part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the user's voice instruction to AI, and the AI ​​can acquire an image based on the instruction.

[0045] The acquisition unit can acquire the state of the cooking utensil in real time and provide it to the analysis unit. The acquisition unit can, for example, acquire the state inside a pot in real time and provide it to the analysis unit. The acquisition unit can also acquire the state inside a frying pan in real time and provide it to the analysis unit. For example, the acquisition unit can acquire the state inside an oven in real time and provide it to the analysis unit. This makes it possible to provide more appropriate advice by acquiring the state of the cooking utensil in real time. Some or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input state data of the cooking utensil to AI, which can analyze the state.

[0046] The analysis unit can provide more accurate advice by analyzing the environmental data provided by the acquisition unit in addition to the image data. For example, the analysis unit can analyze temperature data together with the image data and provide advice on appropriate heat settings. The analysis unit can also analyze humidity data together with the image data and provide advice on appropriate cooking methods. For example, the analysis unit can analyze illuminance data together with the image data and provide advice on appropriate cooking environments. This allows for more accurate advice by analyzing environmental data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input environmental data to AI, which then analyzes the data and provides advice.

[0047] The analysis unit can improve the accuracy of the analysis by referring to past cooking data and comparing it with the current cooking situation. The analysis unit can, for example, refer to past cooking data and compare it with the current cooking situation to provide appropriate advice. The analysis unit can also detect the possibility of failure by referring to past cooking data and comparing it with the current cooking situation. For example, the analysis unit can suggest an optimal cooking method by referring to past cooking data and comparing it with the current cooking situation. Thereby, the accuracy of the analysis is improved by referring to the past cooking data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input past cooking data to AI, which can analyze the data and provide advice.

[0048] The analysis unit can adjust the level of detail of the analysis according to the cooking skill level of the user. For example, the analysis unit can provide a basic analysis result to a novice user. The analysis unit can also provide a detailed analysis result to an intermediate user. For example, the analysis unit can provide a specialized analysis result to an advanced user. This allows more appropriate advice to be given by adjusting the level of detail of the analysis according to the cooking skill level of the user. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the cooking skill level of the user to the AI, and the AI ​​can adjust the level of detail of the analysis.

[0049] The analysis unit can switch the analysis algorithm according to different types of cuisine. For example, in the case of Japanese cuisine, the analysis unit can use an analysis algorithm dedicated to Japanese cuisine. Also, in the case of Western cuisine, the analysis unit can use an analysis algorithm dedicated to Western cuisine. For example, in the case of Chinese cuisine, the analysis unit can use an analysis algorithm dedicated to Chinese cuisine. This allows for more appropriate advice by switching the analysis algorithm according to different types of cuisine. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the type of cuisine to AI, which can select an appropriate analysis algorithm.

[0050] The providing unit can provide advice that is individually customized by referring to the user's past cooking history. The providing unit can provide advice that matches the user's preferences by referring to the user's past cooking history, for example. The providing unit can also provide advice based on allergy information by referring to the user's past cooking history. For example, the providing unit can provide advice according to the user's cooking skill by referring to the user's past cooking history. This makes it possible to provide advice that is individually customized by referring to the past cooking history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the past cooking history into AI, which can analyze the data and provide advice.

[0051] The providing unit can receive the user's feedback in real time and instantly modify the advice content. For example, when the user provides feedback on the advice, the providing unit can instantly modify the advice content. In addition, when the user asks a question about the advice, the providing unit can also instantly provide an answer. For example, when the user expresses dissatisfaction with the advice, the providing unit can instantly propose a remedy. This allows for more appropriate advice by reflecting the feedback in real time. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's feedback to AI, and the AI ​​can modify the advice content based on the feedback.

[0052] The providing unit can select the optimal display method according to the user's device. For example, when the user is using a smartphone, the providing unit can provide a display method that matches the screen size. In addition, when the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. For example, when the user is using a smart watch, the providing unit can provide a simple and highly visible display method. This improves visibility by providing the optimal display method according to the user's device. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's device information into AI, and the AI ​​can select the optimal display method.

[0053] The providing unit can provide advice in multiple languages ​​according to the language setting of the user. The providing unit can automatically set the language of the advice based on, for example, the language setting of the user's device. In addition, the providing unit can provide a language switching function when the user uses multiple languages. For example, when the user selects a specific language, the providing unit can provide advice in that language. This allows the user to receive advice in a language that is easy to understand due to multilingual support. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's language setting to AI, and the AI ​​can provide advice in an appropriate language.

[0054] The registration unit can refer to the user's past meal history and automatically update the preference and allergy information. The registration unit can, for example, refer to the user's past meal history and automatically update the preference information. The registration unit can also refer to the user's past meal history and automatically update the allergy information. For example, the registration unit can refer to the user's past meal history and automatically update the food ingredient preferences. As a result, the information is updated automatically by referring to the past meal history. Some or all of the above-mentioned processing in the registration unit may be performed, for example, using AI or may be performed without using AI. For example, the registration unit can input the past meal history into AI, which analyzes the data and updates the information.

[0055] The registration unit can also enable the user to collectively manage the preferences and allergy information of the user's family and housemates. For example, the registration unit can provide an interface that allows the user to collectively register the preferences and allergy information of the family. The registration unit can also provide a function that allows the user to collectively manage the preferences and allergy information of the housemates. For example, the registration unit can enable the user to individually register the information of the family and housemates and collectively manage it as necessary. This collectively manages the information of the family and housemates, making it easier to manage information when cooking. Some or all of the above-mentioned processing in the registration unit may be performed, for example, using AI, or may be performed without using AI. For example, the registration unit can input the information of the family and housemates into AI, and the AI ​​can analyze the data and manage the information.

[0056] The registration unit can register preferences and allergy information in response to a user's voice instruction. For example, when a user instructs "register your favorite ingredients," the registration unit can register the information. In addition, when a user instructs "register allergy information," the registration unit can also register the information. For example, when a user instructs "register family preferences," the registration unit can register the information. This improves user convenience by registering information in response to a voice instruction. A part or all of the above-mentioned processing in the registration unit may be performed, for example, using AI, or may be performed without using AI. For example, the registration unit can input a user's voice instruction to AI, and the AI ​​can register information based on the instruction.

[0057] The registration unit can analyze the user's social media activity and automatically update the preference and allergy information. The registration unit can, for example, analyze the user's social media posts and automatically update the preference information. The registration unit can also analyze the user's social media posts and automatically update the allergy information. For example, the registration unit can analyze the user's social media activity and automatically update the food ingredient preferences. In this way, the information update is automated by analyzing the social media activity. A part or all of the above-mentioned processing in the registration unit may be performed, for example, using AI or may be performed without using AI. For example, the registration unit can input social media data into AI, which analyzes the data and updates the information.

[0058] The detection unit can refer to past cooking failure data and compare it with the current cooking situation to increase the possibility of failure. The detection unit can, for example, refer to past cooking failure data and compare it with the current cooking situation to detect the possibility of failure. The detection unit can also refer to past cooking failure data and compare it with the current cooking situation to provide an appropriate countermeasure. For example, the detection unit can refer to past cooking failure data and compare it with the current cooking situation to detect signs of failure early. As a result, by referring to the past failure data, the possibility of failure can be increased. A part or all of the above-mentioned processing in the detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the detection unit can input past cooking failure data to AI, and the AI ​​can analyze the data to detect the possibility of failure.

[0059] The detection unit can also analyze voice data during cooking and detect signs of failure early. The detection unit can, for example, analyze voice data during cooking and detect signs of failure early. The detection unit can also analyze voice data during cooking and provide an appropriate countermeasure. For example, the detection unit can analyze voice data during cooking to increase the possibility of failure. In this way, signs of failure can be detected early by analyzing the voice data. A part or all of the above-mentioned processing in the detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the detection unit can input voice data during cooking to AI, which can analyze the data and detect signs of failure.

[0060] The detection unit can adjust the sensitivity of failure detection according to the cooking skill level of the user. For example, the detection unit can provide a highly sensitive failure detection to a novice user. The detection unit can also provide a moderately sensitive failure detection to an intermediate user. For example, the detection unit can provide a less sensitive failure detection to an advanced user. This allows for more appropriate failure detection by adjusting the sensitivity according to the cooking skill level. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the detection unit can input the cooking skill level of the user to the AI, and the AI ​​can adjust the sensitivity.

[0061] The detection unit can switch the failure detection algorithm according to different types of cuisine. For example, in the case of Japanese cuisine, the detection unit can use a failure detection algorithm dedicated to Japanese cuisine. Also, in the case of Western cuisine, the detection unit can use a failure detection algorithm dedicated to Western cuisine. For example, in the case of Chinese cuisine, the detection unit can use a failure detection algorithm dedicated to Chinese cuisine. This allows for more appropriate failure detection by switching the algorithm according to the type of cuisine. Some or all of the above-mentioned processing in the detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the detection unit can input the type of cuisine to the AI, which can select an appropriate failure detection algorithm.

[0062] The guidance unit can provide individually customized voice guidance by referring to the user's past cooking history. The guidance unit can provide, for example, voice guidance tailored to the user's preferences by referring to the user's past cooking history. The guidance unit can also provide voice guidance based on allergy information by referring to the user's past cooking history. For example, the guidance unit can provide voice guidance according to the user's cooking skill by referring to the user's past cooking history. This makes it possible to provide individually customized voice guidance by referring to the past cooking history. A part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the guidance unit can input the past cooking history into AI, which analyzes the data and provides voice guidance.

[0063] The guidance unit can receive the user's feedback in real time and instantly modify the voice guidance content. For example, when the user provides feedback on the voice guidance, the guidance unit can instantly modify the guidance content. In addition, when the user asks a question about the voice guidance, the guidance unit can instantly provide an answer. For example, when the user expresses dissatisfaction with the voice guidance, the guidance unit can instantly propose a remedy. This enables more appropriate voice guidance by reflecting the feedback in real time. A part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the guidance unit can input the user's feedback to AI, and the AI ​​can modify the voice guidance content based on the feedback.

[0064] The guidance unit can select the optimal voice guidance method according to the user's device. For example, when the user is using a smart speaker, the guidance unit can provide voice guidance optimized for the device. In addition, when the user is using a smartphone, the guidance unit can also provide voice guidance optimized for the device. For example, when the user is using a tablet, the guidance unit can provide voice guidance optimized for the device. This improves the user's convenience by providing the optimal voice guidance method according to the device. A part or all of the above-mentioned processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit can input the user's device information into AI, and the AI ​​can select the optimal voice guidance method.

[0065] The guidance unit can provide voice guidance in multiple languages ​​according to the language setting of the user. The guidance unit can automatically set the language of the voice guidance based on the language setting of the user's device, for example. The guidance unit can also provide a language switching function when the user uses multiple languages. For example, when the user selects a specific language, the guidance unit can provide voice guidance in that language. This allows the user to receive voice guidance in a language that is easy for the user to understand due to multilingual support. A part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the guidance unit can input the user's language setting into AI, and the AI ​​can provide voice guidance in an appropriate language.

[0066] The suggestion unit can refer to the user's past cooking history and suggest alternative ingredients that are individually customized. The suggestion unit can, for example, refer to the user's past cooking history and suggest alternative ingredients that match the user's preferences. The suggestion unit can also refer to the user's past cooking history and suggest alternative ingredients based on allergy information. For example, the suggestion unit can refer to the user's past cooking history and suggest alternative ingredients according to the user's cooking skill. This makes it possible to suggest individually customized alternative ingredients by referring to the past cooking history. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the past cooking history into AI, which analyzes the data and suggests alternative ingredients.

[0067] The suggestion unit can receive the user's feedback in real time and instantly revise the proposed content of the alternative material. For example, when the user provides feedback on the proposed alternative material, the suggestion unit can instantly revise the proposed content. In addition, when the user asks a question on the proposed alternative material, the suggestion unit can instantly provide an answer. For example, when the user expresses dissatisfaction with the proposed alternative material, the suggestion unit can instantly propose an improvement measure. This makes it possible to propose a more appropriate alternative material by reflecting the feedback in real time. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the user's feedback to AI, and the AI ​​can revise the proposed content based on the feedback.

[0068] The suggestion unit can select the optimal display method according to the user's device. For example, when the user is using a smartphone, the suggestion unit can provide a display method that matches the screen size. In addition, when the user is using a tablet, the suggestion unit can also provide a display method optimized for a large screen. For example, when the user is using a smart watch, the suggestion unit can provide a simple and highly visible display method. This improves visibility by providing the optimal display method according to the device. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's device information into AI, and the AI ​​can select the optimal display method.

[0069] The suggestion unit can suggest alternative materials in multiple languages ​​according to the language setting of the user. The suggestion unit can automatically set the language of the alternative materials based on, for example, the language setting of the user's device. The suggestion unit can also provide a language switching function when the user uses multiple languages. For example, when the user selects a specific language, the suggestion unit can suggest alternative materials in that language. This allows the user to receive suggestions of alternative materials in a language that is easy for the user to understand due to the multilingual support. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the user's language setting into AI, which can then suggest alternative materials in an appropriate language.

[0070] The recovery unit can provide an individually customized recovery method by referring to the user's past cooking failure history. The recovery unit can provide a recovery method for a similar failure by referring to the user's past cooking failure history, for example. The recovery unit can also provide a recovery method tailored to the user's preferences by referring to the user's past cooking failure history. For example, the recovery unit can provide a recovery method based on allergy information by referring to the user's past cooking failure history. This makes it possible to provide an individually customized recovery method by referring to the past cooking failure history. Some or all of the above-mentioned processing in the recovery unit may be performed, for example, using AI, or may be performed without using AI. For example, the recovery unit can input the past cooking failure history into AI, which analyzes the data and provides a recovery method.

[0071] The recovery unit can receive user feedback in real time and instantly modify the recovery method. For example, when the user provides feedback on the recovery method, the recovery unit can instantly modify the method. In addition, when the user asks a question about the recovery method, the recovery unit can instantly provide an answer. For example, when the user expresses dissatisfaction with the recovery method, the recovery unit can instantly propose an improvement measure. This enables a more appropriate recovery method by reflecting the feedback in real time. A part or all of the above-mentioned processing in the recovery unit may be performed, for example, using AI, or may be performed without using AI. For example, the recovery unit can input the user's feedback to AI, and the AI ​​can modify the recovery method based on the feedback.

[0072] The recovery unit can select the optimal display method according to the user's device. For example, when the user is using a smartphone, the recovery unit can provide a display method that matches the screen size. In addition, when the user is using a tablet, the recovery unit can also provide a display method optimized for a large screen. For example, when the user is using a smart watch, the recovery unit can provide a simple and highly visible display method. This improves visibility by providing the optimal display method according to the device. A part or all of the above-mentioned processing in the recovery unit may be performed, for example, using AI, or may be performed without using AI. For example, the recovery unit can input the user's device information into AI, and the AI ​​can select the optimal display method.

[0073] The recovery unit can provide the recovery method in multiple languages ​​according to the language setting of the user. The recovery unit can automatically set the language of the recovery method based on the language setting of the user's device, for example. The recovery unit can also provide a language switching function when the user uses multiple languages. For example, when the user selects a specific language, the recovery unit can provide the recovery method in that language. This allows the user to receive the recovery method in a language that is easy for the user to understand due to the multilingual support. Some or all of the above-mentioned processing in the recovery unit may be performed, for example, using AI, or may be performed without using AI. For example, the recovery unit can input the user's language setting into AI, and the AI ​​can provide the recovery method in an appropriate language.

[0074] The reduction unit can provide individually customized food loss reduction advice by referring to the user's past consumption history. The reduction unit can provide food loss reduction advice tailored to the user's preferences by referring to the user's past consumption history, for example. The reduction unit can also provide food loss reduction advice based on allergy information by referring to the user's past consumption history. For example, the reduction unit can provide food loss reduction advice according to the user's consumption pattern by referring to the user's past consumption history. This makes it possible to provide individually customized food loss reduction advice by referring to the past consumption history. Some or all of the above-mentioned processing in the reduction unit may be performed, for example, using AI, or may be performed without using AI. For example, the reduction unit can input the past consumption history into AI, which analyzes the data and provides advice.

[0075] The reduction unit can receive user feedback in real time and instantly revise the food loss reduction advice. For example, when the user provides feedback on the food loss reduction advice, the reduction unit can instantly revise the advice content. In addition, when the user asks a question about the food loss reduction advice, the reduction unit can also instantly provide an answer. For example, when the user expresses dissatisfaction with the food loss reduction advice, the reduction unit can instantly suggest improvement measures. This enables more appropriate food loss reduction advice by reflecting feedback in real time. Some or all of the above-mentioned processing in the reduction unit may be performed, for example, using AI, or may be performed without using AI. For example, the reduction unit can input user feedback to AI, and the AI ​​can revise the advice content based on the feedback.

[0076] The reduction unit can select the optimal display method according to the user's device. For example, when the user is using a smartphone, the reduction unit can provide a display method that matches the screen size. In addition, when the user is using a tablet, the reduction unit can also provide a display method optimized for a large screen. For example, when the user is using a smart watch, the reduction unit can provide a simple display method with high visibility. This improves visibility by providing the optimal display method according to the device. A part or all of the above-mentioned processing in the reduction unit may be performed, for example, using AI, or may be performed without using AI. For example, the reduction unit can input the user's device information into AI, and the AI ​​can select the optimal display method.

[0077] The reduction unit can provide food loss reduction advice in multiple languages ​​according to the user's language settings. The reduction unit can automatically set the language of the food loss reduction advice based on the language settings of the user's device, for example. The reduction unit can also provide a language switching function when the user uses multiple languages. For example, when the user selects a specific language, the reduction unit can provide food loss reduction advice in that language. This allows the user to receive food loss reduction advice in a language that is easy to understand due to multilingual support. Some or all of the above-mentioned processing in the reduction unit may be performed, for example, using AI, or may be performed without using AI. For example, the reduction unit can input the user's language settings into the AI, and the AI ​​can provide food loss reduction advice in an appropriate language.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The cooking advice system can adjust the level of detail of advice according to the user's cooking skill level. For example, basic cooking procedures and precautions can be explained in detail to novice users. More advanced techniques and tips can be provided to intermediate users. Furthermore, specialized advice and new recipe suggestions can be given to advanced users. This provides appropriate advice according to the user's skill level, improving cooking efficiency and satisfaction.

[0080] The recovery unit can refer to the user's past cooking failure history and provide an individually customized recovery method. For example, if the user has burned something in the past, it can suggest ways to remove the burnt parts and measures to prevent burning. Also, if the failure was caused by overheating, it can suggest an appropriate way to adjust the heat. Furthermore, it can suggest the optimal recovery method based on the user's preferences and allergy information. This enables effective recovery by learning from past failures.

[0081] The acquisition unit can acquire images of specific cooking steps in response to voice instructions from the user. For example, if the user instructs "Take a picture of the current step," an image of that step can be acquired. Also, if the user instructs "Take a picture of the next step," an image of that step can be acquired. Furthermore, if the user instructs "Take a picture of the state of this ingredient," an image of that ingredient can be acquired. This allows necessary information to be acquired accurately in response to voice instructions from the user.

[0082] The guidance unit can provide individually customized voice guidance by referring to the user's past cooking history. For example, if the user has experience cooking a particular dish in the past, voice guidance related to that dish can be provided. Also, appropriate voice guidance can be provided based on the user's preferences and allergy information. Furthermore, basic guidance for beginners and specialized guidance for advanced cooks can be provided according to the user's cooking skills. This makes it possible to provide appropriate voice guidance according to the user's needs.

[0083] The analysis unit can improve the accuracy of the analysis by referring to past cooking data and comparing it with the current cooking situation. For example, it can provide appropriate advice by referring to past cooking data and comparing it with the current cooking situation. It can also detect the possibility of failure by referring to past cooking data and comparing it with the current cooking situation. Furthermore, it can suggest the optimal cooking method by referring to past cooking data and comparing it with the current cooking situation. This enables highly accurate analysis using past data.

[0084] The detection unit can also analyze voice data during cooking to detect early signs of failure. For example, by analyzing voice data during cooking and detecting sounds of burning or boiling, it can grasp early signs of failure. It can also analyze voice data to provide appropriate countermeasures. Furthermore, it can analyze voice data to identify factors that increase the possibility of failure and suggest preventive measures. This makes it possible to detect failure early using voice data.

[0085] The process flow of the first embodiment will be briefly described below.

[0086] Step 1: When a user starts cooking, the acquisition unit acquires images of the cooking process through a camera. For example, the acquisition unit can acquire images of the cooking process in real time using a camera installed on the top of a cooking utensil. The acquisition unit can also use multiple cameras to simultaneously acquire images from different angles. For example, cameras are installed on the top and sides of a cooking table to simultaneously acquire images from different angles. Step 2: The analysis unit analyzes the image acquired by the acquisition unit. For example, the analysis unit can use image recognition technology to analyze the state of ingredients and cooking utensils during cooking. For example, the analysis unit can use an object detection algorithm to identify ingredients during cooking and analyze their state. The analysis unit can also use pattern recognition technology to analyze the state of cooking utensils. Step 3: The providing unit provides advice based on the results of the analysis by the analyzing unit. For example, the providing unit can suggest alternative ingredients when an ingredient is in short supply or a different ingredient is used. The providing unit can also provide appropriate advice based on the user's preferences and allergy information. Step 4: The registration unit registers the user's preferences and allergy information. For example, the registration unit stores the preferences and allergy information previously input by the user in a database and provides advice based on the information. Step 5: The detection unit detects cooking failures. For example, the detection unit can use image recognition technology to detect signs of failure during cooking. For example, if the heat is too high or the ingredients are not cut properly, the detection unit can detect this and provide appropriate countermeasures. Step 6: The guidance unit provides voice guidance. For example, the guidance unit can detect points that may cause failure during cooking and issue a voice warning. The guidance unit can also provide voice guidance on how to recover from a cooking failure.

[0087] (Example 2) The cooking advice system according to the embodiment of the present invention is a system that uses image recognition and voice guidance to give advice on cooking. Unlike conventional cooking methods that require cooking according to a recipe, this system can flexibly respond to cases where ingredients are insufficient or different ingredients are used. It can also respond to constraints such as user preferences and allergies. First, when a user starts cooking, the system acquires an image of the cooking process through a camera. Using image recognition technology, the system analyzes the current cooking situation and provides necessary advice. For example, if ingredients are insufficient or different ingredients are used, alternative ingredients are suggested. In addition, by registering the user's preferences and allergy information in advance, advice based on the information can be provided. Furthermore, the system detects points where cooking may fail and issues a voice warning. For example, if the heat is too high or the ingredients are cut inappropriately, appropriate measures are provided by voice. In the unlikely event that cooking fails, the system contributes to reducing food waste by advising on recovery methods. This system eliminates the need for users to cook according to recipes, allowing for high-degree of freedom in cooking. In addition, the system prevents cooking failures and allows appropriate measures to be taken even if the cooking fails, reducing the stress of cooking. Furthermore, it is an environmentally friendly system because it contributes to reducing food waste. For example, when a user starts cooking, the system captures images of the cooking process through a camera. Using image recognition technology, the system analyzes the current cooking status and provides necessary advice. For example, if ingredients are in short supply or different ingredients are used, it will suggest alternative ingredients. In addition, by registering the user's preferences and allergy information in advance, it is possible to provide advice based on that. Furthermore, the system detects areas where cooking may fail and issues a voice warning. For example, if the heat is too high or the ingredients are cut inappropriately, it will provide voice guidance on the appropriate way to deal with the problem. In the unlikely event that cooking fails, it will contribute to reducing food waste by advising on how to recover. With this system, users no longer need to follow a recipe, allowing for greater freedom in cooking. In addition, the system prevents cooking failures and allows appropriate measures to be taken even if the cooking fails, reducing the stress of cooking.Furthermore, since it contributes to reducing food waste, it is an environmentally friendly system. As a result, the cooking advice system supports users in cooking, prevents mistakes before they happen, and contributes to reducing food waste.

[0088] The cooking advice system according to the embodiment includes an acquisition unit, an analysis unit, a provision unit, a registration unit, a detection unit, and a guidance unit. When a user starts cooking, the acquisition unit acquires an image of the cooking process through a camera. For example, the acquisition unit can acquire images of the cooking process in real time using a camera installed on the top of the cooking utensil. The acquisition unit can also use multiple cameras to simultaneously acquire images from different angles. For example, cameras are installed on the top and sides of the cooking table to simultaneously acquire images from different angles. The analysis unit analyzes the images acquired by the acquisition unit. For example, the analysis unit can analyze the state of ingredients and cooking utensils during cooking using image recognition technology. The analysis unit can identify ingredients during cooking and analyze their state using, for example, an object detection algorithm. The analysis unit can also analyze the state of cooking utensils using pattern recognition technology. The provision unit provides advice based on the results analyzed by the analysis unit. For example, the provision unit can suggest alternative ingredients when ingredients are in short supply or different ingredients are used. The provision unit can also provide appropriate advice based on the user's preferences and allergy information. The registration unit registers the user's preferences and allergy information. For example, the registration unit stores the preferences and allergy information input by the user in advance in a database, and provides advice based on the information. The detection unit detects cooking failures. For example, the detection unit can detect signs of failure during cooking using image recognition technology. For example, when the heat is too high or the ingredients are cut inappropriately, the detection unit detects it and provides an appropriate countermeasure. The guidance unit provides audio guidance. For example, the guidance unit can detect points where failure may occur during cooking and issue an audio warning. The guidance unit can also provide audio guidance on how to recover from cooking failures. As a result, the cooking advice system according to the embodiment can support the user's cooking, prevent failures before they occur, and contribute to reducing food waste.

[0089] When the user starts cooking, the acquisition unit acquires images during cooking through the camera. For example, the acquisition unit can acquire images during cooking in real time using a camera installed on the top of the cooking utensil. Specifically, the camera has high resolution and can capture detailed actions during cooking and the state of ingredients. The acquisition unit can also use multiple cameras to simultaneously acquire images from different angles. For example, cameras are installed on the top and sides of the cooking table to simultaneously acquire images from different angles. This allows the overall picture during cooking to be grasped and the actions the user is performing to be accurately recorded. Furthermore, the acquisition unit has a function of automatically adjusting the position and angle of the camera, and can provide the optimal viewpoint according to the user's movements. For example, even if the user moves to the edge of the cooking table, the camera automatically follows and can always acquire the optimal image. This allows the acquisition unit to record the user's cooking actions in detail and provide high-quality data to the analysis unit.

[0090] The analysis unit analyzes the image acquired by the acquisition unit. For example, the analysis unit can use image recognition technology to analyze the state of ingredients and cooking utensils during cooking. Specifically, the analysis unit uses an object detection algorithm using deep learning to identify ingredients during cooking and analyze their state. For example, it analyzes how vegetables are cut and how well meat is cooked to determine the appropriate cooking method. The analysis unit can also analyze the state of cooking utensils using pattern recognition technology. For example, it analyzes the temperature of a frying pan and the state of liquid in a pot to determine the appropriate heat and cooking time. Furthermore, the analysis unit can learn past cooking data and understand the user's cooking patterns and preferences. This allows the analysis unit to provide advice tailored to the user's cooking style. For example, it can suggest optimal recipes based on ingredients and cooking methods that the user has used in the past. This allows the analysis unit to support the user's cooking and achieve more efficient and effective cooking.

[0091] The providing unit provides advice based on the results of the analysis by the analyzing unit. For example, the providing unit can suggest alternative ingredients when ingredients are insufficient or different ingredients are used. Specifically, the providing unit analyzes the types and amounts of ingredients used by the user, and suggests alternative ingredients when necessary ingredients are insufficient. For example, even if an ingredient required for a recipe is not on hand, the providing unit suggests an alternative ingredient with a similar flavor or texture, and supports the user to continue cooking smoothly. The providing unit can also provide appropriate advice based on the user's preferences and allergy information. For example, if the user is allergic to a specific ingredient, a recipe that does not use that ingredient is suggested. The providing unit can also provide advice to adjust the amount of seasonings and cooking time according to the user's preferences. In this way, the providing unit can provide advice tailored to the individual needs of the user, achieving a more satisfying cooking experience.

[0092] The registration unit registers the user's preferences and allergy information. For example, the registration unit stores the preferences and allergy information input by the user in advance in a database, and provides advice based on the preferences and allergy information. Specifically, the user inputs his / her preferences and allergy information through an application, and the registration unit stores the information in a database. This allows the provision unit to provide advice tailored to the individual needs of the user. For example, if the user prefers to use a specific ingredient, the provision unit preferentially suggests recipes using that ingredient. Also, if the user is allergic to a specific ingredient, the provision unit suggests recipes that do not use that ingredient. Furthermore, the registration unit can record the user's cooking history and provide advice based on past cooking data. For example, the provision unit provides advice that is useful when the user makes a similar dish again based on the history of dishes made by the user in the past. This allows the registration unit to centrally manage the user's preferences and allergy information and provide more personalized advice.

[0093] The detection unit detects cooking failures. For example, the detection unit can detect signs of failure during cooking using image recognition technology. Specifically, the detection unit detects abnormal conditions during cooking using an anomaly detection algorithm using deep learning. For example, if the heat is too strong or the ingredients are cut inappropriately, the detection unit detects it and provides an appropriate countermeasure. Furthermore, the detection unit can analyze voice data during cooking and detect abnormal sounds. For example, it can detect burning sounds or boiling sounds and issue a warning before cooking fails. In this way, the detection unit prevents failures during cooking and supports the user to proceed with cooking smoothly. In addition, the detection unit can learn past cooking data and detect abnormalities based on the user's cooking pattern. In this way, the detection unit can detect abnormalities according to the individual cooking style of the user and provide more accurate support.

[0094] The guidance unit provides voice guidance. For example, the guidance unit can detect a part where cooking may fail and issue a voice warning. Specifically, the guidance unit provides voice guidance to the user in real time based on information from the analysis unit and the detection unit. For example, if the heat is too high, the guidance unit issues a voice warning saying "Turn down the heat" and guides the user on an appropriate method of dealing with the problem. The guidance unit can also provide voice guidance on a recovery method in the event of a cooking failure. For example, if an ingredient is burnt, the guidance unit provides voice guidance on a specific recovery method such as "Remove the burnt part". Furthermore, the guidance unit can also provide voice guidance on the next step according to the user's cooking progress. For example, the guidance unit provides voice guidance on specific instructions such as "Next, add vegetables" to support the user so that the user can proceed with cooking smoothly. In this way, the guidance unit can support the user's cooking in real time, prevent failures, and achieve more efficient and effective cooking.

[0095] The cooking advice system includes a suggestion unit that suggests alternative ingredients. The suggestion unit can suggest alternative ingredients when an ingredient is insufficient or when a different ingredient is used. For example, the suggestion unit suggests appropriate alternative ingredients based on similarity in nutritional value or taste. The suggestion unit can, for example, input a list of ingredients that the user has, and suggest alternative ingredients based on the list. The suggestion unit can also suggest alternative ingredients based on the user's preferences and allergy information. For example, the suggestion unit suggests alternative ingredients to avoid ingredients to which the user has an allergy. This can also be used when an ingredient is insufficient or when a different ingredient is used. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input a list of ingredients that the user has into AI, and the AI ​​can suggest appropriate alternative ingredients.

[0096] The cooking advice system includes a recovery unit that provides a recovery method when cooking fails. The recovery unit can provide a recovery method when cooking fails. For example, the recovery unit suggests a method for correcting the cooking process or adding ingredients. For example, the recovery unit can suggest a method for removing burnt food when burnt food occurs. The recovery unit can also suggest an appropriate method for dealing with excessive heating when cooking fails. For example, the recovery unit suggests a method for adjusting the heat when excessive heating occurs. This contributes to reducing food loss by providing a recovery method when cooking fails. Some or all of the above-mentioned processing in the recovery unit may be performed using, for example, AI, or may be performed without using AI. For example, the recovery unit can input the situation of the cooking failure to AI, which can then suggest an appropriate recovery method.

[0097] The cooking advice system includes a reduction unit that provides advice for reducing food waste. The reduction unit can provide advice for reducing food waste. For example, the reduction unit suggests a method for preserving ingredients and a recipe for reusing ingredients. The reduction unit can suggest, for example, a method for preserving leftover ingredients. The reduction unit can also suggest a recipe for reusing leftover ingredients. For example, the reduction unit suggests a recipe for soup using leftover vegetables. This contributes to reducing food waste. Some or all of the above-mentioned processing in the reduction unit may be performed using, for example, AI, or may be performed without using AI. For example, the reduction unit can input a list of leftover ingredients into AI, which can suggest appropriate preservation methods and recipes for reusing ingredients.

[0098] The acquisition unit can acquire images during cooking through a camera. The acquisition unit can acquire images during cooking in real time, for example, using a camera installed on the top of the cooking utensil. The acquisition unit can also use multiple cameras to simultaneously acquire images from different angles. For example, cameras are installed on the top and sides of the cooking table to simultaneously acquire images from different angles. This makes it possible to provide real-time advice by acquiring images during cooking. Some or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input images acquired by the camera into AI, which can then analyze the images.

[0099] The analysis unit can analyze the cooking status using image recognition technology. The analysis unit can, for example, use an object detection algorithm to identify ingredients being cooked and analyze their status. The analysis unit can also analyze the status of cooking utensils using pattern recognition technology. For example, the analysis unit identifies the type and amount of ingredients being cooked and analyzes their status. As a result, the analysis accuracy of the cooking status is improved by using image recognition technology. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input image data to AI, which analyzes the image.

[0100] The guidance unit can issue a voice warning when the heat is too strong or the ingredients are cut inappropriately. The guidance unit can issue a voice warning when the heat is too strong, for example. For example, the guidance unit monitors the heat during cooking, and when the heat is too strong, the guidance unit issues a voice warning saying "Turn down the heat." The guidance unit can also issue a voice warning when the ingredients are cut inappropriately. For example, when the ingredients are cut too large, the guidance unit issues a voice warning saying "Cut the ingredients a little finer." This can prevent mistakes during cooking. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the guidance unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the guidance unit can input data on the heat to an AI, which analyzes the heat and issues a warning.

[0101] The acquisition unit can estimate the user's emotion and adjust the timing of image acquisition based on the estimated user's emotion. For example, when the user is nervous, the acquisition unit can frequently acquire images and provide detailed advice. In addition, when the user is relaxed, the acquisition unit can reduce the frequency of image acquisition and acquire images only when necessary. For example, when the user is in a hurry, images of important cooking processes can be acquired preferentially. This allows more appropriate advice to be provided by adjusting the timing of image acquisition according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's facial expression data to an AI, and the AI ​​can estimate the user's emotion and adjust the timing of image acquisition.

[0102] The acquisition unit can simultaneously acquire images from different angles using multiple cameras, enabling more detailed analysis. The acquisition unit can, for example, install cameras on the top and sides of the cooking counter and simultaneously acquire images from different angles. The acquisition unit can also install cameras on the top and sides of a pot to check the state inside the pot and acquire images simultaneously. For example, the acquisition unit can use multiple cameras to acquire images from different angles to check the state of a cooking utensil. This makes it possible to perform detailed analysis by using multiple cameras. Some or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input image data acquired by multiple cameras into AI, and the AI ​​can analyze the images.

[0103] The acquisition unit can also simultaneously acquire environmental data such as temperature and humidity during cooking and provide it to the analysis unit. The acquisition unit can, for example, measure the temperature during cooking with a sensor and provide it to the analysis unit together with an image. The acquisition unit can also measure the humidity during cooking with a sensor and provide it to the analysis unit together with an image. For example, the acquisition unit can simultaneously acquire environmental data (temperature, humidity, illuminance, etc.) during cooking and provide it to the analysis unit. This allows for more accurate analysis by acquiring environmental data simultaneously. Some or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI or may be performed without using AI. For example, the acquisition unit can input temperature and humidity data to AI, which can analyze the environmental data.

[0104] The acquisition unit can estimate the user's emotion and determine the priority of images to be acquired based on the estimated user's emotion. For example, when the user is nervous, the acquisition unit can preferentially acquire images of important cooking steps. In addition, when the user is relaxed, the acquisition unit can also preferentially acquire images for grasping the overall flow. For example, when the user is in a hurry, images of time-consuming steps can be preferentially acquired. This enables more appropriate advice by determining the priority of images according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's facial expression data to an AI, and the AI ​​can estimate the user's emotion and determine the priority of images.

[0105] The acquisition unit can acquire an image of a specific cooking process in response to a user's voice instruction. For example, when the user instructs "take a picture of the current process," the acquisition unit can acquire an image of the process. In addition, when the user instructs "take a picture of the next process," the acquisition unit can also acquire an image of the process. For example, when the user instructs "take a picture of the state of this ingredient," the acquisition unit can acquire an image of the ingredient. In this way, by acquiring an image in response to the user's voice instruction, necessary information can be accurately acquired. Part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the user's voice instruction to AI, and the AI ​​can acquire an image based on the instruction.

[0106] The acquisition unit can acquire the state of the cooking utensil in real time and provide it to the analysis unit. The acquisition unit can, for example, acquire the state inside a pot in real time and provide it to the analysis unit. The acquisition unit can also acquire the state inside a frying pan in real time and provide it to the analysis unit. For example, the acquisition unit can acquire the state inside an oven in real time and provide it to the analysis unit. This makes it possible to provide more appropriate advice by acquiring the state of the cooking utensil in real time. Some or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input state data of the cooking utensil to AI, which can analyze the state.

[0107] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis result based on the estimated user's emotion. For example, when the user is nervous, the analysis unit can provide a simple and easy-to-understand expression method. In addition, when the user is relaxed, the analysis unit can also provide an expression method including detailed information. For example, when the user is in a hurry, an expression method that focuses on the main points can be provided. This allows for more understandable advice by adjusting the expression method of the analysis result according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the analysis unit can input the user's facial expression data to an AI, which can estimate the user's emotion and adjust the expression method of the analysis result.

[0108] The analysis unit can provide more accurate advice by analyzing the environmental data provided by the acquisition unit in addition to the image data. For example, the analysis unit can analyze temperature data together with the image data and provide advice on appropriate heat settings. The analysis unit can also analyze humidity data together with the image data and provide advice on appropriate cooking methods. For example, the analysis unit can analyze illuminance data together with the image data and provide advice on appropriate cooking environments. This allows for more accurate advice by analyzing environmental data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input environmental data to AI, which then analyzes the data and provides advice.

[0109] The analysis unit can improve the accuracy of the analysis by referring to past cooking data and comparing it with the current cooking situation. The analysis unit can, for example, refer to past cooking data and compare it with the current cooking situation to provide appropriate advice. The analysis unit can also detect the possibility of failure by referring to past cooking data and comparing it with the current cooking situation. For example, the analysis unit can suggest an optimal cooking method by referring to past cooking data and comparing it with the current cooking situation. Thereby, the accuracy of the analysis is improved by referring to the past cooking data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input past cooking data to AI, which can analyze the data and provide advice.

[0110] The analysis unit can estimate the user's emotion and determine the priority of the analysis results based on the estimated user's emotion. For example, when the user is nervous, the analysis unit can provide important analysis results with priority. The analysis unit can also provide the entire analysis result when the user is relaxed. For example, when the user is in a hurry, time-consuming analysis results can be provided with priority. This enables more appropriate advice by determining the priority of the analysis results according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the analysis unit can input the user's facial expression data to an AI, and the AI ​​can estimate the user's emotion and determine the priority of the analysis results.

[0111] The analysis unit can adjust the level of detail of the analysis according to the cooking skill level of the user. For example, the analysis unit can provide a basic analysis result to a novice user. The analysis unit can also provide a detailed analysis result to an intermediate user. For example, the analysis unit can provide a specialized analysis result to an advanced user. This allows more appropriate advice to be given by adjusting the level of detail of the analysis according to the cooking skill level of the user. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the cooking skill level of the user to the AI, and the AI ​​can adjust the level of detail of the analysis.

[0112] The analysis unit can switch the analysis algorithm according to different types of cuisine. For example, in the case of Japanese cuisine, the analysis unit can use an analysis algorithm dedicated to Japanese cuisine. Also, in the case of Western cuisine, the analysis unit can use an analysis algorithm dedicated to Western cuisine. For example, in the case of Chinese cuisine, the analysis unit can use an analysis algorithm dedicated to Chinese cuisine. This allows for more appropriate advice by switching the analysis algorithm according to different types of cuisine. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the type of cuisine to AI, which can select an appropriate analysis algorithm.

[0113] The providing unit can estimate the user's emotion and adjust the way of expressing advice based on the estimated user's emotion. For example, when the user is nervous, the providing unit can provide simple and easy-to-understand advice. In addition, when the user is relaxed, the providing unit can also provide detailed advice. For example, when the user is in a hurry, advice that covers the main points can be provided. This allows for more understandable advice by adjusting the way of expressing advice according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the providing unit can input the user's facial expression data to the AI, and the AI ​​can estimate the user's emotion and adjust the way of expressing advice.

[0114] The providing unit can provide advice that is individually customized by referring to the user's past cooking history. The providing unit can provide advice that matches the user's preferences by referring to the user's past cooking history, for example. The providing unit can also provide advice based on allergy information by referring to the user's past cooking history. For example, the providing unit can provide advice according to the user's cooking skill by referring to the user's past cooking history. This makes it possible to provide advice that is individually customized by referring to the past cooking history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the past cooking history into AI, which can analyze the data and provide advice.

[0115] The providing unit can receive the user's feedback in real time and instantly modify the advice content. For example, when the user provides feedback on the advice, the providing unit can instantly modify the advice content. In addition, when the user asks a question about the advice, the providing unit can also instantly provide an answer. For example, when the user expresses dissatisfaction with the advice, the providing unit can instantly propose a remedy. This allows for more appropriate advice by reflecting the feedback in real time. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's feedback to AI, and the AI ​​can modify the advice content based on the feedback.

[0116] The providing unit can estimate the user's emotion and determine the priority of advice based on the estimated user's emotion. For example, when the user is nervous, the providing unit can provide important advice with priority. In addition, when the user is relaxed, the providing unit can also provide overall advice. For example, when the user is in a hurry, time-consuming advice can be provided with priority. This enables more appropriate advice by determining the priority of advice according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the providing unit can input the user's facial expression data to the AI, and the AI ​​can estimate the user's emotion and determine the priority of advice.

[0117] The providing unit can select the optimal display method according to the user's device. For example, when the user is using a smartphone, the providing unit can provide a display method that matches the screen size. In addition, when the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. For example, when the user is using a smart watch, the providing unit can provide a simple and highly visible display method. This improves visibility by providing the optimal display method according to the user's device. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's device information into AI, and the AI ​​can select the optimal display method.

[0118] The providing unit can provide advice in multiple languages ​​according to the language setting of the user. The providing unit can automatically set the language of the advice based on, for example, the language setting of the user's device. In addition, the providing unit can provide a language switching function when the user uses multiple languages. For example, when the user selects a specific language, the providing unit can provide advice in that language. This allows the user to receive advice in a language that is easy to understand due to multilingual support. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's language setting to AI, and the AI ​​can provide advice in an appropriate language.

[0119] The registration unit can estimate the user's emotion and adjust the registration method of preference and allergy information based on the estimated user's emotion. For example, when the user is nervous, the registration unit can provide a simple registration method. Also, when the user is relaxed, the registration unit can provide detailed registration options. For example, when the user is in a hurry, voice input can be prioritized to enable quick registration. This allows smoother information registration by adjusting the registration method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the registration unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the registration unit can input the user's facial expression data to the AI, and the AI ​​can estimate the user's emotion and adjust the registration method.

[0120] The registration unit can refer to the user's past meal history and automatically update the preference and allergy information. The registration unit can, for example, refer to the user's past meal history and automatically update the preference information. The registration unit can also refer to the user's past meal history and automatically update the allergy information. For example, the registration unit can refer to the user's past meal history and automatically update the food ingredient preferences. As a result, the information is updated automatically by referring to the past meal history. Some or all of the above-mentioned processing in the registration unit may be performed, for example, using AI or may be performed without using AI. For example, the registration unit can input the past meal history into AI, which analyzes the data and updates the information.

[0121] The registration unit can also enable the user to collectively manage the preferences and allergy information of the user's family and housemates. For example, the registration unit can provide an interface that allows the user to collectively register the preferences and allergy information of the family. The registration unit can also provide a function that allows the user to collectively manage the preferences and allergy information of the housemates. For example, the registration unit can enable the user to individually register the information of the family and housemates and collectively manage it as necessary. This collectively manages the information of the family and housemates, making it easier to manage information when cooking. Some or all of the above-mentioned processing in the registration unit may be performed, for example, using AI, or may be performed without using AI. For example, the registration unit can input the information of the family and housemates into AI, and the AI ​​can analyze the data and manage the information.

[0122] The registration unit can estimate the user's emotion and determine the priority of the registered information based on the estimated user's emotion. For example, when the user is nervous, the registration unit can preferentially register important information. In addition, when the user is relaxed, the registration unit can also register the entire information. For example, when the user is in a hurry, time-consuming information can be preferentially registered. In this way, important information can be preferentially registered by determining the priority of the registered information according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the registration unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the registration unit can input the user's facial expression data to an AI, and the AI ​​can estimate the user's emotion and determine the priority of the registered information.

[0123] The registration unit can register preferences and allergy information in response to a user's voice instruction. For example, when a user instructs "register your favorite ingredients," the registration unit can register the information. In addition, when a user instructs "register allergy information," the registration unit can also register the information. For example, when a user instructs "register family preferences," the registration unit can register the information. This improves user convenience by registering information in response to a voice instruction. A part or all of the above-mentioned processing in the registration unit may be performed, for example, using AI, or may be performed without using AI. For example, the registration unit can input a user's voice instruction to AI, and the AI ​​can register information based on the instruction.

[0124] The registration unit can analyze the user's social media activity and automatically update the preference and allergy information. The registration unit can, for example, analyze the user's social media posts and automatically update the preference information. The registration unit can also analyze the user's social media posts and automatically update the allergy information. For example, the registration unit can analyze the user's social media activity and automatically update the food ingredient preferences. In this way, the information update is automated by analyzing the social media activity. A part or all of the above-mentioned processing in the registration unit may be performed, for example, using AI or may be performed without using AI. For example, the registration unit can input social media data into AI, which analyzes the data and updates the information.

[0125] The detection unit can estimate the user's emotion and adjust the cooking failure detection method based on the estimated user's emotion. For example, the detection unit can provide a detailed detection method when the user is nervous. The detection unit can also provide a simple detection method when the user is relaxed. For example, when the user is in a hurry, the detection of important failures can be prioritized. This allows for more appropriate failure detection by adjusting the detection method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the detection unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the detection unit can input the user's facial expression data to the AI, and the AI ​​can estimate the user's emotion and adjust the detection method.

[0126] The detection unit can refer to past cooking failure data and compare it with the current cooking situation to increase the possibility of failure. The detection unit can, for example, refer to past cooking failure data and compare it with the current cooking situation to detect the possibility of failure. The detection unit can also refer to past cooking failure data and compare it with the current cooking situation to provide an appropriate countermeasure. For example, the detection unit can refer to past cooking failure data and compare it with the current cooking situation to detect signs of failure early. As a result, by referring to the past failure data, the possibility of failure can be increased. A part or all of the above-mentioned processing in the detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the detection unit can input past cooking failure data to AI, and the AI ​​can analyze the data to detect the possibility of failure.

[0127] The detection unit can also analyze voice data during cooking and detect signs of failure early. The detection unit can, for example, analyze voice data during cooking and detect signs of failure early. The detection unit can also analyze voice data during cooking and provide an appropriate countermeasure. For example, the detection unit can analyze voice data during cooking to increase the possibility of failure. In this way, signs of failure can be detected early by analyzing the voice data. A part or all of the above-mentioned processing in the detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the detection unit can input voice data during cooking to AI, which can analyze the data and detect signs of failure.

[0128] The detection unit can estimate the user's emotion and determine the priority of failure detection based on the estimated user's emotion. For example, when the user is nervous, the detection unit can prioritize detection of important failures. The detection unit can also detect overall failures when the user is relaxed. For example, when the user is in a hurry, the detection of time-consuming failures can be prioritized. In this way, important failures can be detected preferentially by determining the priority of failure detection according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the detection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the detection unit can input the user's facial expression data to an AI, and the AI ​​can estimate the user's emotion to determine the priority of failure detection.

[0129] The detection unit can adjust the sensitivity of failure detection according to the cooking skill level of the user. For example, the detection unit can provide a highly sensitive failure detection to a novice user. The detection unit can also provide a moderately sensitive failure detection to an intermediate user. For example, the detection unit can provide a less sensitive failure detection to an advanced user. This allows for more appropriate failure detection by adjusting the sensitivity according to the cooking skill level. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the detection unit can input the cooking skill level of the user to the AI, and the AI ​​can adjust the sensitivity.

[0130] The detection unit can switch the failure detection algorithm according to different types of cuisine. For example, in the case of Japanese cuisine, the detection unit can use a failure detection algorithm dedicated to Japanese cuisine. Also, in the case of Western cuisine, the detection unit can use a failure detection algorithm dedicated to Western cuisine. For example, in the case of Chinese cuisine, the detection unit can use a failure detection algorithm dedicated to Chinese cuisine. This allows for more appropriate failure detection by switching the algorithm according to the type of cuisine. Some or all of the above-mentioned processing in the detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the detection unit can input the type of cuisine to the AI, which can select an appropriate failure detection algorithm.

[0131] The guidance unit can estimate the user's emotion and adjust the expression method of the voice guidance based on the estimated user's emotion. For example, when the user is nervous, the guidance unit can provide guidance in a calm voice. Also, when the user is relaxed, the guidance unit can provide guidance in a bright voice. For example, when the user is in a hurry, quick and concise voice guidance can be provided. This allows for more understandable guidance by adjusting the expression method of the voice guidance according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the guidance unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the guidance unit can input the user's facial expression data into the AI, and the AI ​​can estimate the user's emotion and adjust the expression method of the voice guidance.

[0132] The guidance unit can provide individually customized voice guidance by referring to the user's past cooking history. The guidance unit can provide, for example, voice guidance tailored to the user's preferences by referring to the user's past cooking history. The guidance unit can also provide voice guidance based on allergy information by referring to the user's past cooking history. For example, the guidance unit can provide voice guidance according to the user's cooking skill by referring to the user's past cooking history. This makes it possible to provide individually customized voice guidance by referring to the past cooking history. A part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the guidance unit can input the past cooking history into AI, which analyzes the data and provides voice guidance.

[0133] The guidance unit can receive the user's feedback in real time and instantly modify the voice guidance content. For example, when the user provides feedback on the voice guidance, the guidance unit can instantly modify the guidance content. In addition, when the user asks a question about the voice guidance, the guidance unit can instantly provide an answer. For example, when the user expresses dissatisfaction with the voice guidance, the guidance unit can instantly propose a remedy. This enables more appropriate voice guidance by reflecting the feedback in real time. A part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the guidance unit can input the user's feedback to AI, and the AI ​​can modify the voice guidance content based on the feedback.

[0134] The guidance unit can estimate the user's emotion and determine the priority of the voice guidance based on the estimated user's emotion. For example, when the user is nervous, the guidance unit can provide important voice guidance with priority. In addition, when the user is relaxed, the guidance unit can also provide the entire voice guidance. For example, when the user is in a hurry, time-consuming voice guidance can be provided with priority. In this way, important guidance can be provided with priority by determining the priority of the voice guidance according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the guidance unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the guidance unit can input the user's facial expression data to the AI, and the AI ​​can estimate the user's emotion and determine the priority of the voice guidance.

[0135] The guidance unit can select the optimal voice guidance method according to the user's device. For example, when the user is using a smart speaker, the guidance unit can provide voice guidance optimized for the device. In addition, when the user is using a smartphone, the guidance unit can also provide voice guidance optimized for the device. For example, when the user is using a tablet, the guidance unit can provide voice guidance optimized for the device. This improves the user's convenience by providing the optimal voice guidance method according to the device. A part or all of the above-mentioned processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit can input the user's device information into AI, and the AI ​​can select the optimal voice guidance method.

[0136] The guidance unit can provide voice guidance in multiple languages ​​according to the language setting of the user. The guidance unit can automatically set the language of the voice guidance based on the language setting of the user's device, for example. The guidance unit can also provide a language switching function when the user uses multiple languages. For example, when the user selects a specific language, the guidance unit can provide voice guidance in that language. This allows the user to receive voice guidance in a language that is easy for the user to understand due to multilingual support. A part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the guidance unit can input the user's language setting into AI, and the AI ​​can provide voice guidance in an appropriate language.

[0137] The suggestion unit can estimate the user's emotion and adjust the method of suggesting alternative materials based on the estimated user's emotion. For example, when the user is nervous, the suggestion unit can suggest a simple and easy-to-understand alternative material. In addition, when the user is relaxed, the suggestion unit can also suggest detailed alternative materials. For example, when the user is in a hurry, a quick and concise alternative material can be suggested. This allows for more appropriate suggestions by adjusting the alternative material suggestion method according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's facial expression data to an AI, and the AI ​​can estimate the user's emotion and adjust the method of suggesting alternative materials.

[0138] The suggestion unit can refer to the user's past cooking history and suggest alternative ingredients that are individually customized. The suggestion unit can, for example, refer to the user's past cooking history and suggest alternative ingredients that match the user's preferences. The suggestion unit can also refer to the user's past cooking history and suggest alternative ingredients based on allergy information. For example, the suggestion unit can refer to the user's past cooking history and suggest alternative ingredients according to the user's cooking skill. This makes it possible to suggest individually customized alternative ingredients by referring to the past cooking history. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the past cooking history into AI, which analyzes the data and suggests alternative ingredients.

[0139] The suggestion unit can receive the user's feedback in real time and instantly revise the proposed content of the alternative material. For example, when the user provides feedback on the proposed alternative material, the suggestion unit can instantly revise the proposed content. In addition, when the user asks a question on the proposed alternative material, the suggestion unit can instantly provide an answer. For example, when the user expresses dissatisfaction with the proposed alternative material, the suggestion unit can instantly propose an improvement measure. This makes it possible to propose a more appropriate alternative material by reflecting the feedback in real time. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the user's feedback to AI, and the AI ​​can revise the proposed content based on the feedback.

[0140] The suggestion unit can estimate the user's emotion and determine the priority order of the alternative materials based on the estimated user's emotion. For example, when the user is nervous, the suggestion unit can preferentially suggest important alternative materials. The suggestion unit can also suggest the entire alternative materials when the user is relaxed. For example, when the user is in a hurry, the suggestion unit can preferentially suggest time-consuming alternative materials. This enables more appropriate suggestions by determining the priority order of the alternative materials according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's facial expression data to an AI, and the AI ​​can estimate the user's emotion and determine the priority order of the alternative materials.

[0141] The suggestion unit can select the optimal display method according to the user's device. For example, when the user is using a smartphone, the suggestion unit can provide a display method that matches the screen size. In addition, when the user is using a tablet, the suggestion unit can also provide a display method optimized for a large screen. For example, when the user is using a smart watch, the suggestion unit can provide a simple and highly visible display method. This improves visibility by providing the optimal display method according to the device. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's device information into AI, and the AI ​​can select the optimal display method.

[0142] The suggestion unit can suggest alternative materials in multiple languages ​​according to the language setting of the user. The suggestion unit can automatically set the language of the alternative materials based on, for example, the language setting of the user's device. The suggestion unit can also provide a language switching function when the user uses multiple languages. For example, when the user selects a specific language, the suggestion unit can suggest alternative materials in that language. This allows the user to receive suggestions of alternative materials in a language that is easy for the user to understand due to the multilingual support. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the user's language setting into AI, which can then suggest alternative materials in an appropriate language.

[0143] The recovery unit can estimate the user's emotion and adjust the expression method of the recovery method based on the estimated user's emotion. For example, when the user is nervous, the recovery unit can provide a simple and easy-to-understand recovery method. The recovery unit can also provide a detailed recovery method when the user is relaxed. For example, when the user is in a hurry, a quick and concise recovery method can be provided. This allows for more appropriate recovery by adjusting the expression method of the recovery method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the recovery unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the recovery unit can input the user's facial expression data into an AI, and the AI ​​can estimate the user's emotion and adjust the expression method of the recovery method.

[0144] The recovery unit can provide an individually customized recovery method by referring to the user's past cooking failure history. The recovery unit can provide a recovery method for a similar failure by referring to the user's past cooking failure history, for example. The recovery unit can also provide a recovery method tailored to the user's preferences by referring to the user's past cooking failure history. For example, the recovery unit can provide a recovery method based on allergy information by referring to the user's past cooking failure history. This makes it possible to provide an individually customized recovery method by referring to the past cooking failure history. Some or all of the above-mentioned processing in the recovery unit may be performed, for example, using AI, or may be performed without using AI. For example, the recovery unit can input the past cooking failure history into AI, which analyzes the data and provides a recovery method.

[0145] The recovery unit can receive user feedback in real time and instantly modify the recovery method. For example, when the user provides feedback on the recovery method, the recovery unit can instantly modify the method. In addition, when the user asks a question about the recovery method, the recovery unit can instantly provide an answer. For example, when the user expresses dissatisfaction with the recovery method, the recovery unit can instantly propose an improvement measure. This enables a more appropriate recovery method by reflecting the feedback in real time. A part or all of the above-mentioned processing in the recovery unit may be performed, for example, using AI, or may be performed without using AI. For example, the recovery unit can input the user's feedback to AI, and the AI ​​can modify the recovery method based on the feedback.

[0146] The recovery unit can estimate the user's emotions and determine the priority of recovery methods based on the estimated user's emotions. For example, when the user is nervous, the recovery unit can provide important recovery methods with priority. The recovery unit can also provide the entire recovery method when the user is relaxed. For example, when the user is in a hurry, a recovery method that takes time can be provided with priority. This enables more appropriate recovery by determining the priority of recovery methods according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the recovery unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the recovery unit can input the user's facial expression data to an AI, and the AI ​​can estimate the user's emotions and determine the priority of recovery methods.

[0147] The recovery unit can select the optimal display method according to the user's device. For example, when the user is using a smartphone, the recovery unit can provide a display method that matches the screen size. In addition, when the user is using a tablet, the recovery unit can also provide a display method optimized for a large screen. For example, when the user is using a smart watch, the recovery unit can provide a simple and highly visible display method. This improves visibility by providing the optimal display method according to the device. A part or all of the above-mentioned processing in the recovery unit may be performed, for example, using AI, or may be performed without using AI. For example, the recovery unit can input the user's device information into AI, and the AI ​​can select the optimal display method.

[0148] The recovery unit can provide the recovery method in multiple languages ​​according to the language setting of the user. The recovery unit can automatically set the language of the recovery method based on the language setting of the user's device, for example. The recovery unit can also provide a language switching function when the user uses multiple languages. For example, when the user selects a specific language, the recovery unit can provide the recovery method in that language. This allows the user to receive the recovery method in a language that is easy for the user to understand due to the multilingual support. Some or all of the above-mentioned processing in the recovery unit may be performed, for example, using AI, or may be performed without using AI. For example, the recovery unit can input the user's language setting into AI, and the AI ​​can provide the recovery method in an appropriate language.

[0149] The reduction unit can estimate the user's emotions and adjust the way of expressing the food waste reduction advice based on the estimated user's emotions. For example, when the user is nervous, the reduction unit can provide simple and easy-to-understand food waste reduction advice. In addition, when the user is relaxed, the reduction unit can also provide detailed food waste reduction advice. For example, when the user is in a hurry, quick and concise food waste reduction advice can be provided. This enables more appropriate food waste reduction advice by adjusting the way of expressing advice according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reduction unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the reduction unit can input the user's facial expression data to an AI, and the AI ​​can estimate the user's emotions and adjust the way of expressing the advice.

[0150] The reduction unit can provide individually customized food loss reduction advice by referring to the user's past consumption history. The reduction unit can provide food loss reduction advice tailored to the user's preferences by referring to the user's past consumption history, for example. The reduction unit can also provide food loss reduction advice based on allergy information by referring to the user's past consumption history. For example, the reduction unit can provide food loss reduction advice according to the user's consumption pattern by referring to the user's past consumption history. This makes it possible to provide individually customized food loss reduction advice by referring to the past consumption history. Some or all of the above-mentioned processing in the reduction unit may be performed, for example, using AI, or may be performed without using AI. For example, the reduction unit can input the past consumption history into AI, which analyzes the data and provides advice.

[0151] The reduction unit can receive user feedback in real time and instantly revise the food loss reduction advice. For example, when the user provides feedback on the food loss reduction advice, the reduction unit can instantly revise the advice content. In addition, when the user asks a question about the food loss reduction advice, the reduction unit can also instantly provide an answer. For example, when the user expresses dissatisfaction with the food loss reduction advice, the reduction unit can instantly suggest improvement measures. This enables more appropriate food loss reduction advice by reflecting feedback in real time. Some or all of the above-mentioned processing in the reduction unit may be performed, for example, using AI, or may be performed without using AI. For example, the reduction unit can input user feedback to AI, and the AI ​​can revise the advice content based on the feedback.

[0152] The reduction unit can estimate the user's emotions and determine the priority of food waste reduction advice based on the estimated user's emotions. For example, when the user is nervous, the reduction unit can provide important food waste reduction advice with priority. The reduction unit can also provide overall food waste reduction advice when the user is relaxed. For example, when the user is in a hurry, time-consuming food waste reduction advice can be provided with priority. This enables more appropriate food waste reduction advice by determining the priority of advice according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reduction unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the reduction unit can input the user's facial expression data to an AI, and the AI ​​can estimate the user's emotions and determine the priority of advice.

[0153] The reduction unit can select the optimal display method according to the user's device. For example, when the user is using a smartphone, the reduction unit can provide a display method that matches the screen size. In addition, when the user is using a tablet, the reduction unit can also provide a display method optimized for a large screen. For example, when the user is using a smart watch, the reduction unit can provide a simple display method with high visibility. This improves visibility by providing the optimal display method according to the device. A part or all of the above-mentioned processing in the reduction unit may be performed, for example, using AI, or may be performed without using AI. For example, the reduction unit can input the user's device information into AI, and the AI ​​can select the optimal display method.

[0154] The reduction unit can provide food loss reduction advice in multiple languages ​​according to the user's language settings. The reduction unit can automatically set the language of the food loss reduction advice based on the language settings of the user's device, for example. The reduction unit can also provide a language switching function when the user uses multiple languages. For example, when the user selects a specific language, the reduction unit can provide food loss reduction advice in that language. This allows the user to receive food loss reduction advice in a language that is easy to understand due to multilingual support. Some or all of the above-mentioned processing in the reduction unit may be performed, for example, using AI, or may be performed without using AI. For example, the reduction unit can input the user's language settings into the AI, and the AI ​​can provide food loss reduction advice in an appropriate language.

[0155] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0156] The cooking advice system can adjust the level of detail of advice according to the user's cooking skill level. For example, basic cooking procedures and precautions can be explained in detail to novice users. More advanced techniques and tips can be provided to intermediate users. Furthermore, specialized advice and new recipe suggestions can be given to advanced users. This provides appropriate advice according to the user's skill level, improving cooking efficiency and satisfaction.

[0157] The suggestion unit can estimate the user's emotions and adjust the method of suggesting substitute ingredients based on the estimated emotions. For example, if the user is nervous, simple and easy-to-understand substitute ingredients can be suggested. If the user is relaxed, detailed substitute ingredients can be suggested. Furthermore, if the user is in a hurry, quick and concise substitute ingredients can be suggested. This allows appropriate suggestions according to the user's emotions, reducing the stress of cooking.

[0158] The recovery unit can refer to the user's past cooking failure history and provide an individually customized recovery method. For example, if the user has burned something in the past, it can suggest ways to remove the burnt parts and measures to prevent burning. Also, if the failure was caused by overheating, it can suggest an appropriate way to adjust the heat. Furthermore, it can suggest the optimal recovery method based on the user's preferences and allergy information. This enables effective recovery by learning from past failures.

[0159] The reduction unit can estimate the user's emotions and adjust the way in which food waste reduction advice is presented based on the estimated emotions. For example, if the user is nervous, simple and easy-to-understand advice can be provided. If the user is relaxed, detailed advice can be provided. Furthermore, if the user is in a hurry, quick and concise advice can be provided. This makes it possible to provide appropriate advice according to the user's emotions, thereby increasing the effectiveness of food waste reduction.

[0160] The acquisition unit can acquire images of specific cooking steps in response to voice instructions from the user. For example, if the user instructs "Take a picture of the current step," an image of that step can be acquired. Also, if the user instructs "Take a picture of the next step," an image of that step can be acquired. Furthermore, if the user instructs "Take a picture of the state of this ingredient," an image of that ingredient can be acquired. This allows necessary information to be acquired accurately in response to voice instructions from the user.

[0161] The analysis unit can estimate the user's emotions and adjust the way in which the analysis results are expressed based on the estimated emotions. For example, if the user is nervous, a simple and easy-to-understand expression can be provided. If the user is relaxed, an expression that includes detailed information can be provided. Furthermore, if the user is in a hurry, an expression that gets straight to the point can be provided. This makes it possible to provide appropriate analysis results according to the user's emotions.

[0162] The guidance unit can provide individually customized voice guidance by referring to the user's past cooking history. For example, if the user has experience cooking a particular dish in the past, voice guidance related to that dish can be provided. Also, appropriate voice guidance can be provided based on the user's preferences and allergy information. Furthermore, basic guidance for beginners and specialized guidance for advanced cooks can be provided according to the user's cooking skills. This makes it possible to provide appropriate voice guidance according to the user's needs.

[0163] The providing unit can estimate the user's emotions and adjust the way in which advice is expressed based on the estimated emotions. For example, if the user is nervous, simple and easy-to-understand advice can be provided. If the user is relaxed, detailed advice can be provided. Furthermore, if the user is in a hurry, advice that focuses on the main points can be provided. This makes it possible to provide appropriate advice according to the user's emotions, improving cooking efficiency.

[0164] The analysis unit can improve the accuracy of the analysis by referring to past cooking data and comparing it with the current cooking situation. For example, it can provide appropriate advice by referring to past cooking data and comparing it with the current cooking situation. It can also detect the possibility of failure by referring to past cooking data and comparing it with the current cooking situation. Furthermore, it can suggest the optimal cooking method by referring to past cooking data and comparing it with the current cooking situation. This enables highly accurate analysis using past data.

[0165] The detection unit can also analyze voice data during cooking to detect early signs of failure. For example, by analyzing voice data during cooking and detecting sounds of burning or boiling, it can grasp early signs of failure. It can also analyze voice data to provide appropriate countermeasures. Furthermore, it can analyze voice data to identify factors that increase the possibility of failure and suggest preventive measures. This makes it possible to detect failure early using voice data.

[0166] The process flow of the second embodiment will be briefly described below.

[0167] Step 1: When a user starts cooking, the acquisition unit acquires images of the cooking process through a camera. For example, the acquisition unit can acquire images of the cooking process in real time using a camera installed on the top of a cooking utensil. The acquisition unit can also use multiple cameras to simultaneously acquire images from different angles. For example, cameras are installed on the top and sides of a cooking table to simultaneously acquire images from different angles. Step 2: The analysis unit analyzes the image acquired by the acquisition unit. For example, the analysis unit can use image recognition technology to analyze the state of ingredients and cooking utensils during cooking. For example, the analysis unit can use an object detection algorithm to identify ingredients during cooking and analyze their state. The analysis unit can also use pattern recognition technology to analyze the state of cooking utensils. Step 3: The providing unit provides advice based on the results of the analysis by the analyzing unit. For example, the providing unit can suggest alternative ingredients when an ingredient is in short supply or a different ingredient is used. The providing unit can also provide appropriate advice based on the user's preferences and allergy information. Step 4: The registration unit registers the user's preferences and allergy information. For example, the registration unit stores the preferences and allergy information previously input by the user in a database and provides advice based on the information. Step 5: The detection unit detects cooking failures. For example, the detection unit can use image recognition technology to detect signs of failure during cooking. For example, if the heat is too high or the ingredients are not cut properly, the detection unit can detect this and provide appropriate countermeasures. Step 6: The guidance unit provides voice guidance. For example, the guidance unit can detect points that may cause failure during cooking and issue a voice warning. The guidance unit can also provide voice guidance on how to recover from a cooking failure.

[0168] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0169] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0170] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0171] Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, provision unit, registration unit, detection unit, guidance unit, suggestion unit, recovery unit, and reduction unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit acquires an image during cooking using the camera 42 of the smart device 14, and the image is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the acquired image data. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides advice based on the analysis results. The registration unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and registers the user's preferences and allergy information. The detection unit detects cooking failure using, for example, the camera 42 of the smart device 14, and the image is analyzed by the specific processing unit 290 of the data processing device 12. The guidance unit provides voice guidance using, for example, the speaker 40B of the smart device 14. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests alternative ingredients. The recovery unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides a recovery method when cooking fails. The reduction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides advice for reducing food waste. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0172] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0173] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0174] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0175] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0176] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0177] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0178] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0179] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0180] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0181] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0182] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0183] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0184] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0185] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0186] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0187] Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, provision unit, registration unit, detection unit, guidance unit, suggestion unit, recovery unit, and reduction unit is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit acquires an image during cooking using the camera 42 of the smart glasses 214, and is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the acquired image data. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides advice based on the analysis result. The registration unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and registers the user's preferences and allergy information. The detection unit detects cooking failure using, for example, the camera 42 of the smart glasses 214, and is analyzed by the specific processing unit 290 of the data processing device 12. The guidance unit provides voice guidance using, for example, the speaker 240 of the smart glasses 214. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests alternative ingredients. The recovery unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides a recovery method when cooking fails. The reduction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides advice for reducing food waste. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0188] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0189] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0190] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0191] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0192] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0193] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0194] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0195] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0196] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0197] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0198] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0199] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0200] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0201] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0202] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0203] Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, provision unit, registration unit, detection unit, guidance unit, suggestion unit, recovery unit, and reduction unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the acquisition unit acquires an image during cooking using the camera 42 of the headset type terminal 314, and the image is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the acquired image data. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides advice based on the analysis results. The registration unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and registers the user's preferences and allergy information. The detection unit detects cooking failures using, for example, the camera 42 of the headset type terminal 314, and the image is analyzed by the specific processing unit 290 of the data processing device 12. The guidance unit provides voice guidance using, for example, the speaker 240 of the headset type terminal 314. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests alternative ingredients. The recovery unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides a recovery method when cooking fails. The reduction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides advice for reducing food waste. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0204] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0205] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0206] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0207] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0208] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0209] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0210] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0211] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0212] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0213] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0214] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0215] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0216] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0217] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0218] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0219] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0220] Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, provision unit, registration unit, detection unit, guidance unit, suggestion unit, recovery unit, and reduction unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit acquires an image during cooking using the camera 42 of the robot 414, and the image is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the acquired image data. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides advice based on the analysis results. The registration unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and registers the user's preferences and allergy information. The detection unit detects cooking failures using, for example, the camera 42 of the robot 414, and the image is analyzed by the specific processing unit 290 of the data processing device 12. The guidance unit provides voice guidance using, for example, the speaker 240 of the robot 414. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests alternative ingredients. The recovery unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides a recovery method when cooking fails. The reduction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides advice for reducing food waste. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0221] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0222] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0223] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0224] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0225] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0226] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0227] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0228] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0229] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0230] In addition, 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 upon request from the data processing device 12.

[0231] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0232] As the hardware resource for executing the specific process, the following various processors can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application specific integrated circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0233] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0234] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0235] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0236] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0237] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0238] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0239] (Appendix 1) An acquisition unit that acquires an image; an analysis unit that analyzes the image acquired by the acquisition unit; a providing unit that provides advice based on the result of the analysis by the analyzing unit; A registration unit for registering user preferences and allergy information; A detection unit for detecting cooking failure; A guidance unit that provides voice guidance. A system characterized by: (Appendix 2) Equipped with a proposal department that proposes alternative materials 2. The system of claim 1, (Appendix 3) Equipped with a recovery section that provides a recovery method in the event of a cooking failure 2. The system of claim 1, (Appendix 4) Have a food waste reduction department that provides advice on reducing food waste 2. The system of claim 1, (Appendix 5) The acquisition unit is Capture images of the cooking process through a camera 2. The system of claim 1, (Appendix 6) The analysis unit is Analyzing cooking status using image recognition technology 2. The system of claim 1, (Appendix 7) The guide unit is Gives audio warnings if the heat is too high or ingredients are not cut properly 2. The system of claim 1, (Appendix 8) The acquisition unit is A user's emotion is estimated, and the timing of image acquisition is adjusted based on the estimated user's emotion. 2. The system of claim 1, (Appendix 9) The acquisition unit is Use multiple cameras to capture images from different angles simultaneously, allowing for more detailed analysis 2. The system of claim 1, (Appendix 10) The acquisition unit is Environmental data such as temperature and humidity during cooking is also collected and provided to the analysis department. 2. The system of claim 1, (Appendix 11) The acquisition unit is Estimate the user's emotion and prioritize images to be acquired based on the estimated user's emotion. 2. The system of claim 1, (Appendix 12) The acquisition unit is Acquire images of specific cooking steps in response to user voice commands 2. The system of claim 1, (Appendix 13) The acquisition unit is The status of cooking utensils is acquired in real time and provided to the analysis department. 2. The system of claim 1, (Appendix 14) The analysis unit is Infer user emotions and adjust the presentation of analysis results based on the inferred user emotions 2. The system of claim 1, (Appendix 15) The analysis unit is In addition to image data, the system also analyzes environmental data provided by the acquisition unit to provide more accurate advice. 2. The system of claim 1, (Appendix 16) The analysis unit is Refer to past cooking data and compare it with the current cooking situation to improve the accuracy of analysis. 2. The system of claim 1, (Appendix 17) The analysis unit is Infer user emotions and prioritize analysis results based on the estimated user emotions. 2. The system of claim 1, (Appendix 18) The analysis unit is Adjust the level of analysis detail according to the user's cooking skill level 2. The system of claim 1, (Appendix 19) The analysis unit is Switching analysis algorithms for different types of cuisine 2. The system of claim 1, (Appendix 20) The providing unit is Inferring the user's emotions and adjusting the way advice is presented based on the estimated user's emotions 2. The system of claim 1, (Appendix 21) The providing unit is Refers to the user's past cooking history and provides personalized advice 2. The system of claim 1, (Appendix 22) The providing unit is Receive real-time user feedback and instantly revise advice 2. The system of claim 1, (Appendix 23) The providing unit is Estimate the user's emotions and prioritize advice based on the estimated user emotions. 2. The system of claim 1, (Appendix 24) The providing unit is Select the best display method depending on the user's device 2. The system of claim 1, (Appendix 25) The providing unit is Providing advice in multiple languages ​​based on the user's language settings 2. The system of claim 1, (Appendix 26) The registration unit is The system estimates the user's emotions and adjusts the registration method of preferences and allergy information based on the estimated user emotions. 2. The system of claim 1, (Appendix 27) The registration unit is Refer to the user's past meal history and automatically update preferences and allergy information 2. The system of claim 1, (Appendix 28) The registration unit is Allow users to centrally manage preferences and allergy information for their family and housemates 2. The system of claim 1, (Appendix 29) The registration unit is Estimate user sentiment and prioritize registration information based on the estimated user sentiment 2. The system of claim 1, (Appendix 30) The registration unit is Register preferences and allergy information according to user voice instructions 2. The system of claim 1, (Appendix 31) The registration unit is Analyze your social media activity to automatically update your preferences and allergies 2. The system of claim 1, (Appendix 32) The detection unit is To estimate a user's emotion and adjust a cooking failure detection method based on the estimated user's emotion. 2. The system of claim 1, (Appendix 33) The detection unit is Refer to past cooking failure data and compare it with the current cooking situation to increase the possibility of failure 2. The system of claim 1, (Appendix 34) The detection unit is It also analyzes voice data during cooking to detect early signs of failure. 2. The system of claim 1, (Appendix 35) The detection unit is Estimate user emotions and prioritize failure detection based on the estimated user emotions. 2. The system of claim 1, (Appendix 36) The detection unit is Adjust the sensitivity of failure detection according to the user's cooking skill level 2. The system of claim 1, (Appendix 37) The detection unit is Switching failure detection algorithms for different types of dishes 2. The system of claim 1, (Appendix 38) The guide unit is Estimating a user's emotions and adjusting the way voice guidance is presented based on the estimated user's emotions 2. The system of claim 1, (Appendix 39) The guide unit is Refers to the user's past cooking history and provides individually customized voice guidance 2. The system of claim 1, (Appendix 40) The guide unit is Receive real-time user feedback and instantly revise voice prompts 2. The system of claim 1, (Appendix 41) The guide unit is Estimate the user's emotions and determine the priority of voice guidance based on the estimated user's emotions. 2. The system of claim 1, (Appendix 42) The guide unit is Select the optimal voice guidance method based on the user's device 2. The system of claim 1, (Appendix 43) The guide unit is Provides voice guidance in multiple languages ​​according to the user's language settings 2. The system of claim 1, (Appendix 44) The suggestion unit, Estimating a user's emotion and adjusting a method for proposing alternative materials based on the estimated user's emotion 3. The system of claim 2, (Appendix 45) The suggestion unit, Referencing the user's cooking history to suggest customized alternative ingredients 3. The system of claim 2, (Appendix 46) The suggestion unit, Receive real-time user feedback and instantly revise alternative material suggestions 3. The system of claim 2, (Appendix 47) The suggestion unit, To estimate a user's emotion and prioritize alternative materials based on the estimated user's emotion. 3. The system of claim 2, (Appendix 48) The suggestion unit, Select the best display method depending on the user's device 3. The system of claim 2, (Appendix 49) The suggestion unit, Suggest alternative ingredients in multiple languages ​​based on the user's language settings 3. The system of claim 2, (Appendix 50) The recovery unit is Infer the user's emotions and adjust the way the recovery method is expressed based on the estimated user's emotions. 4. The system of claim 3. (Appendix 51) The recovery unit is Refer to the user's past cooking failure history and provide an individually customized recovery method 4. The system of claim 3. (Appendix 52) The recovery unit is Receive real-time user feedback and instantly modify your recovery strategy 4. The system of claim 3. (Appendix 53) The recovery unit is Inferring user emotions and prioritizing recovery methods based on the estimated user emotions 4. The system of claim 3. (Appendix 54) The recovery unit is Select the best display method depending on the user's device 4. The system of claim 3. (Appendix 55) The recovery unit is Provide recovery instructions in multiple languages ​​depending on the user's language settings 4. The system of claim 3. (Appendix 56) The reduction section is Estimate the user's emotions and adjust the way food waste reduction advice is presented based on the estimated user emotions 5. The system of claim 4. (Appendix 57) The reduction section is Referencing the user's past consumption history, the service provides individually tailored advice on reducing food waste 5. The system of claim 4. (Appendix 58) The reduction section is Receive real-time user feedback and instantly revise food waste reduction advice 5. The system of claim 4. (Appendix 59) The reduction section is Estimate the user's emotions and prioritize food waste reduction advice based on the estimated user emotions 5. The system of claim 4. (Appendix 60) The reduction section is Select the best display method depending on the user's device 5. The system of claim 4. (Appendix 61) The reduction section is Providing food waste reduction advice in multiple languages ​​according to the user's language settings 5. The system of claim 4. [Explanation of symbols]

[0240] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An acquisition unit that acquires an image; an analysis unit that analyzes the image acquired by the acquisition unit; a providing unit that provides advice based on the result of the analysis by the analyzing unit; A registration unit for registering user preferences and allergy information; A detection unit for detecting cooking failure; A guidance unit that provides voice guidance. A system characterized by:

2. Equipped with a proposal department that proposes alternative materials 2. The system of claim 1 .

3. Equipped with a recovery section that provides a recovery method in the event of a cooking failure 2. The system of claim 1 .

4. Have a food waste reduction department that provides advice on reducing food waste 2. The system of claim 1 .

5. The acquisition unit is Capture images of the cooking process through a camera 2. The system of claim 1 .

6. The analysis unit is Analyzing cooking status using image recognition technology 2. The system of claim 1 .

7. The guide unit is Gives audio warnings if the heat is too high or ingredients are not cut properly 2. The system of claim 1 .

8. The acquisition unit is A user's emotion is estimated, and the timing of image acquisition is adjusted based on the estimated user's emotion.

2. The system of claim 1 .

9. The acquisition unit is Use multiple cameras to capture images from different angles simultaneously, allowing for more detailed analysis 2. The system of claim 1 .

Images