System

Abstract

An object of a system according to an embodiment is to grasp the content of a refrigerator, propose an appropriate menu, and efficiently order missing ingredients.SOLUTION: A system according to an embodiment includes a photographing unit, a receiving unit, an analyzing unit, a proposing unit, and an ordering unit. The photographing unit photographs a picture of the contents of the refrigerator. The reception unit inputs a photograph taken by the photographing unit to the system. The analysis unit analyzes the photograph input by the reception unit and identifies food ingredients in the refrigerator. The suggestion unit suggests a menu on the basis of the foodstuff identified by the analysis unit. The order unit orders the insufficient food materials proposed by the proposal unit using the electronic commerce system.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the drawback of making it difficult to understand the contents of a refrigerator and suggest appropriate menu items, and it is also difficult to efficiently order ingredients that are missing.

[0005] The system according to the embodiment aims to grasp the contents of the refrigerator, propose appropriate menu items, and efficiently order any missing ingredients. [Means for solving the problem]

[0006] The system according to the embodiment includes a photographing unit, a reception unit, an analysis unit, a proposal unit, and an ordering unit. The photographing unit takes a photograph of the contents of the refrigerator. The reception unit inputs the photograph taken by the photographing unit into the system. The analysis unit analyzes the photograph input by the reception unit and identifies ingredients in the refrigerator. The proposal unit proposes a menu based on the ingredients identified by the analysis unit. The ordering unit orders the missing ingredients proposed by the proposal unit using an e-commerce system. [Effects of the Invention]

[0007] The system according to the embodiment can grasp the contents of the refrigerator, suggest appropriate menu items, and efficiently order any missing ingredients. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention takes a photo of the contents of a refrigerator and uses the photo to suggest the perfect menu for tonight's dinner. In this system, a user takes a photo of the contents of the refrigerator and inputs the photo into the system. The system then uses AI to analyze the photo and identify the ingredients in the refrigerator. Based on the identified ingredients, the AI ​​suggests the perfect menu for tonight's dinner. Furthermore, the system suggests dishes that can be made by purchasing missing ingredients, and users can order them directly using an e-commerce system. This allows the system to effectively utilize the contents of the refrigerator and easily prepare dinner. For example, if a user wants to make a dish using chicken and vegetables, the AI ​​suggests the perfect recipe and allows them to order missing seasonings and additional ingredients online. This allows the user to easily prepare dinner.

[0029] A proposal system according to an embodiment includes a photographing unit, a reception unit, an analysis unit, a proposal unit, and an ordering unit. The photographing unit takes a photo of the contents of a refrigerator. For example, a user photographs ingredients in the refrigerator. The reception unit inputs the photo taken by the photographing unit into the system. For example, the user uploads the photo taken by the user to the system. The analysis unit uses AI to analyze the photo input by the reception unit and identify ingredients in the refrigerator. For example, the analysis unit uses image recognition technology to identify ingredients appearing in the photo. The proposal unit uses AI to propose a menu based on the ingredients identified by the analysis unit. For example, the proposal unit proposes a menu that takes into consideration nutritional balance and cooking time based on the identified ingredients. The ordering unit orders missing ingredients proposed by the proposal unit using an e-commerce system. For example, the ordering unit allows the user to order missing ingredients online. This allows the proposal system according to an embodiment to effectively utilize the contents of the refrigerator and easily prepare dinner.

[0030] The analysis unit can identify ingredients in the refrigerator. The analysis unit can use, for example, image recognition technology to identify ingredients in the refrigerator. For example, the analysis unit can identify ingredients in a photograph and identify each ingredient. The analysis unit can also refer to a database to obtain detailed information about the identified ingredients. For example, the analysis unit can obtain information such as the name, type, and expiration date of the ingredient. This allows the analysis unit to accurately identify ingredients in the refrigerator.

[0031] The suggestion unit can suggest a menu based on the identified ingredients. For example, the suggestion unit can suggest a menu that takes into consideration nutritional balance and cooking time based on the identified ingredients. For example, the suggestion unit can suggest a dish using chicken and vegetables. The suggestion unit can also suggest an optimal menu by taking into consideration the user's preferences and past eating history. For example, the suggestion unit can suggest a new menu based on dishes that the user has enjoyed eating in the past. This allows the suggestion unit to suggest an optimal menu based on the identified ingredients.

[0032] The suggestion unit can suggest missing ingredients. For example, the suggestion unit suggests missing ingredients based on the identified ingredients. For example, the suggestion unit suggests a dish using chicken and vegetables, and suggests missing condiments or additional ingredients. The suggestion unit can also provide a list of missing ingredients so that the user can order online. For example, the suggestion unit lists missing ingredients, allowing the user to easily order. In this way, the suggestion unit can suggest missing ingredients, allowing the user to grasp the ingredients they need.

[0033] The ordering unit can order missing ingredients using an e-commerce system. The ordering unit, for example, allows a user to order missing ingredients online. For example, the ordering unit orders missing ingredients using an e-commerce system. The ordering unit can also allow a user to check the delivery status of the ordered ingredients. For example, the ordering unit displays the delivery status of the ordered ingredients in real time. This allows the ordering unit to easily order missing ingredients.

[0034] The suggestion unit can suggest a new menu based on ingredients ordered online by the user. For example, the suggestion unit suggests a new menu based on ingredients ordered online by the user. For example, the suggestion unit suggests a new dish using chicken and vegetables ordered by the user. The suggestion unit can also suggest a new menu by taking into consideration the user's preferences and past eating history. For example, the suggestion unit suggests a new menu based on dishes that the user has liked to eat in the past. This allows the suggestion unit to suggest a new menu based on ingredients ordered online.

[0035] The photographing unit can set photographing conditions taking into account the temperature and humidity inside the refrigerator. For example, when the temperature inside the refrigerator is high, the system automatically adjusts the exposure to take a clear photograph. Also, when the humidity inside the refrigerator is high, the system automatically adjusts the focus to take a clear photograph. For example, the photographing unit sets optimal photographing conditions taking into account both the temperature and humidity inside the refrigerator. This allows the photographing unit to provide optimal photographing conditions according to the environment inside the refrigerator.

[0036] The photographing unit can automatically adjust the arrangement of ingredients in the refrigerator to make it easier to photograph. For example, the photographing unit can have the system automatically organize the ingredients and arrange them in a way that makes it easier to photograph. The photographing unit can also have the system adjust the position of ingredients so that important ingredients are easier to see. For example, the photographing unit can have the system classify ingredients and organize them by category before photographing them. This allows the photographing unit to automatically adjust the arrangement of ingredients, making it easier to photograph.

[0037] The photographing unit can evaluate the freshness of ingredients and photograph them in order of freshness. For example, the photographing unit allows the system to evaluate the freshness of ingredients and photograph them in order of freshness. The photographing unit can also allow the system to prioritize photographing ingredients with low freshness and suggest using them sooner. For example, the photographing unit allows the system to determine the optimal photographing order based on the freshness evaluation results. This allows the photographing unit to prioritize photographing ingredients with high freshness, thereby making effective use of fresh ingredients.

[0038] The camera unit can automatically adjust the lighting inside the refrigerator to provide an optimal shooting environment. For example, the camera unit can automatically adjust the lighting inside the refrigerator to optimize brightness. The camera unit can also adjust the color temperature of the lighting so that ingredients are photographed in natural colors. For example, the camera unit can adjust the angle of the lighting to prevent shadows. This allows the camera unit to optimize the lighting inside the refrigerator and take clear photos.

[0039] The photographing unit records the position information of ingredients in the refrigerator and can refer to it the next time the photograph is taken. For example, the photographing unit may have a system record the position information of ingredients and place them in the same position the next time the photograph is taken. The photographing unit may also have a system suggest the optimal placement for the next photograph based on the position information of ingredients. For example, the photographing unit may have a system analyze the position information of ingredients and suggest an efficient placement method. In this way, by recording the position information of ingredients, the photographing unit can suggest an efficient placement the next time the photograph is taken.

[0040] The photographing unit can automatically read and record the barcodes of ingredients in the refrigerator. For example, the photographing unit allows a system to automatically read the barcodes of ingredients and record them in a database. The photographing unit can also allow a system to obtain detailed information about ingredients based on the barcode information. For example, the photographing unit allows a system to manage the expiration dates of ingredients based on the barcode information. This allows the photographing unit to obtain detailed information by reading the barcodes of ingredients.

[0041] The reception unit can select a reception method by referring to the user's past usage history. The reception unit, for example, proposes the optimal reception method based on the user's past usage history. The reception unit can also analyze the user's past usage history and propose the most efficient reception method. For example, the reception unit preferentially proposes reception methods that the user has frequently used in the past. This allows the reception unit to provide the optimal reception method based on the user's past usage history.

[0042] The reception unit can select a reception means depending on the user's input method. For example, if the user desires voice input, the reception unit provides a voice recognition function. Also, if the user desires text input, the reception unit can provide keyboard input. For example, if the user desires image input, the reception unit provides an image recognition function. This allows the reception unit to provide the optimal reception means depending on the user's input method.

[0043] The reception unit can customize the reception contents based on the user's current situation. For example, when the user uses the service at night, the reception unit provides a night mode interface. Furthermore, when the user uses the service in a specific location, the reception unit can also provide the reception contents that are optimal for that location. For example, the reception unit suggests the optimal reception contents based on the user's current situation. This allows the reception unit to provide the optimal reception contents according to the user's situation.

[0044] The reception unit can provide a reception method taking into consideration device information of the user. For example, if the user is using a smartphone, the reception unit can provide a reception method optimized for the smartphone. Furthermore, if the user is using a tablet, the reception unit can also provide a reception method optimized for the tablet. For example, if the user is using a desktop, the reception unit can provide a reception method optimized for the desktop. This allows the reception unit to provide the optimal reception method depending on the device of the user.

[0045] The reception unit can customize the reception method by reflecting the user's past feedback. For example, the reception unit proposes an optimal reception method based on the user's past feedback. The reception unit can also analyze the user's past feedback and improve the reception method. For example, the reception unit reflects the user's feedback and provides a customized reception method. This allows the reception unit to provide an optimal reception method based on the past feedback.

[0046] The reception unit can provide multilingual reception according to the user's language setting. For example, the reception unit automatically sets the reception language based on the language setting of the user's device. The reception unit can also provide a language switching function when the user uses multiple languages. For example, when the user selects a specific language, the reception unit provides reception in that language. In this way, the reception unit can provide multilingual reception, making it easier for users to use.

[0047] The analysis unit can evaluate the nutritional value of ingredients and provide analysis results based on nutritional balance. For example, the system can evaluate the nutritional value of ingredients and suggest a nutritionally balanced menu. The analysis unit can also perform analysis with emphasis on specific nutrients and provide the results. For example, the system can suggest a healthy menu based on the nutritional value of ingredients. In this way, the analysis unit can suggest a healthy menu by providing analysis results that take nutritional balance into consideration.

[0048] The analysis unit can provide analysis results based on the shelf life of ingredients. For example, the analysis unit allows the system to evaluate the shelf life of ingredients and propose a menu that prioritizes the use of ingredients with an upcoming expiration date. The analysis unit can also propose an optimal menu based on the shelf life. For example, the analysis unit allows the system to consider the shelf life and propose a menu that reduces wasted ingredients. In this way, the analysis unit can reduce wasted ingredients by providing analysis results that take the shelf life into consideration.

[0049] The analysis unit can provide analysis results based on the allergen information of ingredients. For example, the system evaluates the allergen information of ingredients and suggests a menu that does not contain allergens. The analysis unit can also suggest an optimal menu based on the allergen information. For example, the system considers the allergen information and suggests a safe menu. In this way, the analysis unit can suggest a safe menu by providing analysis results that take allergen information into consideration.

[0050] The analysis unit can provide analysis results based on the origin information of ingredients. For example, the system evaluates the origin information of ingredients and suggests a menu that prioritizes the use of locally produced ingredients. The analysis unit can also suggest dishes from a specific region based on the origin information. For example, the system considers the origin information and suggests a menu that emphasizes the quality of ingredients. In this way, the analysis unit can suggest a menu that prioritizes the use of locally produced ingredients by providing analysis results that consider the origin information.

[0051] The analysis unit can provide analysis results based on the price information of ingredients. For example, the system evaluates the price information of ingredients and proposes a menu with good cost performance. The analysis unit can also propose a menu that fits the budget based on the price information. For example, the system considers the price information and proposes an economical menu. In this way, the analysis unit can propose a menu with good cost performance by providing analysis results that consider the price information.

[0052] The analysis unit can provide analysis results based on the cooking method of the ingredients. For example, the system evaluates the cooking method of the ingredients and suggests a menu that is easy to cook. The analysis unit can also suggest a menu that uses specific cooking equipment based on the cooking method. For example, the analysis unit can suggest a menu that can shorten the cooking time by taking the cooking method into consideration. In this way, the analysis unit can suggest a menu that is easy to cook by providing analysis results that take the cooking method into consideration.

[0053] The suggestion unit can suggest an optimal menu by referring to the user's past meal history. For example, the suggestion unit can suggest a preferred menu based on the user's past meal history. The suggestion unit can also analyze the user's past meal history and suggest a well-balanced menu. For example, the suggestion unit can suggest a new menu by referring to the user's past meal history. This allows the suggestion unit to suggest an optimal menu based on the user's past meal history.

[0054] The suggestion unit can suggest a menu based on the user's health condition. For example, the suggestion unit evaluates the user's health condition and suggests a nutritionally balanced menu. The suggestion unit can also suggest a menu that emphasizes specific nutrients. For example, the suggestion unit takes the user's health condition into consideration and suggests a healthy menu. In this way, the suggestion unit can support the user's health by suggesting a menu that takes the user's health condition into consideration.

[0055] The suggestion unit can suggest a menu based on the user's ingredient preferences. For example, the suggestion unit suggests an optimal menu based on the user's favorite ingredients. The suggestion unit can also suggest a preferred menu based on the user's past selection history. For example, the suggestion unit considers the user's preferences and suggests a new menu. In this way, the suggestion unit can increase user satisfaction by suggesting a menu that takes into account the user's ingredient preferences.

[0056] The suggestion unit can suggest a menu based on the user's geographical location information. For example, the suggestion unit can suggest a menu using local ingredients based on the user's geographical location information. The suggestion unit can also suggest dishes from a specific region by taking the user's geographical location information into consideration. For example, the suggestion unit can suggest a menu using seasonal ingredients based on the user's geographical location information. In this way, the suggestion unit can provide a menu that makes use of local ingredients by suggesting a menu that takes the geographical location information into consideration.

[0057] The suggestion unit can analyze the user's social media activity to suggest related menus. For example, the suggestion unit can analyze the user's social media posts to suggest related menus. The suggestion unit can also suggest related menus by referring to the activities of the user's friends on social media. For example, the suggestion unit can suggest related menus based on the user's social media check-in information. In this way, the suggestion unit can provide a menu that matches the user's interests by suggesting related menus based on social media activity.

[0058] The suggestion unit can customize the suggestion content by reflecting the user's past feedback. For example, the suggestion unit suggests an optimal menu based on the user's past feedback. The suggestion unit can also analyze the user's past feedback and improve the suggestion content. For example, the suggestion unit reflects the user's feedback and suggests a customized menu. In this way, the suggestion unit can increase user satisfaction by customizing the suggestion content based on the user's past feedback.

[0059] The ordering unit can select the optimal ordering method by referring to the user's past order history. For example, the ordering unit can suggest the optimal ordering method based on the user's past order history. The ordering unit can also analyze the user's past order history and suggest an efficient ordering method. For example, the ordering unit can provide a customized ordering method by referring to the user's past order history. This allows the ordering unit to provide the optimal ordering method based on the user's past order history.

[0060] The ordering unit can provide an ordering means based on the user's payment method. For example, if the user uses a credit card, the ordering unit can provide credit card payment as a priority. Also, if the user uses electronic money, the ordering unit can provide electronic money payment as a priority. For example, if the user wants to pay in cash, the ordering unit can provide a cash payment option. In this way, the ordering unit can increase user convenience by providing the optimal ordering means according to the payment method.

[0061] The order unit can provide a delivery method based on the user's delivery address information. For example, the order unit can suggest the optimal delivery method based on the user's delivery address information. The order unit can also suggest an efficient delivery method by referring to the user's past delivery history. For example, the order unit can suggest a fast delivery method based on the user's current location information. This allows the order unit to provide the optimal delivery method according to the delivery address information, enabling fast and efficient delivery.

[0062] The ordering unit can provide an ordering method based on the device information of the user. For example, if the user is using a smartphone, the ordering unit can provide an ordering method optimized for the smartphone. Furthermore, if the user is using a tablet, the ordering unit can also provide an ordering method optimized for the tablet. For example, if the user is using a desktop, the ordering unit can provide an ordering method optimized for the desktop. In this way, the ordering unit can improve user convenience by providing the optimal ordering method according to the device information.

[0063] The ordering unit can customize the ordering method by reflecting the user's past feedback. For example, the ordering unit suggests the optimal ordering method based on the user's past feedback. The ordering unit can also analyze the user's past feedback and improve the ordering method. For example, the ordering unit reflects the user's feedback and provides a customized ordering method. This allows the ordering unit to provide the optimal ordering method based on the past feedback.

[0064] The order unit can provide multilingual orders according to the user's language setting. For example, the order unit automatically sets the language of the order based on the language setting of the user's device. The order unit can also provide a language switching function when the user uses multiple languages. For example, if the user selects a specific language, the order unit provides the order in that language. This allows the order unit to provide multilingual orders, making it easier for the user to use.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The analysis unit can evaluate the storage conditions of ingredients and provide analysis results based on the storage conditions. For example, the analysis unit can evaluate the freshness of ingredients in a refrigerator and suggest a menu that prioritizes the use of fresh ingredients. It can also suggest a menu that uses ingredients that have been poorly stored as soon as possible. Furthermore, the analysis unit can manage the expiration dates of ingredients based on the storage conditions and suggest a menu that reduces waste. This allows the analysis unit to provide analysis results that take into account the storage conditions of ingredients.

[0067] The suggestion unit can evaluate the user's health condition and suggest a menu based on the health condition. For example, if the user is on a diet, the suggestion unit can suggest a low-calorie menu. Also, if the user needs a specific nutrient, the suggestion unit can suggest a menu that contains a lot of that nutrient. Furthermore, if the user has an allergy, the suggestion unit can suggest a menu that does not contain allergens. In this way, the suggestion unit can suggest a menu that takes the user's health condition into consideration.

[0068] The analysis unit can evaluate the nutritional value of ingredients and provide analysis results based on nutritional balance. For example, the analysis unit allows the system to evaluate the nutritional value of ingredients and suggest a nutritionally balanced menu. The analysis unit can also perform analysis with emphasis on specific nutrients and provide results. For example, the analysis unit allows the system to suggest a healthy menu based on the nutritional value of ingredients. In this way, the analysis unit can suggest a healthy menu by providing analysis results that take nutritional balance into consideration.

[0069] The photographing unit can automatically adjust the arrangement of ingredients in the refrigerator to make it easier to photograph. For example, the system can automatically organize ingredients to make them easier to photograph. The photographing unit can also adjust the position of ingredients to make important ingredients easier to see. For example, the system can classify ingredients and organize them by category before photographing them. This allows the photographing unit to automatically adjust the arrangement of ingredients to make it easier to photograph.

[0070] The analysis unit can provide analysis results based on the allergen information of ingredients. For example, the system evaluates the allergen information of ingredients and suggests a menu that does not contain allergens. The analysis unit can also suggest an optimal menu based on the allergen information. For example, the analysis unit can suggest a safe menu by taking the allergen information into consideration. In this way, the analysis unit can suggest a safe menu by providing analysis results that take allergen information into consideration.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The photographing unit takes a photograph of the contents of the refrigerator. For example, the user takes a photograph of the ingredients in the refrigerator. Step 2: The reception unit inputs the photo taken by the photographing unit into the system. For example, the user uploads the photo to the system. Step 3: The analysis unit uses AI to analyze the photo input by the reception unit and identify the ingredients in the refrigerator. For example, the analysis unit uses image recognition technology to identify the ingredients in the photo. Step 4: The suggestion unit uses AI to suggest menus based on the ingredients identified by the analysis unit. For example, the suggestion unit suggests menus that take into consideration nutritional balance and cooking time based on the ingredients identified. Step 5: The ordering unit orders the missing ingredients suggested by the suggestion unit using an e-commerce system. For example, the ordering unit allows the user to order the missing ingredients online.

[0073] (Example 2) A system according to an embodiment of the present invention takes a photo of the contents of a refrigerator and uses the photo to suggest the perfect menu for tonight's dinner. In this system, a user takes a photo of the contents of the refrigerator and inputs the photo into the system. The system then uses AI to analyze the photo and identify the ingredients in the refrigerator. Based on the identified ingredients, the AI ​​suggests the perfect menu for tonight's dinner. Furthermore, the system suggests dishes that can be made by purchasing missing ingredients, and users can order them directly using an e-commerce system. This allows the system to effectively utilize the contents of the refrigerator and easily prepare dinner. For example, if a user wants to make a dish using chicken and vegetables, the AI ​​suggests the perfect recipe and allows them to order missing seasonings and additional ingredients online. This allows the user to easily prepare dinner.

[0074] A proposal system according to an embodiment includes a photographing unit, a reception unit, an analysis unit, a proposal unit, and an ordering unit. The photographing unit takes a photo of the contents of a refrigerator. For example, a user photographs ingredients in the refrigerator. The reception unit inputs the photo taken by the photographing unit into the system. For example, the user uploads the photo taken by the user to the system. The analysis unit uses AI to analyze the photo input by the reception unit and identify ingredients in the refrigerator. For example, the analysis unit uses image recognition technology to identify ingredients appearing in the photo. The proposal unit uses AI to propose a menu based on the ingredients identified by the analysis unit. For example, the proposal unit proposes a menu that takes into consideration nutritional balance and cooking time based on the identified ingredients. The ordering unit orders missing ingredients proposed by the proposal unit using an e-commerce system. For example, the ordering unit allows the user to order missing ingredients online. This allows the proposal system according to an embodiment to effectively utilize the contents of the refrigerator and easily prepare dinner.

[0075] The analysis unit can identify ingredients in the refrigerator. The analysis unit can use, for example, image recognition technology to identify ingredients in the refrigerator. For example, the analysis unit can identify ingredients in a photograph and identify each ingredient. The analysis unit can also refer to a database to obtain detailed information about the identified ingredients. For example, the analysis unit can obtain information such as the name, type, and expiration date of the ingredient. This allows the analysis unit to accurately identify ingredients in the refrigerator.

[0076] The suggestion unit can suggest a menu based on the identified ingredients. For example, the suggestion unit can suggest a menu that takes into consideration nutritional balance and cooking time based on the identified ingredients. For example, the suggestion unit can suggest a dish using chicken and vegetables. The suggestion unit can also suggest an optimal menu by taking into consideration the user's preferences and past eating history. For example, the suggestion unit can suggest a new menu based on dishes that the user has enjoyed eating in the past. This allows the suggestion unit to suggest an optimal menu based on the identified ingredients.

[0077] The suggestion unit can suggest missing ingredients. For example, the suggestion unit suggests missing ingredients based on the identified ingredients. For example, the suggestion unit suggests a dish using chicken and vegetables, and suggests missing condiments or additional ingredients. The suggestion unit can also provide a list of missing ingredients so that the user can order online. For example, the suggestion unit lists missing ingredients, allowing the user to easily order. In this way, the suggestion unit can suggest missing ingredients, allowing the user to grasp the ingredients they need.

[0078] The ordering unit can order missing ingredients using an e-commerce system. The ordering unit, for example, allows a user to order missing ingredients online. For example, the ordering unit orders missing ingredients using an e-commerce system. The ordering unit can also allow a user to check the delivery status of the ordered ingredients. For example, the ordering unit displays the delivery status of the ordered ingredients in real time. This allows the ordering unit to easily order missing ingredients.

[0079] The suggestion unit can suggest a new menu based on ingredients ordered online by the user. For example, the suggestion unit suggests a new menu based on ingredients ordered online by the user. For example, the suggestion unit suggests a new dish using chicken and vegetables ordered by the user. The suggestion unit can also suggest a new menu by taking into consideration the user's preferences and past eating history. For example, the suggestion unit suggests a new menu based on dishes that the user has liked to eat in the past. This allows the suggestion unit to suggest a new menu based on ingredients ordered online.

[0080] The image capturing unit can estimate the user's emotions and adjust the timing of capturing images based on the estimated user emotions. For example, the image capturing unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, if the user is feeling stressed, the image capturing unit allows the system to automatically capture images at the optimal timing. Alternatively, if the user is relaxed, the image capturing unit can allow the user to manually select the timing of capturing images. For example, if the user is in a hurry, the image capturing unit allows the system to quickly capture images. This allows the image capturing unit to provide the optimal timing of capturing images according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] The photographing unit can set photographing conditions taking into account the temperature and humidity inside the refrigerator. For example, when the temperature inside the refrigerator is high, the system automatically adjusts the exposure to take a clear photograph. Also, when the humidity inside the refrigerator is high, the system automatically adjusts the focus to take a clear photograph. For example, the photographing unit sets optimal photographing conditions taking into account both the temperature and humidity inside the refrigerator. This allows the photographing unit to provide optimal photographing conditions according to the environment inside the refrigerator.

[0082] The photographing unit can automatically adjust the arrangement of ingredients in the refrigerator to make it easier to photograph. For example, the photographing unit can have the system automatically organize the ingredients and arrange them in a way that makes it easier to photograph. The photographing unit can also have the system adjust the position of ingredients so that important ingredients are easier to see. For example, the photographing unit can have the system classify ingredients and organize them by category before photographing them. This allows the photographing unit to automatically adjust the arrangement of ingredients, making it easier to photograph.

[0083] The photographing unit can evaluate the freshness of ingredients and photograph them in order of freshness. For example, the photographing unit allows the system to evaluate the freshness of ingredients and photograph them in order of freshness. The photographing unit can also allow the system to prioritize photographing ingredients with low freshness and suggest using them sooner. For example, the photographing unit allows the system to determine the optimal photographing order based on the freshness evaluation results. This allows the photographing unit to prioritize photographing ingredients with high freshness, thereby making effective use of fresh ingredients.

[0084] The photographing unit can estimate the user's emotions and determine the priority of ingredients to photograph based on the estimated user emotions. The photographing unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is stressed, the photographing unit prioritizes photographing ingredients that the system can easily prepare. Also, if the user is relaxed, the photographing unit can prioritize photographing ingredients that take a long time to prepare. For example, if the user is in a hurry, the photographing unit prioritizes photographing ingredients that the system can quickly prepare. In this way, the photographing unit can determine the priority of ingredients to photograph based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] The camera unit can automatically adjust the lighting inside the refrigerator to provide an optimal shooting environment. For example, the camera unit can automatically adjust the lighting inside the refrigerator to optimize brightness. The camera unit can also adjust the color temperature of the lighting so that ingredients are photographed in natural colors. For example, the camera unit can adjust the angle of the lighting to prevent shadows. This allows the camera unit to optimize the lighting inside the refrigerator and take clear photos.

[0086] The photographing unit records the position information of ingredients in the refrigerator and can refer to it the next time the photograph is taken. For example, the photographing unit may have a system record the position information of ingredients and place them in the same position the next time the photograph is taken. The photographing unit may also have a system suggest the optimal placement for the next photograph based on the position information of ingredients. For example, the photographing unit may have a system analyze the position information of ingredients and suggest an efficient placement method. In this way, by recording the position information of ingredients, the photographing unit can suggest an efficient placement the next time the photograph is taken.

[0087] The photographing unit can automatically read and record the barcodes of ingredients in the refrigerator. For example, the photographing unit allows a system to automatically read the barcodes of ingredients and record them in a database. The photographing unit can also allow a system to obtain detailed information about ingredients based on the barcode information. For example, the photographing unit allows a system to manage the expiration dates of ingredients based on the barcode information. This allows the photographing unit to obtain detailed information by reading the barcodes of ingredients.

[0088] The reception unit can estimate the user's emotions and adjust the reception interface based on the estimated user emotions. For example, the reception unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, if the user is feeling stressed, the reception unit provides a simple interface and minimizes input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. For example, if the user is in a hurry, the reception unit can prioritize voice input to enable quick input. This allows the reception unit to provide an optimal reception interface according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0089] The reception unit can select a reception method by referring to the user's past usage history. The reception unit, for example, proposes the optimal reception method based on the user's past usage history. The reception unit can also analyze the user's past usage history and propose the most efficient reception method. For example, the reception unit preferentially proposes reception methods that the user has frequently used in the past. This allows the reception unit to provide the optimal reception method based on the user's past usage history.

[0090] The reception unit can select a reception means depending on the user's input method. For example, if the user desires voice input, the reception unit provides a voice recognition function. Also, if the user desires text input, the reception unit can provide keyboard input. For example, if the user desires image input, the reception unit provides an image recognition function. This allows the reception unit to provide the optimal reception means depending on the user's input method.

[0091] The reception unit can customize the reception contents based on the user's current situation. For example, when the user uses the service at night, the reception unit provides a night mode interface. Furthermore, when the user uses the service in a specific location, the reception unit can also provide the reception contents that are optimal for that location. For example, the reception unit suggests the optimal reception contents based on the user's current situation. This allows the reception unit to provide the optimal reception contents according to the user's situation.

[0092] The reception unit can estimate the user's emotions and prioritize the reception contents based on the estimated user emotions. The reception unit, for example, captures the user's facial expression with a camera and estimates the emotions using an emotion estimation algorithm. For example, if the user is feeling stressed, the reception unit prioritizes displaying important reception contents. The reception unit can also provide detailed reception contents when the user is relaxed. For example, if the user is in a hurry, the reception unit prioritizes displaying reception contents that can be processed quickly. This allows the reception unit to prioritize the reception contents according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0093] The reception unit can provide a reception method taking into consideration device information of the user. For example, if the user is using a smartphone, the reception unit can provide a reception method optimized for the smartphone. Furthermore, if the user is using a tablet, the reception unit can also provide a reception method optimized for the tablet. For example, if the user is using a desktop, the reception unit can provide a reception method optimized for the desktop. This allows the reception unit to provide the optimal reception method depending on the device of the user.

[0094] The reception unit can customize the reception method by reflecting the user's past feedback. For example, the reception unit proposes an optimal reception method based on the user's past feedback. The reception unit can also analyze the user's past feedback and improve the reception method. For example, the reception unit reflects the user's feedback and provides a customized reception method. This allows the reception unit to provide an optimal reception method based on the past feedback.

[0095] The reception unit can provide multilingual reception according to the user's language setting. For example, the reception unit automatically sets the reception language based on the language setting of the user's device. The reception unit can also provide a language switching function when the user uses multiple languages. For example, when the user selects a specific language, the reception unit provides reception in that language. In this way, the reception unit can provide multilingual reception, making it easier for users to use.

[0096] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotions using an emotion estimation algorithm. For example, if the user is feeling stressed, the analysis unit causes the system to increase the accuracy of the analysis and provide results quickly. Furthermore, if the user is relaxed, the analysis unit can perform a detailed analysis and provide results with high accuracy. For example, if the user is in a hurry, the analysis unit causes the system to perform an analysis quickly and provide results. This allows the analysis unit to adjust the accuracy of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0097] The analysis unit can evaluate the nutritional value of ingredients and provide analysis results based on nutritional balance. For example, the system can evaluate the nutritional value of ingredients and suggest a nutritionally balanced menu. The analysis unit can also perform analysis with emphasis on specific nutrients and provide the results. For example, the system can suggest a healthy menu based on the nutritional value of ingredients. In this way, the analysis unit can suggest a healthy menu by providing analysis results that take nutritional balance into consideration.

[0098] The analysis unit can provide analysis results based on the shelf life of ingredients. For example, the analysis unit allows the system to evaluate the shelf life of ingredients and propose a menu that prioritizes the use of ingredients with an upcoming expiration date. The analysis unit can also propose an optimal menu based on the shelf life. For example, the analysis unit allows the system to consider the shelf life and propose a menu that reduces wasted ingredients. In this way, the analysis unit can reduce wasted ingredients by providing analysis results that take the shelf life into consideration.

[0099] The analysis unit can provide analysis results based on the allergen information of ingredients. For example, the system evaluates the allergen information of ingredients and suggests a menu that does not contain allergens. The analysis unit can also suggest an optimal menu based on the allergen information. For example, the system considers the allergen information and suggests a safe menu. In this way, the analysis unit can suggest a safe menu by providing analysis results that take allergen information into consideration.

[0100] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is feeling stressed, the analysis unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit provides a display method that focuses on the main points. This allows the analysis unit to adjust the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0101] The analysis unit can provide analysis results based on the origin information of ingredients. For example, the system evaluates the origin information of ingredients and suggests a menu that prioritizes the use of locally produced ingredients. The analysis unit can also suggest dishes from a specific region based on the origin information. For example, the system considers the origin information and suggests a menu that emphasizes the quality of ingredients. In this way, the analysis unit can suggest a menu that prioritizes the use of locally produced ingredients by providing analysis results that consider the origin information.

[0102] The analysis unit can provide analysis results based on the price information of ingredients. For example, the system evaluates the price information of ingredients and proposes a menu with good cost performance. The analysis unit can also propose a menu that fits the budget based on the price information. For example, the system considers the price information and proposes an economical menu. In this way, the analysis unit can propose a menu with good cost performance by providing analysis results that consider the price information.

[0103] The analysis unit can provide analysis results based on the cooking method of the ingredients. For example, the system evaluates the cooking method of the ingredients and suggests a menu that is easy to cook. The analysis unit can also suggest a menu that uses specific cooking equipment based on the cooking method. For example, the analysis unit can suggest a menu that can shorten the cooking time by taking the cooking method into consideration. In this way, the analysis unit can suggest a menu that is easy to cook by providing analysis results that take the cooking method into consideration.

[0104] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, the suggestion unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is feeling stressed, the suggestion unit makes a simple and easy-to-understand suggestion. Also, if the user is relaxed, the suggestion unit can make a suggestion that includes detailed information. For example, if the user is in a hurry, the suggestion unit makes a suggestion that is quickly understandable. This allows the suggestion unit to adjust the way suggestions are expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0105] The suggestion unit can suggest an optimal menu by referring to the user's past meal history. For example, the suggestion unit can suggest a preferred menu based on the user's past meal history. The suggestion unit can also analyze the user's past meal history and suggest a well-balanced menu. For example, the suggestion unit can suggest a new menu by referring to the user's past meal history. This allows the suggestion unit to suggest an optimal menu based on the user's past meal history.

[0106] The suggestion unit can suggest a menu based on the user's health condition. For example, the suggestion unit evaluates the user's health condition and suggests a nutritionally balanced menu. The suggestion unit can also suggest a menu that emphasizes specific nutrients. For example, the suggestion unit takes the user's health condition into consideration and suggests a healthy menu. In this way, the suggestion unit can support the user's health by suggesting a menu that takes the user's health condition into consideration.

[0107] The suggestion unit can suggest a menu based on the user's ingredient preferences. For example, the suggestion unit suggests an optimal menu based on the user's favorite ingredients. The suggestion unit can also suggest a preferred menu based on the user's past selection history. For example, the suggestion unit considers the user's preferences and suggests a new menu. In this way, the suggestion unit can increase user satisfaction by suggesting a menu that takes into account the user's ingredient preferences.

[0108] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated user emotions. The suggestion unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is stressed, the suggestion unit can prioritize suggesting menus that are easy to prepare. Also, if the user is relaxed, the suggestion unit can prioritize suggesting menus that take a long time to prepare. For example, if the user is in a hurry, the suggestion unit can prioritize suggesting menus that can be prepared quickly. This allows the suggestion unit to prioritize suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0109] The suggestion unit can suggest a menu based on the user's geographical location information. For example, the suggestion unit can suggest a menu using local ingredients based on the user's geographical location information. The suggestion unit can also suggest dishes from a specific region by taking the user's geographical location information into consideration. For example, the suggestion unit can suggest a menu using seasonal ingredients based on the user's geographical location information. In this way, the suggestion unit can provide a menu that makes use of local ingredients by suggesting a menu that takes the geographical location information into consideration.

[0110] The suggestion unit can analyze the user's social media activity to suggest related menus. For example, the suggestion unit can analyze the user's social media posts to suggest related menus. The suggestion unit can also suggest related menus by referring to the activities of the user's friends on social media. For example, the suggestion unit can suggest related menus based on the user's social media check-in information. In this way, the suggestion unit can provide a menu that matches the user's interests by suggesting related menus based on social media activity.

[0111] The suggestion unit can customize the suggestion content by reflecting the user's past feedback. For example, the suggestion unit suggests an optimal menu based on the user's past feedback. The suggestion unit can also analyze the user's past feedback and improve the suggestion content. For example, the suggestion unit reflects the user's feedback and suggests a customized menu. In this way, the suggestion unit can increase user satisfaction by customizing the suggestion content based on the user's past feedback.

[0112] The ordering unit can estimate the user's emotions and adjust the ordering process based on the estimated user emotions. For example, the ordering unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, if the user is feeling stressed, the ordering unit provides a simple and quick ordering process. The ordering unit can also provide detailed ordering options if the user is relaxed. For example, if the user is in a hurry, the ordering unit provides a process that allows the user to complete the order quickly. This allows the ordering unit to adjust the ordering process according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0113] The ordering unit can select the optimal ordering method by referring to the user's past order history. For example, the ordering unit can suggest the optimal ordering method based on the user's past order history. The ordering unit can also analyze the user's past order history and suggest an efficient ordering method. For example, the ordering unit can provide a customized ordering method by referring to the user's past order history. This allows the ordering unit to provide the optimal ordering method based on the user's past order history.

[0114] The ordering unit can provide an ordering means based on the user's payment method. For example, if the user uses a credit card, the ordering unit can provide credit card payment as a priority. Also, if the user uses electronic money, the ordering unit can provide electronic money payment as a priority. For example, if the user wants to pay in cash, the ordering unit can provide a cash payment option. In this way, the ordering unit can increase user convenience by providing the optimal ordering means according to the payment method.

[0115] The order unit can provide a delivery method based on the user's delivery address information. For example, the order unit can suggest the optimal delivery method based on the user's delivery address information. The order unit can also suggest an efficient delivery method by referring to the user's past delivery history. For example, the order unit can suggest a fast delivery method based on the user's current location information. This allows the order unit to provide the optimal delivery method according to the delivery address information, enabling fast and efficient delivery.

[0116] The ordering unit can estimate the user's emotions and prioritize order contents based on the estimated user emotions. The ordering unit, for example, captures the user's facial expression with a camera and estimates the emotions using an emotion estimation algorithm. For example, if the user is feeling stressed, the ordering unit prioritizes displaying important order contents. The ordering unit can also provide detailed order contents if the user is relaxed. For example, if the user is in a hurry, the ordering unit prioritizes displaying order contents that can be processed quickly. This allows the ordering unit to prioritize order contents according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0117] The ordering unit can provide an ordering method based on the device information of the user. For example, if the user is using a smartphone, the ordering unit can provide an ordering method optimized for the smartphone. Furthermore, if the user is using a tablet, the ordering unit can also provide an ordering method optimized for the tablet. For example, if the user is using a desktop, the ordering unit can provide an ordering method optimized for the desktop. In this way, the ordering unit can improve user convenience by providing the optimal ordering method according to the device information.

[0118] The ordering unit can customize the ordering method by reflecting the user's past feedback. For example, the ordering unit suggests the optimal ordering method based on the user's past feedback. The ordering unit can also analyze the user's past feedback and improve the ordering method. For example, the ordering unit reflects the user's feedback and provides a customized ordering method. This allows the ordering unit to provide the optimal ordering method based on the past feedback.

[0119] The order unit can provide multilingual orders according to the user's language setting. For example, the order unit automatically sets the language of the order based on the language setting of the user's device. The order unit can also provide a language switching function when the user uses multiple languages. For example, if the user selects a specific language, the order unit provides the order in that language. This allows the order unit to provide multilingual orders, making it easier for the user to use. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned photographing unit, reception unit, analysis unit, suggestion unit, and ordering unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the photographing unit is realized by the camera 42 of the smart device 14 and takes a photo of the contents of the refrigerator. The reception unit is realized by the control unit 46A of the smart device 14 and inputs the taken photo into the system. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the photo using AI to identify ingredients in the refrigerator. The suggestion unit is realized by the identification processing unit 290 of the data processing device 12 and suggests a menu based on the identified ingredients. The ordering unit is realized by the control unit 46A of the smart device 14 and orders the suggested missing ingredients using the e-commerce system. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned photographing unit, reception unit, analysis unit, suggestion unit, and ordering unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the photographing unit is realized by the camera 42 of the smart glasses 214 and takes a photo of the contents of the refrigerator. The reception unit is realized by the control unit 46A of the smart glasses 214 and inputs the taken photo into the system. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the photo using AI to identify ingredients in the refrigerator. The suggestion unit is realized by the identification processing unit 290 of the data processing device 12 and suggests a menu based on the identified ingredients. The ordering unit is realized by the control unit 46A of the smart glasses 214 and orders the suggested missing ingredients using the e-commerce system. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned photographing unit, reception unit, analysis unit, suggestion unit, and ordering unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the photographing unit is realized by the camera 42 of the headset terminal 314 and takes a photo of the contents of the refrigerator. The reception unit is realized by the control unit 46A of the headset terminal 314 and inputs the taken photo into the system. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the photo using AI to identify ingredients in the refrigerator. The suggestion unit is realized by the identification processing unit 290 of the data processing device 12 and suggests a menu based on the identified ingredients. The ordering unit is realized by the control unit 46A of the headset terminal 314 and orders the suggested missing ingredients using the e-commerce system. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned photographing unit, reception unit, analysis unit, suggestion unit, and ordering unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the photographing unit is realized by the camera 42 of the robot 414 and takes a photo of the contents of the refrigerator. The reception unit is realized by the control unit 46A of the robot 414 and inputs the taken photo into the system. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the photo using AI to identify ingredients in the refrigerator. The suggestion unit is realized by the identification processing unit 290 of the data processing device 12 and suggests a menu based on the identified ingredients. The ordering unit is realized by the control unit 46A of the robot 414 and orders the suggested missing ingredients using the e-commerce system.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The suggestion unit can estimate the user's emotions and adjust the difficulty of the suggested menu based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can prioritize suggesting menus that are easy to prepare. Also, if the user is relaxed, the suggestion unit can suggest delicious menus that take time to prepare. Furthermore, if the user is in a hurry, the suggestion unit can suggest menus that can be prepared in a short time. In this way, the suggestion unit can suggest the optimal menu according to the user's emotions.

[0122] The analysis unit can evaluate the storage conditions of ingredients and provide analysis results based on the storage conditions. For example, the analysis unit can evaluate the freshness of ingredients in a refrigerator and suggest a menu that prioritizes the use of fresh ingredients. It can also suggest a menu that uses ingredients that have been poorly stored as soon as possible. Furthermore, the analysis unit can manage the expiration dates of ingredients based on the storage conditions and suggest a menu that reduces waste. This allows the analysis unit to provide analysis results that take into account the storage conditions of ingredients.

[0123] The suggestion unit can estimate the user's emotions and adjust the variations of the suggested menu based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can suggest a simple and classic menu. If the user is relaxed, the suggestion unit can also suggest new recipes or challenging menus. Furthermore, if the user is in a hurry, the suggestion unit can suggest a menu that can be cooked in a short time. In this way, the suggestion unit can suggest the optimal menu according to the user's emotions.

[0124] The suggestion unit can evaluate the user's health condition and suggest a menu based on the health condition. For example, if the user is on a diet, the suggestion unit can suggest a low-calorie menu. Also, if the user needs a specific nutrient, the suggestion unit can suggest a menu that contains a lot of that nutrient. Furthermore, if the user has an allergy, the suggestion unit can suggest a menu that does not contain allergens. In this way, the suggestion unit can suggest a menu that takes the user's health condition into consideration.

[0125] The order unit can estimate the user's emotions and simplify the ordering procedure based on the estimated user's emotions. For example, if the user is feeling stressed, the order unit can allow the user to complete the order with minimal input. If the user is feeling relaxed, the order unit can provide detailed ordering options. Furthermore, if the user is in a hurry, the order unit can provide a procedure that allows the user to complete the order quickly. In this way, the order unit can provide the optimal ordering procedure according to the user's emotions.

[0126] The analysis unit can evaluate the nutritional value of ingredients and provide analysis results based on nutritional balance. For example, the analysis unit allows the system to evaluate the nutritional value of ingredients and suggest a nutritionally balanced menu. The analysis unit can also perform analysis with emphasis on specific nutrients and provide results. For example, the analysis unit allows the system to suggest a healthy menu based on the nutritional value of ingredients. In this way, the analysis unit can suggest a healthy menu by providing analysis results that take nutritional balance into consideration.

[0127] The suggestion unit can estimate the user's emotions and adjust the presentation of the suggested menu based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can provide a simple and visually easy-to-understand presentation. If the user is relaxed, the suggestion unit can provide a presentation that includes detailed information. If the user is in a hurry, the suggestion unit can provide a presentation that focuses on the main points. In this way, the suggestion unit can provide an optimal presentation according to the user's emotions.

[0128] The photographing unit can automatically adjust the arrangement of ingredients in the refrigerator to make it easier to photograph. For example, the system can automatically organize ingredients to make them easier to photograph. The photographing unit can also adjust the position of ingredients to make important ingredients easier to see. For example, the system can classify ingredients and organize them by category before photographing them. This allows the photographing unit to automatically adjust the arrangement of ingredients to make it easier to photograph.

[0129] The suggestion unit can estimate the user's emotions and adjust the portions of the menu to be suggested based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can suggest a menu that is small but satisfying. Also, if the user is relaxed, the suggestion unit can suggest a menu with a larger portion. Furthermore, if the user is in a hurry, the suggestion unit can suggest a menu with a small portion that can be cooked in a short time. In this way, the suggestion unit can suggest the optimal portion of the menu according to the user's emotions.

[0130] The analysis unit can provide analysis results based on the allergen information of ingredients. For example, the system evaluates the allergen information of ingredients and suggests a menu that does not contain allergens. The analysis unit can also suggest an optimal menu based on the allergen information. For example, the analysis unit can suggest a safe menu by taking the allergen information into consideration. In this way, the analysis unit can suggest a safe menu by providing analysis results that take allergen information into consideration.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The photographing unit takes a photograph of the contents of the refrigerator. For example, the user takes a photograph of the ingredients in the refrigerator. Step 2: The reception unit inputs the photo taken by the photographing unit into the system. For example, the user uploads the photo to the system. Step 3: The analysis unit uses AI to analyze the photo input by the reception unit and identify the ingredients in the refrigerator. For example, the analysis unit uses image recognition technology to identify the ingredients in the photo. Step 4: The suggestion unit uses AI to suggest menus based on the ingredients identified by the analysis unit. For example, the suggestion unit suggests menus that take into consideration nutritional balance and cooking time based on the ingredients identified. Step 5: The ordering unit orders the missing ingredients suggested by the suggestion unit using an e-commerce system. For example, the ordering unit allows the user to order the missing ingredients online.

[0133] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0139] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0155] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0161] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0164] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0165] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0172] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0173] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0175] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0176] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0177] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0178] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0179] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0180] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0181] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0182] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0183] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0184] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0187] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0188] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0189] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0190] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0191] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0192] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0193] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0194] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0195] 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.

[0196] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0197] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0198] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0199] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0200] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0201] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0202] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0203] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0204] [Explanation of symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A photography department that takes photos of the contents of the refrigerator, a reception unit that inputs the photograph taken by the photographing unit into the system; an analysis unit that analyzes the photo input by the reception unit and identifies ingredients in the refrigerator; a suggestion unit that suggests a menu based on the ingredients identified by the analysis unit; an ordering unit that orders the missing ingredients suggested by the suggestion unit using an electronic commerce system; A system characterized by:

2. The analysis unit Identify the ingredients in the refrigerator 2. The system of claim 1.

3. The proposal unit Suggest menu items based on identified ingredients 2. The system of claim 1.

4. The proposal unit Suggesting missing ingredients 2. The system of claim 1.

5. The ordering unit Use an e-commerce system to order missing ingredients 2. The system of claim 1.

6. The proposal unit Suggest new menu items based on ingredients ordered online by the user 2. The system of claim 1.

7. The imaging unit is Estimate the user's emotions and adjust the timing of taking photos based on the estimated user emotions.

2. The system of claim 1.

8. The imaging unit is Set the shooting conditions taking into account the temperature and humidity inside the refrigerator 2. The system of claim 1.

Images