system

Abstract

The system according to this embodiment aims to streamline the manufacturing and operation establishment of automated cooking robots. [Solution] The system according to the embodiment comprises an acquisition unit, a learning unit, and an instruction unit. The acquisition unit acquires images of ingredients and finished products. The learning unit learns cooking methods based on the images acquired by the acquisition unit. The instruction unit gives instructions to the automated cooking robot based on the cooking methods learned by the learning unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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] [[ID=2)]]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it takes time and effort to manufacture and establish the operation of an automatic cooking robot.

[0005] The system according to the embodiment aims to improve the efficiency of manufacturing and establishing the operation of an automatic cooking robot.

Means for Solving the Problems

[0006] ) The system according to the embodiment includes an acquisition unit, a learning unit, and an instruction unit. The acquisition unit acquires images of food ingredients and finished products. The learning unit learns a cooking method based on the images acquired by the acquisition unit. The instruction unit gives an instruction to an automatic cooking robot based on the cooking method learned by the learning unit.

Effects of the Invention

[0007] The system according to this embodiment can streamline the manufacturing and operation establishment of automated cooking robots. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An automated cooking robot system according to an embodiment of the present invention is a system that uses a generative AI to learn cooking methods. This system acquires images of ingredients and finished products, and the generative AI learns cooking methods based on these images. The generative AI identifies the cooking situation and determines the optimal cooking method through trial and error. Finally, the automated cooking robot prepares the food based on the cooking method learned by the generative AI. This system can significantly reduce the effort required for manufacturing, establishing the operation of, and adjusting automated cooking robots. Furthermore, by learning cooking methods, the generative AI can handle a variety of dishes. In addition, the process of the generative AI learning cooking methods can lead to increased efficiency and improved quality of cooking. For example, it can reduce food waste and shorten cooking time. Also, by optimizing the cooking method, the generative AI can consistently provide dishes of a certain quality. In this way, an automated cooking robot system using generative AI can address labor shortages and reduce wage costs, providing efficient and high-quality food. For example, the ingredient acquisition unit takes images of ingredients with a high-resolution camera, and the generative AI analyzes the images to recognize the type and condition of the ingredients. Next, it acquires images of finished products, and the generative AI learns cooking methods based on these images. Generative AI, for example, uses deep learning technology to learn how to cook ingredients. Through trial and error, the generative AI finds the optimal cooking method. For example, it tries different cooking methods, evaluates the results, and selects the best one. Based on the cooking methods learned by the generative AI, an automated cooking robot prepares the food. For example, the automated cooking robot follows the cooking procedures instructed by the generative AI, performing tasks such as cutting, stir-frying, and boiling ingredients. This enables the provision of efficient and high-quality meals. As a result, the automated cooking robot system reduces food waste, shortens cooking time, and consistently provides meals of the same quality.

[0029] The automated cooking robot system according to this embodiment comprises an acquisition unit, a learning unit, and an instruction unit. The acquisition unit acquires images of ingredients and finished products. For example, the acquisition unit can take images of ingredients using a high-resolution camera. The acquisition unit can also take images of finished products. For example, the acquisition unit takes images of ingredients and inputs those images into a generation AI. The generation AI recognizes the type and state of the ingredients based on those images. The learning unit uses the generation AI to learn cooking methods based on the images acquired by the acquisition unit. For example, the learning unit uses deep learning technology to learn cooking methods for ingredients. The generation AI finds the optimal cooking method through trial and error. For example, the generation AI tries different cooking methods, evaluates the results, and selects the optimal method. The instruction unit gives instructions to the automated cooking robot based on the cooking methods learned by the learning unit. For example, the instruction unit transmits the cooking procedure learned by the generation AI to the automated cooking robot. The automated cooking robot performs cooking such as cutting, stir-frying, and boiling the ingredients according to the instructions from the instruction unit. As a result, the automated cooking robot system according to this embodiment can reduce food waste, shorten cooking time, and consistently provide dishes of a certain quality. For example, the acquisition unit captures images of ingredients with a high-resolution camera, and the generation AI analyzes the images to recognize the type and condition of the ingredients. Next, it acquires images of the finished product, and the generation AI learns cooking methods based on those images. The generation AI learns cooking methods for ingredients, for example, using deep learning technology. The generation AI finds the optimal cooking method through trial and error. For example, the generation AI tries different cooking methods, evaluates the results, and selects the best method. Based on the cooking methods learned by the generation AI, the automated cooking robot prepares the dishes. For example, the automated cooking robot performs cooking procedures such as cutting, stir-frying, and boiling ingredients according to the cooking procedures instructed by the generation AI. This makes it possible to provide efficient and high-quality dishes.

[0030] A system characterized by having a trial unit in which a generative AI performs trial and error, and including specific methods for trial and error. In the trial unit, the generative AI performs trial and error. For example, the trial unit tries different cooking methods, evaluates the results, and selects the optimal method. The generative AI performs trial and error, for example, using deep learning technology. The generative AI finds the optimal cooking method by repeating trial and error. For example, the generative AI tries different cooking methods, evaluates the results, and selects the optimal method. In this way, the generative AI optimizes the cooking method by performing trial and error. For example, the trial unit tries different cooking methods, evaluates the results, and selects the optimal method. The generative AI performs trial and error, for example, using deep learning technology. The generative AI finds the optimal cooking method by repeating trial and error. For example, the generative AI tries different cooking methods, evaluates the results, and selects the optimal method. In this way, the generative AI optimizes the cooking method by performing trial and error.

[0031] A system characterized by having an identification unit that identifies the cooking status. The identification unit identifies the cooking status. For example, the identification unit can identify temperature, time, the state of ingredients, etc. The identification unit identifies the cooking status using sensors, for example. For example, the identification unit measures the temperature during cooking using a temperature sensor and inputs that data into a generating AI. The generating AI identifies the cooking status based on that data. The identification unit also measures the cooking time using a time sensor and inputs that data into the generating AI. The generating AI identifies the cooking status based on that data. Furthermore, the identification unit can use image recognition technology to identify the state of ingredients. For example, the identification unit takes an image of the ingredients and inputs that image into the generating AI. The generating AI identifies the state of the ingredients based on that image. As a result, the accuracy of cooking is improved by the identification unit identifying the cooking status. For example, the identification unit measures the temperature during cooking using a temperature sensor and inputs that data into the generating AI. The generating AI identifies the cooking status based on that data. The identification unit also measures the cooking time using a time sensor and inputs that data into the generating AI. The generating AI identifies the cooking status based on that data. Furthermore, the identification unit can use image recognition technology to identify the state of the ingredients. For example, the identification unit takes a picture of the ingredients and inputs that image into the generating AI. The generating AI then identifies the state of the ingredients based on that image. As a result, the identification unit can improve the accuracy of cooking by identifying the cooking status.

[0032] A system characterized by having a decision unit that determines the cooking method. The decision unit determines the cooking method. For example, the decision unit determines the optimal cooking method based on cooking methods learned by the generative AI. The decision unit determines the cooking method using, for example, deep learning technology. The generative AI finds the optimal cooking method through repeated trial and error. For example, the generative AI tries different cooking methods, evaluates the results, and selects the optimal method. The decision unit determines the optimal cooking method based on the results. In this way, the decision unit can select the optimal cooking method by determining the cooking method. For example, the decision unit determines the optimal cooking method based on cooking methods learned by the generative AI. The decision unit determines the cooking method using, for example, deep learning technology. The generative AI finds the optimal cooking method through repeated trial and error. For example, the generative AI tries different cooking methods, evaluates the results, and selects the optimal method. The decision unit determines the optimal cooking method based on the results. In this way, the decision unit can select the optimal cooking method by determining the cooking method.

[0033] A system characterized by having a communication unit for instructing a robot on cooking methods learned by a generative AI. The communication unit is used when instructing the robot on cooking methods learned by the generative AI. For example, the communication unit transmits the cooking procedures learned by the generative AI to an automated cooking robot using communication protocols such as Wi-Fi or Bluetooth (registered trademark). The communication unit can transmit the cooking procedures learned by the generative AI to an automated cooking robot using Wi-Fi, for example. The communication unit can also transmit the cooking procedures learned by the generative AI to an automated cooking robot using Bluetooth. This allows the communication unit to accurately instruct the robot on cooking methods learned by the generative AI. For example, the communication unit can transmit the cooking procedures learned by the generative AI to an automated cooking robot using Wi-Fi, for example. The communication unit can also transmit the cooking procedures learned by the generative AI to an automated cooking robot using Bluetooth. This allows the communication unit to accurately instruct the robot on cooking methods learned by the generative AI.

[0034] The system is characterized by having an interface unit that instructs a robot on cooking methods learned by a generative AI. The interface unit is used when instructing the robot on cooking methods learned by the generative AI. For example, the interface unit uses an interface such as a touchscreen or voice input to instruct the automated cooking robot on cooking procedures learned by the generative AI. The interface unit can, for example, use a touchscreen to instruct the automated cooking robot on cooking procedures learned by the generative AI. The interface unit can also use voice input to instruct the automated cooking robot on cooking procedures learned by the generative AI. This allows the interface unit to intuitively instruct the robot on cooking methods learned by the generative AI.

[0035] This system features an acquisition unit that automatically evaluates the freshness and quality of ingredients and selects the optimal ingredients. For example, the acquisition unit uses a generating AI to analyze images of ingredients and select the freshest ones. Furthermore, the acquisition unit can also use a generating AI to evaluate the quality of ingredients and select the optimal ones. Additionally, the acquisition unit can use a generating AI to analyze the color and shape of ingredients and select the optimal ones. This allows the system to automatically evaluate the freshness and quality of ingredients, ensuring that the best ingredients are always used.

[0036] This system is characterized by its ability to customize the ingredients it acquires by referencing the user's past cooking history. For example, the acquisition unit uses a generating AI to analyze the user's past cooking history and prioritizes acquiring frequently used ingredients. Furthermore, the acquisition unit can also acquire ingredients that were popular in the past, taking the user's preferences into account. Additionally, the acquisition unit can use the generating AI to suggest new ingredients based on the user's past cooking history. This allows the system to acquire ingredients that match the user's preferences by referencing their past cooking history.

[0037] This system is characterized by its ability to prioritize the acquisition of regionally specific ingredients when acquiring ingredients, taking into account the user's geographical location information. The acquisition unit prioritizes the acquisition of regionally specific ingredients based on the user's geographical location information. For example, the acquisition unit's generating AI analyzes the user's current location and prioritizes the acquisition of ingredients that are easily available in that area. Furthermore, the acquisition unit can also have the generating AI suggest regionally specific ingredients based on the user's geographical location information. Additionally, the acquisition unit can also have the generating AI prioritize the acquisition of local ingredients, taking into account the user's geographical location information. This allows for the priority acquisition of regionally specific ingredients by considering the user's geographical location information.

[0038] This system is characterized by its acquisition unit selecting ingredients based on the user's dietary restrictions and allergy information. The acquisition unit selects ingredients based on the user's dietary restrictions and allergy information. For example, the acquisition unit's generating AI selects safe ingredients based on the user's allergy information. Furthermore, the acquisition unit's generating AI can also select appropriate ingredients considering the user's dietary restrictions. Additionally, the acquisition unit's generating AI can also select optimal ingredients considering the user's health condition. This ensures that safe and appropriate ingredients are selected by considering the user's dietary restrictions and allergy information.

[0039] A system characterized by its learning unit optimizing its learning algorithm by referencing past cooking data during the learning process. For example, the learning unit allows the generative AI to analyze past cooking data and learn the optimal cooking method. The learning unit can also optimize the learning algorithm based on past successes of the generative AI. Furthermore, the learning unit can analyze past failures of the generative AI and improve the learning algorithm. This allows the learning algorithm to be optimized by referencing past cooking data.

[0040] A system characterized by its learning unit applying different learning methods to different categories of cuisine during the learning process. For example, the learning unit can apply different learning methods to the generating AI for categories such as Japanese, Western, and Chinese cuisine. Furthermore, the learning unit can also apply different learning methods to the generating AI for categories such as desserts, main dishes, and side dishes. Additionally, the learning unit can apply different learning methods to the generating AI for categories such as soups, salads, and grilled dishes. This allows the system to handle a wider variety of cuisines by applying different learning methods to different categories.

[0041] This system is characterized by its learning unit selecting training data while considering the seasonality of ingredients. For example, the learning unit allows the generating AI to consider seasonal ingredients and select the optimal training data. Furthermore, the learning unit can also allow the generating AI to learn seasonal dishes and optimal cooking methods. Additionally, the learning unit can allow the generating AI to consider the characteristics of seasonal ingredients and select training data. This allows for the selection of more appropriate training data by considering the seasonality of ingredients.

[0042] This system is characterized by its learning unit customizing learning content based on the user's food preferences during the learning process. For example, the learning unit's generating AI considers the user's preferences and selects the optimal learning content. Furthermore, the learning unit's generating AI can also customize learning content based on the user's past cooking history. Additionally, the learning unit's generating AI can analyze the user's food preferences and optimize the learning content. This allows the system to learn more appropriate cooking methods by customizing learning content based on the user's food preferences.

[0043] This system is characterized by an instruction unit that monitors the progress of cooking in real time when issuing instructions and modifies the instructions as needed. For example, the instruction unit's generating AI monitors the progress of cooking in real time and modifies the instructions as needed. The instruction unit can also have the generating AI analyze the progress of cooking and provide optimal instructions. Furthermore, the instruction unit can optimize the instructions based on the progress of cooking. This improves cooking accuracy by monitoring the progress of cooking in real time and modifying the instructions as needed.

[0044] A system characterized by an instruction unit that generates optimal instructions based on the performance and characteristics of the cooking robot when instructions are given. The instruction unit generates optimal instructions based on the performance and characteristics of the cooking robot when instructions are given. For example, the instruction unit's generating AI considers the performance of the cooking robot and generates optimal instructions. The instruction unit can also generate optimal instructions by having the generating AI analyze the characteristics of the cooking robot. Furthermore, the instruction unit can optimize instructions based on the movements of the cooking robot by having the generating AI optimize the instructions. As a result, the accuracy of cooking is improved by generating optimal instructions based on the performance and characteristics of the cooking robot. For example, the instruction unit's generating AI considers the performance of the cooking robot and generates optimal instructions. The instruction unit can also generate optimal instructions by having the generating AI analyze the characteristics of the cooking robot. Furthermore, the instruction unit can optimize instructions based on the movements of the cooking robot by having the generating AI optimize the instructions. As a result, the accuracy of cooking is improved by generating optimal instructions based on the performance and characteristics of the cooking robot.

[0045] This system is characterized by its instruction unit adjusting the instruction content while considering the temperature and humidity of the cooking environment. For example, the instruction unit's generating AI considers the temperature of the cooking environment and provides optimal instructions. Furthermore, the instruction unit's generating AI can analyze the humidity of the cooking environment and provide optimal instructions. Additionally, the instruction unit can adjust the instruction content based on changes in the cooking environment. This allows for more appropriate instructions by considering the temperature and humidity of the cooking environment.

[0046] This system is characterized by its ability to customize instructions by referencing the user's past cooking history when issuing instructions. For example, the instruction unit's generating AI analyzes the user's past cooking history and provides optimal instructions. Furthermore, the instruction unit's generating AI can also consider the user's preferences and customize the instructions. Additionally, the instruction unit's generating AI can optimize the instructions based on the user's past cooking history. This allows for more appropriate instructions by referencing the user's past cooking history.

[0047] A system characterized by a trial unit that optimizes the trial algorithm by referring to past trial data during the trial. The trial unit optimizes the trial algorithm by referring to past trial data during the trial. For example, the trial unit analyzes past trial data and applies the optimal trial algorithm. The trial unit can also optimize the trial algorithm based on past successes. Furthermore, the trial unit can analyze past failures and improve the trial algorithm. This allows for the optimization of the trial algorithm by referring to past trial data.

[0048] A system characterized by a trial unit that combines different cooking methods during the trial process to conduct new trials. The trial unit combines different cooking methods during the trial process to conduct new trials. For example, the trial unit combines different cooking methods to test new recipes. The trial unit can also combine different cooking techniques to test the optimal cooking method. Furthermore, the trial unit can combine different ingredients to test new dishes. This allows for new trials by combining different cooking methods. For example, the trial unit combines different cooking methods to test new recipes. The trial unit can also combine different cooking techniques to test the optimal cooking method. Furthermore, the trial unit can combine different ingredients to test new dishes. This allows for new trials by combining different cooking methods.

[0049] A system characterized by an identification unit that optimizes its identification algorithm by referring to past identification data during the identification process. The identification unit optimizes its identification algorithm by referring to past identification data during the identification process. For example, the identification unit analyzes past identification data and applies the optimal identification algorithm. The identification unit can also optimize its identification algorithm based on past successes. Furthermore, the identification unit can analyze past failures and improve its identification algorithm. This allows for the optimization of the identification algorithm by referring to past identification data.

[0050] This system is characterized by its identification unit combining different ingredients and cooking conditions during the identification process. The identification unit combines different ingredients and cooking conditions during the identification process. For example, the identification unit combines different ingredients to achieve optimal identification. Furthermore, the identification unit can also combine different cooking conditions to achieve optimal identification. In addition, the identification unit can combine different cooking techniques to achieve optimal identification. This allows for more accurate identification by combining different ingredients and cooking conditions.

[0051] A system characterized by a decision unit that optimizes its decision algorithm by referring to past decision data when making a decision. The decision unit optimizes its decision algorithm by referring to past decision data when making a decision. For example, the decision unit analyzes past decision data and applies the optimal decision algorithm. The decision unit can also optimize its decision algorithm based on past successes. Furthermore, the decision unit can analyze past failures and improve its decision algorithm. This allows the decision algorithm to be optimized by referring to past decision data.

[0052] A system characterized by a decision-making unit that combines different cooking methods to make a new decision. The decision-making unit combines different cooking methods to make a new decision. For example, the decision-making unit combines different cooking methods to make a new decision. Furthermore, the decision-making unit can combine different cooking techniques to make the optimal decision. In addition, the decision-making unit can combine different ingredients to make a new decision. This makes it possible to make new decisions by combining different cooking methods. For example, the decision-making unit combines different cooking methods to make a new decision. Furthermore, the decision-making unit can combine different cooking techniques to make the optimal decision. Furthermore, the decision-making unit can combine different ingredients to make a new decision. This makes it possible to make new decisions by combining different cooking methods.

[0053] A system characterized by a communication unit that optimizes the communication algorithm by referring to past communication data during communication. The communication unit optimizes the communication algorithm by referring to past communication data during communication. For example, the communication unit analyzes past communication data and applies the optimal communication algorithm. The communication unit can also optimize the communication algorithm based on past successes. Furthermore, the communication unit can analyze past failures and improve the communication algorithm. This allows for the optimization of the communication algorithm by referring to past communication data. For example, the communication unit analyzes past communication data and applies the optimal communication algorithm. The communication unit can also optimize the communication algorithm based on past successes. Furthermore, the communication unit can analyze past failures and improve the communication algorithm. This allows for the optimization of the communication algorithm by referring to past communication data.

[0054] A system characterized by its communication unit combining different communication protocols during communication. The communication unit combines different communication protocols during communication. For example, the communication unit combines different communication protocols to achieve optimal communication. Furthermore, the communication unit can combine different communication technologies to achieve optimal communication. In addition, the communication unit can combine different communication means to achieve optimal communication. This allows for more appropriate communication by combining different communication protocols. For example, the communication unit combines different communication protocols to achieve optimal communication. Furthermore, the communication unit can combine different communication technologies to achieve optimal communication. Furthermore, the communication unit can combine different communication means to achieve optimal communication. This allows for more appropriate communication by combining different communication protocols.

[0055] A system characterized by an interface unit that, when displaying the interface, selects the optimal display method by referring to the user's past operation history. The interface unit selects the optimal display method by referring to the user's past operation history when displaying the interface. For example, the interface unit selects the optimal display method based on the user's past operation history. Furthermore, the interface unit can also customize the display method considering the user's preferences. In addition, the interface unit can analyze the user's past operation history and optimize the display method. This allows the system to select the optimal display method by referring to the user's past operation history. For example, the interface unit selects the optimal display method based on the user's past operation history. Furthermore, the interface unit can also customize the display method considering the user's preferences. Furthermore, the interface unit can analyze the user's past operation history and optimize the display method. This allows the system to select the optimal display method by referring to the user's past operation history.

[0056] A system characterized by an interface unit that, when displaying the interface, selects the optimal display method considering the user's device information. For example, if the user is using a smartphone, the interface unit provides a display method adapted to the screen size. Furthermore, if the user is using a tablet, the interface unit can also provide a display method optimized for larger screens. Additionally, if the user is using a smartwatch, the interface unit can provide a concise and highly visible display method. This allows the system to select the optimal display method by considering the user's device information.

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

[0058] The automated cooking robot system can also be equipped with a voice recognition unit. The voice recognition unit can acquire voice instructions from the user and adjust the cooking method based on those instructions. For example, if the user says, "Add a little more salt," the voice recognition unit analyzes the instruction and transmits it to the generating AI. The generating AI adjusts the cooking method based on that instruction and adds the appropriate amount of salt. The voice recognition unit can also provide appropriate answers to questions the user asks during cooking. This allows the user to give instructions without using their hands during cooking, enabling more efficient cooking.

[0059] The automated cooking robot system can also be equipped with a temperature control unit. This unit can monitor the cooking temperature in real time and adjust it as needed. For example, if the temperature is too high during cooking, it can temporarily pause heating and return to an appropriate temperature. Conversely, if the temperature is too low, it can intensify heating to maintain the correct temperature. Furthermore, the temperature control unit can pre-set optimal temperature profiles for specific dishes and automatically adjust the temperature based on those profiles. This improves cooking accuracy and ensures that cooking is always performed at the optimal temperature.

[0060] The automated cooking robot system can also be equipped with a food management unit. This unit manages the inventory of food in the refrigerator and pantry and can notify the user if necessary ingredients are running low. For example, the unit can scan the barcodes of food items in the refrigerator and record inventory information in a database. It can also calculate the amount of food needed during cooking and notify the user if inventory is running low. Furthermore, the unit can manage the expiration dates of ingredients and suggest using ingredients nearing their expiration date first. This reduces food waste and enables efficient food management.

[0061] The automated cooking robot system can also be equipped with a recipe suggestion unit. This unit can suggest new recipes based on the user's past cooking history and preferences. For example, it can analyze data on dishes the user has made in the past and suggest similar dishes or new variations. It can also consider the user's ingredient inventory and suggest recipes that can be made with the ingredients they have on hand. Furthermore, it can suggest special recipes tailored to the season or events. This increases the user's opportunities to try new dishes and broadens their culinary options.

[0062] The automated cooking robot system can also be equipped with a health management unit that monitors the user's health status. This unit can acquire the user's health data and adjust cooking methods and ingredients based on that data. For example, it can monitor the user's blood glucose and blood pressure levels and suggest meals tailored to their health condition. Furthermore, it can consider the user's allergy information and select ingredients that do not contain allergens. It can also suggest recipes that consider calorie restrictions and nutritional balance to match the user's weight loss goals. This supports the user's health and helps them achieve a healthier diet.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The acquisition unit acquires images of the ingredients and the finished product. For example, the acquisition unit uses a high-resolution camera to take pictures of the ingredients and inputs those images into the generation AI. The generation AI recognizes the type and condition of the ingredients based on those images. The acquisition unit also takes pictures of the finished product. Step 2: The learning unit uses a generative AI to learn cooking methods based on images acquired by the acquisition unit. For example, the learning unit uses deep learning technology to learn how to cook ingredients. The generative AI finds the optimal cooking method through trial and error. It tries different cooking methods, evaluates the results, and selects the best method. Step 3: The instruction unit gives instructions to the automated cooking robot based on the cooking methods learned by the learning unit. For example, the instruction unit sends the cooking procedure learned by the generating AI to the automated cooking robot. The automated cooking robot then performs cooking tasks such as cutting, stir-frying, and boiling the ingredients according to the instructions from the instruction unit.

[0065] (Example of form 2) An automated cooking robot system according to an embodiment of the present invention is a system that uses a generative AI to learn cooking methods. This system acquires images of ingredients and finished products, and the generative AI learns cooking methods based on these images. The generative AI identifies the cooking situation and determines the optimal cooking method through trial and error. Finally, the automated cooking robot prepares the food based on the cooking method learned by the generative AI. This system can significantly reduce the effort required for manufacturing, establishing the operation of, and adjusting automated cooking robots. Furthermore, by learning cooking methods, the generative AI can handle a variety of dishes. In addition, the process of the generative AI learning cooking methods can lead to increased efficiency and improved quality of cooking. For example, it can reduce food waste and shorten cooking time. Also, by optimizing the cooking method, the generative AI can consistently provide dishes of a certain quality. In this way, an automated cooking robot system using generative AI can address labor shortages and reduce wage costs, providing efficient and high-quality food. For example, the ingredient acquisition unit takes images of ingredients with a high-resolution camera, and the generative AI analyzes the images to recognize the type and condition of the ingredients. Next, it acquires images of finished products, and the generative AI learns cooking methods based on these images. Generative AI, for example, uses deep learning technology to learn how to cook ingredients. Through trial and error, the generative AI finds the optimal cooking method. For example, it tries different cooking methods, evaluates the results, and selects the best one. Based on the cooking methods learned by the generative AI, an automated cooking robot prepares the food. For example, the automated cooking robot follows the cooking procedures instructed by the generative AI, performing tasks such as cutting, stir-frying, and boiling ingredients. This enables the provision of efficient and high-quality meals. As a result, the automated cooking robot system reduces food waste, shortens cooking time, and consistently provides meals of the same quality.

[0066] The automated cooking robot system according to this embodiment comprises an acquisition unit, a learning unit, and an instruction unit. The acquisition unit acquires images of ingredients and finished products. For example, the acquisition unit can take images of ingredients using a high-resolution camera. The acquisition unit can also take images of finished products. For example, the acquisition unit takes images of ingredients and inputs those images into a generation AI. The generation AI recognizes the type and state of the ingredients based on those images. The learning unit uses the generation AI to learn cooking methods based on the images acquired by the acquisition unit. For example, the learning unit uses deep learning technology to learn cooking methods for ingredients. The generation AI finds the optimal cooking method through trial and error. For example, the generation AI tries different cooking methods, evaluates the results, and selects the optimal method. The instruction unit gives instructions to the automated cooking robot based on the cooking methods learned by the learning unit. For example, the instruction unit transmits the cooking procedure learned by the generation AI to the automated cooking robot. The automated cooking robot performs cooking such as cutting, stir-frying, and boiling the ingredients according to the instructions from the instruction unit. As a result, the automated cooking robot system according to this embodiment can reduce food waste, shorten cooking time, and consistently provide dishes of a certain quality. For example, the acquisition unit captures images of ingredients with a high-resolution camera, and the generation AI analyzes the images to recognize the type and condition of the ingredients. Next, it acquires images of the finished product, and the generation AI learns cooking methods based on those images. The generation AI learns cooking methods for ingredients, for example, using deep learning technology. The generation AI finds the optimal cooking method through trial and error. For example, the generation AI tries different cooking methods, evaluates the results, and selects the best method. Based on the cooking methods learned by the generation AI, the automated cooking robot prepares the dishes. For example, the automated cooking robot performs cooking procedures such as cutting, stir-frying, and boiling ingredients according to the cooking procedures instructed by the generation AI. This makes it possible to provide efficient and high-quality dishes.

[0067] A system characterized by having a trial unit in which a generative AI performs trial and error, and including specific methods for trial and error. In the trial unit, the generative AI performs trial and error. For example, the trial unit tries different cooking methods, evaluates the results, and selects the optimal method. The generative AI performs trial and error, for example, using deep learning technology. The generative AI finds the optimal cooking method by repeating trial and error. For example, the generative AI tries different cooking methods, evaluates the results, and selects the optimal method. In this way, the generative AI optimizes the cooking method by performing trial and error. For example, the trial unit tries different cooking methods, evaluates the results, and selects the optimal method. The generative AI performs trial and error, for example, using deep learning technology. The generative AI finds the optimal cooking method by repeating trial and error. For example, the generative AI tries different cooking methods, evaluates the results, and selects the optimal method. In this way, the generative AI optimizes the cooking method by performing trial and error.

[0068] A system characterized by having an identification unit that identifies the cooking status. The identification unit identifies the cooking status. For example, the identification unit can identify temperature, time, the state of ingredients, etc. The identification unit identifies the cooking status using sensors, for example. For example, the identification unit measures the temperature during cooking using a temperature sensor and inputs that data into a generating AI. The generating AI identifies the cooking status based on that data. The identification unit also measures the cooking time using a time sensor and inputs that data into the generating AI. The generating AI identifies the cooking status based on that data. Furthermore, the identification unit can use image recognition technology to identify the state of ingredients. For example, the identification unit takes an image of the ingredients and inputs that image into the generating AI. The generating AI identifies the state of the ingredients based on that image. As a result, the accuracy of cooking is improved by the identification unit identifying the cooking status. For example, the identification unit measures the temperature during cooking using a temperature sensor and inputs that data into the generating AI. The generating AI identifies the cooking status based on that data. The identification unit also measures the cooking time using a time sensor and inputs that data into the generating AI. The generating AI identifies the cooking status based on that data. Furthermore, the identification unit can use image recognition technology to identify the state of the ingredients. For example, the identification unit takes a picture of the ingredients and inputs that image into the generating AI. The generating AI then identifies the state of the ingredients based on that image. As a result, the identification unit can improve the accuracy of cooking by identifying the cooking status.

[0069] A system characterized by having a decision unit that determines the cooking method. The decision unit determines the cooking method. For example, the decision unit determines the optimal cooking method based on cooking methods learned by the generative AI. The decision unit determines the cooking method using, for example, deep learning technology. The generative AI finds the optimal cooking method through repeated trial and error. For example, the generative AI tries different cooking methods, evaluates the results, and selects the optimal method. The decision unit determines the optimal cooking method based on the results. In this way, the decision unit can select the optimal cooking method by determining the cooking method. For example, the decision unit determines the optimal cooking method based on cooking methods learned by the generative AI. The decision unit determines the cooking method using, for example, deep learning technology. The generative AI finds the optimal cooking method through repeated trial and error. For example, the generative AI tries different cooking methods, evaluates the results, and selects the optimal method. The decision unit determines the optimal cooking method based on the results. In this way, the decision unit can select the optimal cooking method by determining the cooking method.

[0070] A system characterized by having a communication unit for instructing a robot on cooking methods learned by a generative AI. The communication unit is used when instructing the robot on cooking methods learned by the generative AI. For example, the communication unit transmits the cooking procedures learned by the generative AI to an automated cooking robot using communication protocols such as Wi-Fi or Bluetooth. The communication unit can transmit the cooking procedures learned by the generative AI to an automated cooking robot using Wi-Fi, for example. The communication unit can also transmit the cooking procedures learned by the generative AI to an automated cooking robot using Bluetooth. This allows the communication unit to accurately instruct the robot on cooking methods learned by the generative AI. For example, the communication unit can transmit the cooking procedures learned by the generative AI to an automated cooking robot using Wi-Fi, for example. The communication unit can also transmit the cooking procedures learned by the generative AI to an automated cooking robot using Bluetooth. This allows the communication unit to accurately instruct the robot on cooking methods learned by the generative AI.

[0071] The system is characterized by having an interface unit that instructs a robot on cooking methods learned by a generative AI. The interface unit is used when instructing the robot on cooking methods learned by the generative AI. For example, the interface unit uses an interface such as a touchscreen or voice input to instruct the automated cooking robot on cooking procedures learned by the generative AI. The interface unit can, for example, use a touchscreen to instruct the automated cooking robot on cooking procedures learned by the generative AI. The interface unit can also use voice input to instruct the automated cooking robot on cooking procedures learned by the generative AI. This allows the interface unit to intuitively instruct the robot on cooking methods learned by the generative AI.

[0072] This system is characterized by an acquisition unit that estimates the user's emotions and adjusts the timing of ingredient acquisition based on the estimated emotions. For example, if the user is stressed, the acquisition unit's generating AI will accelerate the ingredient acquisition timing to speed up cooking. Also, if the user is relaxed, the acquisition unit's generating AI can delay the ingredient acquisition timing to slow down cooking. Furthermore, if the user is in a hurry, the acquisition unit's generating AI can optimize the ingredient acquisition timing to speed up cooking. This allows for more appropriate cooking by adjusting the ingredient acquisition timing according to the user's emotions. For example, if the user is stressed, the acquisition unit's generating AI will accelerate the ingredient acquisition timing to speed up cooking. Also, if the user is relaxed, the acquisition unit's generating AI can delay the ingredient acquisition timing to slow down cooking. Furthermore, if the user is in a hurry, the acquisition unit's generating AI can optimize the ingredient acquisition timing to speed up cooking. This allows for more appropriate cooking by adjusting the timing of ingredient retrieval according to the user's emotions.

[0073] This system features an acquisition unit that automatically evaluates the freshness and quality of ingredients and selects the optimal ingredients. For example, the acquisition unit uses a generating AI to analyze images of ingredients and select the freshest ones. Furthermore, the acquisition unit can also use a generating AI to evaluate the quality of ingredients and select the optimal ones. Additionally, the acquisition unit can use a generating AI to analyze the color and shape of ingredients and select the optimal ones. This allows the system to automatically evaluate the freshness and quality of ingredients, ensuring that the best ingredients are always used.

[0074] This system is characterized by its ability to customize the ingredients it acquires by referencing the user's past cooking history. For example, the acquisition unit uses a generating AI to analyze the user's past cooking history and prioritizes acquiring frequently used ingredients. Furthermore, the acquisition unit can also acquire ingredients that were popular in the past, taking the user's preferences into account. Additionally, the acquisition unit can use the generating AI to suggest new ingredients based on the user's past cooking history. This allows the system to acquire ingredients that match the user's preferences by referencing their past cooking history.

[0075] A system characterized by an acquisition unit that estimates the user's emotions and determines the priority of ingredients to acquire based on the estimated user emotions. For example, if the user is stressed, the acquisition unit's generating AI will prioritize acquiring ingredients with relaxing effects. Also, if the user is relaxed, the acquisition unit's generating AI can prioritize acquiring highly nutritious ingredients. Furthermore, if the user is in a hurry, the acquisition unit's generating AI can prioritize acquiring ingredients with short cooking times. In this way, by determining the priority of ingredients according to the user's emotions, more appropriate ingredients can be acquired. For example, if the user is stressed, the acquisition unit's generating AI will prioritize acquiring ingredients with relaxing effects. Also, if the user is relaxed, the acquisition unit's generating AI can prioritize acquiring highly nutritious ingredients. Furthermore, if the user is in a hurry, the acquisition unit's generating AI can prioritize acquiring ingredients with short cooking times. In this way, by determining the priority of ingredients according to the user's emotions, more appropriate ingredients can be acquired.

[0076] This system is characterized by its ability to prioritize the acquisition of regionally specific ingredients when acquiring ingredients, taking into account the user's geographical location information. The acquisition unit prioritizes the acquisition of regionally specific ingredients based on the user's geographical location information. For example, the acquisition unit's generating AI analyzes the user's current location and prioritizes the acquisition of ingredients that are easily available in that area. Furthermore, the acquisition unit can also have the generating AI suggest regionally specific ingredients based on the user's geographical location information. Additionally, the acquisition unit can also have the generating AI prioritize the acquisition of local ingredients, taking into account the user's geographical location information. This allows for the priority acquisition of regionally specific ingredients by considering the user's geographical location information.

[0077] This system is characterized by its acquisition unit selecting ingredients based on the user's dietary restrictions and allergy information. The acquisition unit selects ingredients based on the user's dietary restrictions and allergy information. For example, the acquisition unit's generating AI selects safe ingredients based on the user's allergy information. Furthermore, the acquisition unit's generating AI can also select appropriate ingredients considering the user's dietary restrictions. Additionally, the acquisition unit's generating AI can also select optimal ingredients considering the user's health condition. This ensures that safe and appropriate ingredients are selected by considering the user's dietary restrictions and allergy information.

[0078] A system characterized by a learning unit that estimates the user's emotions and adjusts the cooking method it learns based on the estimated user emotions. For example, if the user is relaxed, the learning unit will have the generating AI learn a relaxed cooking method. Also, if the user is in a hurry, the learning unit can have the generating AI learn a fast cooking method. Furthermore, if the user is excited, the learning unit can have the generating AI learn a visually appealing cooking method. In this way, by adjusting the cooking method learned according to the user's emotions, a more appropriate cooking method can be learned. For example, if the user is relaxed, the generating AI will learn a relaxed cooking method. Also, if the user is in a hurry, the learning unit can have the generating AI learn a fast cooking method. Furthermore, if the user is excited, the learning unit can have the generating AI learn a visually appealing cooking method. In this way, by adjusting the cooking method learned according to the user's emotions, a more appropriate cooking method can be learned.

[0079] A system characterized by its learning unit optimizing its learning algorithm by referencing past cooking data during the learning process. For example, the learning unit allows the generative AI to analyze past cooking data and learn the optimal cooking method. The learning unit can also optimize the learning algorithm based on past successes of the generative AI. Furthermore, the learning unit can analyze past failures of the generative AI and improve the learning algorithm. This allows the learning algorithm to be optimized by referencing past cooking data.

[0080] A system characterized by its learning unit applying different learning methods to different categories of cuisine during the learning process. For example, the learning unit can apply different learning methods to the generating AI for categories such as Japanese, Western, and Chinese cuisine. Furthermore, the learning unit can also apply different learning methods to the generating AI for categories such as desserts, main dishes, and side dishes. Additionally, the learning unit can apply different learning methods to the generating AI for categories such as soups, salads, and grilled dishes. This allows the system to handle a wider variety of cuisines by applying different learning methods to different categories.

[0081] A system characterized by a learning unit that estimates the user's emotions and adjusts the learning progress based on the estimated emotions. The learning unit estimates the user's emotions and adjusts the learning progress based on the estimated emotions. For example, if the user is relaxed, the learning unit will have the generating AI proceed slowly. Also, if the user is in a hurry, the learning unit can have the generating AI proceed quickly. Furthermore, if the user is excited, the learning unit can have the generating AI proceed visually appealing. This allows for more appropriate learning by adjusting the learning progress according to the user's emotions. For example, if the user is relaxed, the learning unit will have the generating AI proceed slowly. Also, if the user is in a hurry, the learning unit can have the generating AI proceed quickly. Furthermore, if the user is excited, the learning unit can have the generating AI proceed visually appealing. This allows for more appropriate learning by adjusting the learning progress according to the user's emotions.

[0082] This system is characterized by its learning unit selecting training data while considering the seasonality of ingredients. For example, the learning unit allows the generating AI to consider seasonal ingredients and select the optimal training data. Furthermore, the learning unit can also allow the generating AI to learn seasonal dishes and optimal cooking methods. Additionally, the learning unit can allow the generating AI to consider the characteristics of seasonal ingredients and select training data. This allows for the selection of more appropriate training data by considering the seasonality of ingredients.

[0083] This system is characterized by its learning unit customizing learning content based on the user's food preferences during the learning process. For example, the learning unit's generating AI considers the user's preferences and selects the optimal learning content. Furthermore, the learning unit's generating AI can also customize learning content based on the user's past cooking history. Additionally, the learning unit's generating AI can analyze the user's food preferences and optimize the learning content. This allows the system to learn more appropriate cooking methods by customizing learning content based on the user's food preferences.

[0084] A system characterized by an instruction unit that estimates the user's emotions and adjusts the way instructions are expressed based on the estimated emotions. For example, if the user is relaxed, the generating AI will give instructions in a gentle manner. Also, if the user is in a hurry, the generating AI can give concise and quick instructions. Furthermore, if the user is excited, the generating AI can give visually appealing instructions. This allows for more appropriate instructions by adjusting the way instructions are expressed according to the user's emotions.

[0085] This system is characterized by an instruction unit that monitors the progress of cooking in real time when issuing instructions and modifies the instructions as needed. For example, the instruction unit's generating AI monitors the progress of cooking in real time and modifies the instructions as needed. The instruction unit can also have the generating AI analyze the progress of cooking and provide optimal instructions. Furthermore, the instruction unit can optimize the instructions based on the progress of cooking. This improves cooking accuracy by monitoring the progress of cooking in real time and modifying the instructions as needed.

[0086] A system characterized by an instruction unit that generates optimal instructions based on the performance and characteristics of the cooking robot when instructions are given. The instruction unit generates optimal instructions based on the performance and characteristics of the cooking robot when instructions are given. For example, the instruction unit's generating AI considers the performance of the cooking robot and generates optimal instructions. The instruction unit can also generate optimal instructions by having the generating AI analyze the characteristics of the cooking robot. Furthermore, the instruction unit can optimize instructions based on the movements of the cooking robot by having the generating AI optimize the instructions. As a result, the accuracy of cooking is improved by generating optimal instructions based on the performance and characteristics of the cooking robot. For example, the instruction unit's generating AI considers the performance of the cooking robot and generates optimal instructions. The instruction unit can also generate optimal instructions by having the generating AI analyze the characteristics of the cooking robot. Furthermore, the instruction unit can optimize instructions based on the movements of the cooking robot by having the generating AI optimize the instructions. As a result, the accuracy of cooking is improved by generating optimal instructions based on the performance and characteristics of the cooking robot.

[0087] A system characterized by an instruction unit that estimates the user's emotions and determines the priority of instructions based on the estimated emotions. The instruction unit estimates the user's emotions and determines the priority of instructions based on the estimated emotions. For example, if the user is feeling stressed, the instruction unit's generating AI will prioritize important instructions. Also, if the user is relaxed, the instruction unit's generating AI can prioritize detailed instructions. Furthermore, if the user is in a hurry, the instruction unit's generating AI can prioritize quick instructions. This allows for more appropriate instructions by determining the priority of instructions according to the user's emotions. For example, if the user is feeling stressed, the instruction unit's generating AI will prioritize important instructions. Also, if the user is relaxed, the instruction unit's generating AI can prioritize detailed instructions. Furthermore, if the user is in a hurry, the instruction unit's generating AI can prioritize quick instructions. This allows for more appropriate instructions by determining the priority of instructions according to the user's emotions.

[0088] This system is characterized by its instruction unit adjusting the instruction content while considering the temperature and humidity of the cooking environment. For example, the instruction unit's generating AI considers the temperature of the cooking environment and provides optimal instructions. Furthermore, the instruction unit's generating AI can analyze the humidity of the cooking environment and provide optimal instructions. Additionally, the instruction unit can adjust the instruction content based on changes in the cooking environment. This allows for more appropriate instructions by considering the temperature and humidity of the cooking environment.

[0089] This system is characterized by its ability to customize instructions by referencing the user's past cooking history when issuing instructions. For example, the instruction unit's generating AI analyzes the user's past cooking history and provides optimal instructions. Furthermore, the instruction unit's generating AI can also consider the user's preferences and customize the instructions. Additionally, the instruction unit's generating AI can optimize the instructions based on the user's past cooking history. This allows for more appropriate instructions by referencing the user's past cooking history.

[0090] A system characterized by a trial unit that estimates the user's emotions and adjusts the frequency of trials based on the estimated emotions. The trial unit estimates the user's emotions and adjusts the frequency of trials based on the estimated emotions. For example, if the user is relaxed, the trial unit reduces the frequency of trials and performs a leisurely cooking process. Also, if the user is in a hurry, the trial unit can increase the frequency of trials and perform a rapid cooking process. Furthermore, if the user is excited, the trial unit can adjust the frequency of trials and perform a visually appealing cooking process. This allows for more appropriate trials by adjusting the frequency of trials according to the user's emotions. For example, if the user is relaxed, the trial unit reduces the frequency of trials and performs a leisurely cooking process. Also, if the user is in a hurry, the trial unit can increase the frequency of trials and perform a rapid cooking process. Furthermore, if the user is excited, the trial unit can adjust the frequency of trials and perform a visually appealing cooking process. This allows for more appropriate trials by adjusting the frequency of trials according to the user's emotions.

[0091] A system characterized by a trial unit that optimizes the trial algorithm by referring to past trial data during the trial. The trial unit optimizes the trial algorithm by referring to past trial data during the trial. For example, the trial unit analyzes past trial data and applies the optimal trial algorithm. The trial unit can also optimize the trial algorithm based on past successes. Furthermore, the trial unit can analyze past failures and improve the trial algorithm. This allows for the optimization of the trial algorithm by referring to past trial data.

[0092] A system characterized by a trial unit that estimates the user's emotions and determines the order of trials based on the estimated user emotions. The trial unit estimates the user's emotions and determines the order of trials based on the estimated user emotions. For example, if the user is relaxed, the trial unit will make the order of trials slower. Also, if the user is in a hurry, the trial unit can also advance the order of trials quickly. Furthermore, if the user is excited, the trial unit can also make the order of trials visually appealing. This allows for more appropriate trials by determining the order of trials according to the user's emotions. For example, if the user is relaxed, the trial unit will make the order of trials slower. Also, if the user is in a hurry, the trial unit can also advance the order of trials quickly. Furthermore, if the user is excited, the trial unit can also make the order of trials visually appealing. This allows for more appropriate trials by determining the order of trials according to the user's emotions.

[0093] A system characterized by a trial unit that combines different cooking methods during the trial process to conduct new trials. The trial unit combines different cooking methods during the trial process to conduct new trials. For example, the trial unit combines different cooking methods to test new recipes. The trial unit can also combine different cooking techniques to test the optimal cooking method. Furthermore, the trial unit can combine different ingredients to test new dishes. This allows for new trials by combining different cooking methods. For example, the trial unit combines different cooking methods to test new recipes. The trial unit can also combine different cooking techniques to test the optimal cooking method. Furthermore, the trial unit can combine different ingredients to test new dishes. This allows for new trials by combining different cooking methods.

[0094] A system characterized by an identification unit that estimates the user's emotions and adjusts the accuracy of identification based on the estimated emotions. The identification unit estimates the user's emotions and adjusts the accuracy of identification based on the estimated emotions. For example, the identification unit increases the accuracy of identification when the user is relaxed. Also, the identification unit can speed up the accuracy of identification when the user is in a hurry. Furthermore, the identification unit can make the accuracy of identification visually appealing when the user is excited. This allows for more appropriate identification by adjusting the accuracy of identification according to the user's emotions. For example, the identification unit increases the accuracy of identification when the user is relaxed. Also, the identification unit can speed up the accuracy of identification when the user is in a hurry. Furthermore, the identification unit can make the accuracy of identification visually appealing when the user is excited. This allows for more appropriate identification by adjusting the accuracy of identification according to the user's emotions.

[0095] A system characterized by an identification unit that optimizes its identification algorithm by referring to past identification data during the identification process. The identification unit optimizes its identification algorithm by referring to past identification data during the identification process. For example, the identification unit analyzes past identification data and applies the optimal identification algorithm. The identification unit can also optimize its identification algorithm based on past successes. Furthermore, the identification unit can analyze past failures and improve its identification algorithm. This allows for the optimization of the identification algorithm by referring to past identification data.

[0096] A system characterized by an identification unit that estimates the user's emotions and determines the priority of identification based on the estimated user emotions. The identification unit estimates the user's emotions and determines the priority of identification based on the estimated user emotions. For example, if the user is feeling stressed, the identification unit will prioritize important identification. Also, if the user is relaxed, the identification unit can prioritize detailed identification. Furthermore, if the user is in a hurry, the identification unit can prioritize rapid identification. This allows for more appropriate identification by determining the priority of identification according to the user's emotions. For example, if the user is feeling stressed, the identification unit will prioritize important identification. Also, if the user is relaxed, the identification unit can prioritize detailed identification. Furthermore, if the user is in a hurry, the identification unit can prioritize rapid identification. This allows for more appropriate identification by determining the priority of identification according to the user's emotions.

[0097] This system is characterized by its identification unit combining different ingredients and cooking conditions during the identification process. The identification unit combines different ingredients and cooking conditions during the identification process. For example, the identification unit combines different ingredients to achieve optimal identification. Furthermore, the identification unit can also combine different cooking conditions to achieve optimal identification. In addition, the identification unit can combine different cooking techniques to achieve optimal identification. This allows for more accurate identification by combining different ingredients and cooking conditions.

[0098] A system characterized by a decision-making unit that estimates the user's emotions and adjusts the criteria for judgment based on the estimated emotions. The decision-making unit estimates the user's emotions and adjusts the criteria for judgment based on the estimated emotions. For example, if the user is relaxed, the decision-making unit will loosen the criteria for judgment. Also, if the user is in a hurry, the decision-making unit can also make the criteria for judgment quicker. Furthermore, if the user is excited, the decision-making unit can also make the criteria for judgment visually appealing. This allows for more appropriate judgments by adjusting the criteria for judgment according to the user's emotions. For example, if the user is relaxed, the decision-making unit will loosen the criteria for judgment. Also, if the user is in a hurry, the decision-making unit can also make the criteria for judgment quicker. Furthermore, if the user is excited, the decision-making unit can also make the criteria for judgment visually appealing. This allows for more appropriate judgments by adjusting the criteria for judgment according to the user's emotions.

[0099] A system characterized by a decision unit that optimizes its decision algorithm by referring to past decision data when making a decision. The decision unit optimizes its decision algorithm by referring to past decision data when making a decision. For example, the decision unit analyzes past decision data and applies the optimal decision algorithm. The decision unit can also optimize its decision algorithm based on past successes. Furthermore, the decision unit can analyze past failures and improve its decision algorithm. This allows the decision algorithm to be optimized by referring to past decision data.

[0100] A system characterized by a decision-making unit that estimates the user's emotions and determines the priority of decisions based on the estimated emotions. The decision-making unit estimates the user's emotions and determines the priority of decisions based on the estimated emotions. For example, if the user is feeling stressed, the decision-making unit will prioritize important decisions. Also, if the user is relaxed, the decision-making unit can prioritize detailed decisions. Furthermore, if the user is in a hurry, the decision-making unit can prioritize quick decisions. This allows for more appropriate decisions by determining the priority of decisions according to the user's emotions. For example, if the user is feeling stressed, the decision-making unit will prioritize important decisions. Also, if the user is relaxed, the decision-making unit can prioritize detailed decisions. Furthermore, if the user is in a hurry, the decision-making unit can prioritize quick decisions. This allows for more appropriate decisions by determining the priority of decisions according to the user's emotions.

[0101] A system characterized by a decision-making unit that combines different cooking methods to make a new decision. The decision-making unit combines different cooking methods to make a new decision. For example, the decision-making unit combines different cooking methods to make a new decision. Furthermore, the decision-making unit can combine different cooking techniques to make the optimal decision. In addition, the decision-making unit can combine different ingredients to make a new decision. This makes it possible to make new decisions by combining different cooking methods. For example, the decision-making unit combines different cooking methods to make a new decision. Furthermore, the decision-making unit can combine different cooking techniques to make the optimal decision. Furthermore, the decision-making unit can combine different ingredients to make a new decision. This makes it possible to make new decisions by combining different cooking methods.

[0102] A system characterized by a communication unit that estimates the user's emotions and adjusts the frequency of communication based on the estimated emotions. For example, the communication unit reduces the frequency of communication when the user is relaxed. It can also increase the frequency of communication when the user is in a hurry. Furthermore, the communication unit can adjust the frequency of communication when the user is excited. This allows for more appropriate communication by adjusting the frequency of communication according to the user's emotions.

[0103] A system characterized by a communication unit that optimizes the communication algorithm by referring to past communication data during communication. The communication unit optimizes the communication algorithm by referring to past communication data during communication. For example, the communication unit analyzes past communication data and applies the optimal communication algorithm. The communication unit can also optimize the communication algorithm based on past successes. Furthermore, the communication unit can analyze past failures and improve the communication algorithm. This allows for the optimization of the communication algorithm by referring to past communication data. For example, the communication unit analyzes past communication data and applies the optimal communication algorithm. The communication unit can also optimize the communication algorithm based on past successes. Furthermore, the communication unit can analyze past failures and improve the communication algorithm. This allows for the optimization of the communication algorithm by referring to past communication data.

[0104] A system characterized by a communication unit that estimates the user's emotions and determines communication priorities based on the estimated emotions. For example, if the user is stressed, the communication unit prioritizes important communications. Furthermore, if the user is relaxed, the communication unit can prioritize detailed communications. Additionally, if the user is in a hurry, the communication unit can prioritize urgent communications. This allows for more appropriate communication by prioritizing communications according to the user's emotions.

[0105] A system characterized by its communication unit combining different communication protocols during communication. The communication unit combines different communication protocols during communication. For example, the communication unit combines different communication protocols to achieve optimal communication. Furthermore, the communication unit can combine different communication technologies to achieve optimal communication. In addition, the communication unit can combine different communication means to achieve optimal communication. This allows for more appropriate communication by combining different communication protocols. For example, the communication unit combines different communication protocols to achieve optimal communication. Furthermore, the communication unit can combine different communication technologies to achieve optimal communication. Furthermore, the communication unit can combine different communication means to achieve optimal communication. This allows for more appropriate communication by combining different communication protocols.

[0106] A system characterized by an interface unit that estimates the user's emotions and adjusts the interface display method based on the estimated user emotions. The interface unit estimates the user's emotions and adjusts the interface display method based on the estimated user emotions. For example, if the user is tense, the interface unit provides an interface with calm colors. Also, if the user is enjoying themselves, the interface unit can provide an interface with bright colors. Furthermore, if the user is tired, the interface unit can provide a simple and highly visible interface. This allows for a more appropriate display by adjusting the interface display method according to the user's emotions. For example, if the user is tense, the interface unit provides an interface with calm colors. Also, if the user is enjoying themselves, the interface unit can provide an interface with bright colors. Furthermore, if the user is tired, the interface unit can provide a simple and highly visible interface. This allows for a more appropriate display by adjusting the interface display method according to the user's emotions.

[0107] A system characterized by an interface unit that, when displaying the interface, selects the optimal display method by referring to the user's past operation history. The interface unit selects the optimal display method by referring to the user's past operation history when displaying the interface. For example, the interface unit selects the optimal display method based on the user's past operation history. Furthermore, the interface unit can also customize the display method considering the user's preferences. In addition, the interface unit can analyze the user's past operation history and optimize the display method. This allows the system to select the optimal display method by referring to the user's past operation history. For example, the interface unit selects the optimal display method based on the user's past operation history. Furthermore, the interface unit can also customize the display method considering the user's preferences. Furthermore, the interface unit can analyze the user's past operation history and optimize the display method. This allows the system to select the optimal display method by referring to the user's past operation history.

[0108] A system characterized by an interface unit that estimates the user's emotions and adjusts the interface's operation procedures based on the estimated user emotions. For example, if the user is tense, the interface unit provides simple operation procedures. Furthermore, if the user is enjoying themselves, the interface unit can provide detailed operation procedures. Additionally, if the user is tired, the interface unit can provide concise operation procedures. This allows for more appropriate operation by adjusting the interface's operation procedures according to the user's emotions.

[0109] A system characterized by an interface unit that, when displaying the interface, selects the optimal display method considering the user's device information. For example, if the user is using a smartphone, the interface unit provides a display method adapted to the screen size. Furthermore, if the user is using a tablet, the interface unit can also provide a display method optimized for larger screens. Additionally, if the user is using a smartwatch, the interface unit can provide a concise and highly visible display method. This allows the system to select the optimal display method by considering the user's device information. === Hard Collateral 1-1 === Each of the multiple elements described above, including the acquisition unit, learning unit, instruction unit, trial unit, identification unit, judgment unit, communication unit, and interface unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit uses the camera 42 of the smart device 14 to capture an image of the ingredients, and the generating AI analyzes the image to recognize the type and state of the ingredients. The learning unit is implemented in the specific processing unit 290 of the data processing unit 12 and learns cooking methods using deep learning technology. The instruction unit is implemented in the specific processing unit 46A of the smart device 14 and transmits the cooking procedure learned by the generating AI to the automated cooking robot. The trial unit is implemented in the specific processing unit 290 of the data processing unit 12 and tries different cooking methods, evaluates the results, and selects the optimal method. The identification unit identifies the cooking status using the sensors of the smart device 14 and inputs that data to the generating AI. The judgment unit is implemented in the specific processing unit 290 of the data processing unit 12 and determines the optimal cooking method based on the cooking methods learned by the generating AI. The communication unit transmits the cooking procedure learned by the generating AI to the automated cooking robot, for example, using the communication I / F 44 of the smart device 14. The interface unit instructs the automated cooking robot on the cooking procedure learned by the generating AI, for example, using the touchscreen of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements described above, including the acquisition unit, learning unit, instruction unit, trial unit, identification unit, decision unit, communication unit, and interface unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit uses the camera 42 of the smart glasses 214 to capture an image of the food, and the generating AI analyzes the image to recognize the type and state of the food. The learning unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and learns cooking methods using deep learning technology. The instruction unit is implemented, for example, by the control unit 46A of the smart glasses 214, and transmits the cooking procedure learned by the generating AI to the automated cooking robot. The trial unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and tries different cooking methods, evaluates the results, and selects the optimal method. The identification unit uses the sensors of the smart glasses 214 to identify the cooking status and inputs that data to the generating AI. The decision unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and determines the optimal cooking method based on the cooking methods learned by the generating AI. The communication unit transmits the cooking procedure learned by the generating AI to the automated cooking robot, for example, using the communication I / F 44 of the smart glasses 214. The interface unit instructs the automated cooking robot on the cooking procedure learned by the generating AI, for example, using the touchscreen of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements described above, including the acquisition unit, learning unit, instruction unit, trial unit, identification unit, decision unit, communication unit, and interface unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit uses the camera 42 of the headset terminal 314 to capture images of ingredients, and the generating AI analyzes the images to recognize the type and state of the ingredients. The learning unit is implemented in the identification processing unit 290 of the data processing unit 12 and learns cooking methods using deep learning technology. The instruction unit is implemented in the control unit 46A of the headset terminal 314 and transmits the cooking procedure learned by the generating AI to the automated cooking robot. The trial unit is implemented in the identification processing unit 290 of the data processing unit 12 and tries different cooking methods, evaluates the results, and selects the optimal method. The identification unit identifies the cooking status using the sensors of the headset terminal 314 and inputs that data to the generating AI. The decision unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and determines the optimal cooking method based on the cooking methods learned by the generating AI. The communication unit transmits the cooking procedure learned by the generating AI to the automated cooking robot, for example, using the communication I / F 44 of the headset terminal 314. The interface unit instructs the automated cooking robot on the cooking procedure learned by the generating AI, for example, using the touchscreen of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements described above, including the acquisition unit, learning unit, instruction unit, trial unit, identification unit, judgment unit, communication unit, and interface unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the acquisition unit uses the camera 42 of the robot 414 to capture images of the ingredients, and the generating AI analyzes the images to recognize the type and state of the ingredients. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and learns cooking methods using deep learning technology. The instruction unit is implemented, for example, by the control unit 46A of the robot 414, and transmits the cooking procedure learned by the generating AI to the automated cooking robot. The trial unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and tries different cooking methods, evaluates the results, and selects the optimal method. The identification unit uses the sensors of the robot 414 to identify the cooking status and inputs the data to the generating AI. The judgment unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and determines the optimal cooking method based on the cooking methods learned by the generating AI. The communication unit transmits the cooking procedure learned by the generating AI to the automated cooking robot, for example, using the communication I / F 44 of the robot 414. The interface unit instructs the automated cooking robot on the cooking procedure learned by the generating AI, for example, using the touchscreen of the robot 414.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The automated cooking robot system can also be equipped with a voice recognition unit. The voice recognition unit can acquire voice instructions from the user and adjust the cooking method based on those instructions. For example, if the user says, "Add a little more salt," the voice recognition unit analyzes the instruction and transmits it to the generating AI. The generating AI adjusts the cooking method based on that instruction and adds the appropriate amount of salt. The voice recognition unit can also provide appropriate answers to questions the user asks during cooking. This allows the user to give instructions without using their hands during cooking, enabling more efficient cooking.

[0112] The automated cooking robot system can also be equipped with a temperature control unit. This unit can monitor the cooking temperature in real time and adjust it as needed. For example, if the temperature is too high during cooking, it can temporarily pause heating and return to an appropriate temperature. Conversely, if the temperature is too low, it can intensify heating to maintain the correct temperature. Furthermore, the temperature control unit can pre-set optimal temperature profiles for specific dishes and automatically adjust the temperature based on those profiles. This improves cooking accuracy and ensures that cooking is always performed at the optimal temperature.

[0113] The automated cooking robot system can also be equipped with a food management unit. This unit manages the inventory of food in the refrigerator and pantry and can notify the user if necessary ingredients are running low. For example, the unit can scan the barcodes of food items in the refrigerator and record inventory information in a database. It can also calculate the amount of food needed during cooking and notify the user if inventory is running low. Furthermore, the unit can manage the expiration dates of ingredients and suggest using ingredients nearing their expiration date first. This reduces food waste and enables efficient food management.

[0114] The automated cooking robot system can also be equipped with a recipe suggestion unit. This unit can suggest new recipes based on the user's past cooking history and preferences. For example, it can analyze data on dishes the user has made in the past and suggest similar dishes or new variations. It can also consider the user's ingredient inventory and suggest recipes that can be made with the ingredients they have on hand. Furthermore, it can suggest special recipes tailored to the season or events. This increases the user's opportunities to try new dishes and broadens their culinary options.

[0115] The automated cooking robot system can also be equipped with a health management unit that monitors the user's health status. This unit can acquire the user's health data and adjust cooking methods and ingredients based on that data. For example, it can monitor the user's blood glucose and blood pressure levels and suggest meals tailored to their health condition. Furthermore, it can consider the user's allergy information and select ingredients that do not contain allergens. It can also suggest recipes that consider calorie restrictions and nutritional balance to match the user's weight loss goals. This supports the user's health and helps them achieve a healthier diet.

[0116] The automated cooking robot system can estimate the user's emotions and adjust the cooking speed based on that estimation. For example, if the user is stressed, the system can speed up the cooking process to complete the meal quickly. If the user is relaxed, the system can slow down the cooking process to allow for a more leisurely cooking experience. Furthermore, if the user is in a hurry, the system can optimize the cooking speed to ensure efficient cooking. By adjusting the cooking speed according to the user's emotions, the system enables more appropriate cooking.

[0117] The automated cooking robot system's learning unit can estimate the user's emotions and adjust the difficulty level of the recipes it learns based on those emotions. For example, if the user is relaxed, the learning unit will learn more difficult recipes. Conversely, if the user is stressed, it can learn easier recipes. Furthermore, if the user is excited, it can learn visually appealing recipes. By adjusting the difficulty level of the recipes learned according to the user's emotions, the system can learn more appropriate recipes.

[0118] The automated cooking robot system can use a control unit to estimate the user's emotions and adjust the level of detail in the cooking instructions based on those emotions. For example, if the user is relaxed, the control unit can provide detailed instructions. If the user is in a hurry, it can provide concise instructions. Furthermore, if the user is excited, it can provide visually appealing instructions. By adjusting the level of detail in the cooking instructions according to the user's emotions, more appropriate cooking becomes possible.

[0119] The automated cooking robot system can have a trial unit that estimates the user's emotions and select the type of trial based on those emotions. For example, if the user is relaxed, the trial unit will try a new cooking method. If the user is stressed, the trial unit can also try an existing cooking method. Furthermore, if the user is excited, the trial unit can try a visually appealing cooking method. By selecting the type of trial according to the user's emotions, more appropriate trials become possible.

[0120] The automated cooking robot system's identification unit can estimate the user's emotions and adjust its identification accuracy based on those emotions. For example, the identification unit can increase its accuracy when the user is relaxed. It can also speed up identification when the user is in a hurry. Furthermore, it can make identification visually appealing when the user is excited. By adjusting the identification accuracy according to the user's emotions, more appropriate identification becomes possible.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The acquisition unit acquires images of the ingredients and the finished product. For example, the acquisition unit uses a high-resolution camera to take pictures of the ingredients and inputs those images into the generation AI. The generation AI recognizes the type and condition of the ingredients based on those images. The acquisition unit also takes pictures of the finished product. Step 2: The learning unit uses a generative AI to learn cooking methods based on images acquired by the acquisition unit. For example, the learning unit uses deep learning technology to learn how to cook ingredients. The generative AI finds the optimal cooking method through trial and error. It tries different cooking methods, evaluates the results, and selects the best method. Step 3: The instruction unit gives instructions to the automated cooking robot based on the cooking methods learned by the learning unit. For example, the instruction unit sends the cooking procedure learned by the generating AI to the automated cooking robot. The automated cooking robot then performs cooking tasks such as cutting, stir-frying, and boiling the ingredients according to the instructions from the instruction unit.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0176] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0186] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An acquisition unit that acquires images of ingredients and the finished product, A learning unit that learns cooking methods based on images acquired by the acquisition unit, An instruction unit that gives instructions to an automated cooking robot based on the cooking method learned by the learning unit, Equipped with A system characterized by the following features.

2. The generating AI includes a trial unit that performs trial and error, and includes specific methods for trial and error. The system according to feature 1.

3. It is equipped with an identification unit that identifies the cooking status. The system according to feature 1.

4. It is equipped with a judgment unit that determines the cooking method. The system according to feature 1.

5. It includes a communication unit for instructing the robot on cooking methods learned by the generation AI. The system according to feature 1.

6. It features an interface unit that instructs the robot on cooking methods learned by the generation AI. The system according to feature 1.

7. The acquisition unit is, The system estimates the user's emotions and adjusts the timing of ingredient acquisition using specific methods based on those estimated emotions. The system according to feature 1.

8. The acquisition unit is, The system automatically evaluates the freshness and quality of ingredients and selects appropriate ingredients based on specific criteria. The system according to feature 1.

9. The acquisition unit is, When retrieving ingredients, the system customizes the ingredients retrieved by referencing the user's past cooking history. The system according to feature 1.

Images