System

Abstract

An object of a system according to an embodiment is to efficiently perform management of purchased foodstuffs and reminding of an expiration date.SOLUTION: A system according to an embodiment includes a receipt analysis unit, a menu suggestion unit, a food ingredient management unit, a reminder unit, and a priority suggestion unit. The receipt analysis unit analyzes the content of the receipt. The menu proposal unit proposes a menu based on the information analyzed by the receipt analysis unit. The ingredient management unit manages ingredients used based on the menu proposed by the menu proposal unit and ingredients purchased in the past and not used. The reminder unit is configured to remind a user of a food item having an approaching expiration date based on the food item information managed by the food item management unit. The priority suggestion unit suggests a menu in which the foodstuffs reminded by the reminder unit are preferentially used.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 does not adequately manage purchased ingredients or provide reminders about expiration dates, which can lead to food waste.

[0005] The system according to the embodiment aims to efficiently manage purchased ingredients and provide reminders of expiration dates. [Means for solving the problem]

[0006] The system according to the embodiment includes a receipt analysis unit, a menu proposal unit, an ingredient management unit, a reminder unit, and a priority proposal unit. The receipt analysis unit analyzes the contents of a receipt. The menu proposal unit proposes a menu based on the information analyzed by the receipt analysis unit. The ingredient management unit manages ingredients used based on the menu proposed by the menu proposal unit and ingredients purchased in the past but not used. The reminder unit reminds users of ingredients whose expiration date is approaching based on the ingredient information managed by the ingredient management unit. The priority proposal unit proposes a menu that prioritizes the use of ingredients reminded by the reminder unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently manage purchased ingredients and provide reminders of expiration dates. [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 allows an AI to create a menu for the day simply by taking a photo of the receipt from food purchases with a smartphone. This system allows users to take a photo of the receipt with their smartphone, and the AI ​​analyzes the receipt and suggests a menu for the day. Furthermore, by inputting a menu, the system manages the ingredients used and previously purchased but unused ingredients, as well as the contents of the refrigerator. It also reminds users of ingredients that are close to their expiration date and suggests menus to prioritize. Furthermore, by inputting family medical history (e.g., obesity, diabetes), the system suggests menus that take health into consideration. For example, a user takes a photo of a receipt from food purchases with their smartphone. It is important to take a photo that clearly captures the contents of the receipt. Next, the AI ​​analyzes the contents of the input receipt, extracts information about the ingredients listed on the receipt, and suggests a menu for the day. Furthermore, by inputting a menu, the system manages the ingredients used and previously purchased but unused ingredients. It also manages the contents of the refrigerator, reminds users of ingredients that are close to their expiration date, and suggests menus to prioritize. Furthermore, by entering the family's medical history (obesity, diabetes, etc.), the system will suggest menus that take health conditions into consideration. This allows the system to reduce food waste and easily suggest menus that take health conditions into consideration. This allows the system to reduce food waste and easily suggest menus that take health conditions into consideration.

[0029] The ingredient management system according to the embodiment includes a receipt analysis unit, a menu proposal unit, an ingredient management unit, a reminder unit, and a priority proposal unit. The receipt analysis unit analyzes the contents of a receipt. The contents of a receipt include, but are not limited to, the purchased items, price, and purchase date and time. The receipt analysis unit converts the text information on the receipt into digital data using, for example, OCR technology. The receipt analysis unit can also extract ingredient information from the receipt using natural language processing technology. For example, the receipt analysis unit can extract ingredient information using an AI model that takes an image of the receipt as input and outputs ingredient information. The menu proposal unit proposes a menu based on the information analyzed by the receipt analysis unit. The menu proposal unit proposes a menu taking into consideration, for example, nutritional balance, calories, and ingredient combinations. For example, the menu proposal unit can propose a menu using an AI model that takes the extracted ingredient information as input and outputs a menu. The ingredient management unit manages ingredients used in the menu proposed by the menu proposal unit as well as ingredients purchased in the past but not used. The ingredient management unit, for example, manages inventory and records usage history. For example, the ingredient management unit can record ingredients used based on menu information entered by the user and reflect this in the next menu proposal. The reminder unit reminds users about ingredients whose expiration date is approaching based on the ingredient information managed by the ingredient management unit. The reminder unit, for example, sets the notification method and timing of the reminder. For example, the reminder unit can notify the user of ingredients whose expiration date is approaching and encourage them to use them. The priority suggestion unit proposes menus that prioritize ingredients that have been reminded by the reminder unit. The priority suggestion unit, for example, sets criteria for prioritizing ingredients that are close to their expiration date. For example, the priority suggestion unit can propose menus using an AI model that inputs ingredients that have been reminded and outputs menus that will be prioritized. This allows the ingredient management system according to the embodiment to reduce ingredient waste and propose menus that take health conditions into consideration.

[0030] The receipt analysis unit can analyze the contents of a receipt and extract ingredient information. The receipt analysis unit can convert the text information on a receipt into digital data using, for example, OCR technology. For example, the receipt analysis unit can scan an image of a receipt and extract the text information. The receipt analysis unit can also extract ingredient information listed on a receipt using natural language processing technology. For example, the receipt analysis unit can extract ingredient information using an AI model that inputs an image of a receipt and outputs ingredient information. This analysis of the contents of a receipt and extraction of ingredient information improves the accuracy of menu suggestions. Some or all of the above-described processing by the receipt analysis unit can be performed using, for example, AI, or without AI.

[0031] The menu proposal unit can propose a menu based on the extracted ingredient information. The menu proposal unit proposes a menu taking into consideration, for example, nutritional balance, calories, and ingredient combinations. For example, the menu proposal unit can propose a menu using an AI model that takes the extracted ingredient information as input and outputs a menu. The menu proposal unit can also propose a menu taking into consideration the user's preferences and allergy information. For example, the menu proposal unit takes the user's preferences and allergy information as input and proposes a menu based on that. In this way, by proposing a menu based on the extracted ingredient information, ingredients can be used without waste. Some or all of the above-mentioned processing in the menu proposal unit may be performed, for example, using AI, or may be performed without using AI.

[0032] The ingredient management unit can manage ingredients used based on the menu entered by the user and ingredients purchased in the past but not used. The ingredient management unit performs, for example, inventory management and recording of usage history. For example, the ingredient management unit can record ingredients used based on the menu information entered by the user and reflect this in the next menu proposal. The ingredient management unit can also manage ingredients purchased in the past but not used and use this information in the next menu proposal. For example, the ingredient management unit can record information on ingredients purchased in the past but not used and propose a menu based on this information. In this way, ingredient waste can be reduced by managing ingredients based on the menu entered by the user. Some or all of the above-mentioned processing in the ingredient management unit may be performed, for example, using AI or without AI.

[0033] The reminding unit can remind the user of ingredients whose expiration date is approaching. The reminding unit, for example, sets the notification method and timing of the reminder. For example, the reminding unit can notify the user of ingredients whose expiration date is approaching and encourage them to use them. The reminding unit can also set how many days before the expiration date the reminder will be sent. For example, the reminding unit can start reminding one week before the expiration date and send notifications every day. In this way, by being reminded of ingredients whose expiration date is approaching, it is possible to reduce food waste. Some or all of the above-mentioned processing in the reminding unit may be performed, for example, using AI, or may be performed without using AI.

[0034] The priority suggestion unit can suggest a menu that prioritizes the use of the reminded ingredients. The priority suggestion unit, for example, sets a criterion for prioritizing ingredients with an approaching expiration date. For example, the priority suggestion unit can suggest a menu using an AI model that inputs the reminded ingredients and outputs a menu that prioritizes the use of the reminded ingredients. The priority suggestion unit can also suggest a menu taking into consideration the frequency of use and usage history of the reminded ingredients. For example, the priority suggestion unit inputs the frequency of use of the reminded ingredients and suggests a menu based on that. In this way, by suggesting a menu that prioritizes the use of the reminded ingredients, it is possible to reduce ingredient waste. Some or all of the above-mentioned processing in the priority suggestion unit may be performed, for example, using AI, or may be performed without using AI.

[0035] The menu suggestion unit can suggest a menu that takes into account the medical history of the family members. The menu suggestion unit, for example, suggests a menu that takes into account the medical history of the family members. For example, the menu suggestion unit inputs the medical history of the family members and suggests a menu based on that. The medical history of the family members includes, but is not limited to, allergy information and medical history. For example, if a family member has a history of diabetes, the menu suggestion unit can suggest a menu that is low in carbohydrates. Furthermore, if a family member has a history of obesity, the menu suggestion unit can also suggest a menu that is low in calories. In this way, by suggesting a menu that takes into account the medical history of the family members, it is possible to provide meals that take into account their health status. Some or all of the above-mentioned processing in the menu suggestion unit may be performed, for example, using AI or without using AI.

[0036] The receipt analysis unit can automatically correct light reflections and shadows when photographing a receipt. The receipt analysis unit corrects light reflections and shadows, for example, using image processing technology. For example, the receipt analysis unit detects light reflections when photographing a receipt and automatically corrects them. The receipt analysis unit can also detect shadows and automatically adjust brightness. The receipt analysis unit can also perform image processing to make the overall brightness uniform. For example, the receipt analysis unit corrects light reflections and shadows using filtering technology that makes the image brightness uniform. This improves analysis accuracy by correcting light reflections and shadows when photographing a receipt. Some or all of the above-described processing in the receipt analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0037] The receipt analysis unit can also recognize handwritten notes and additional information when analyzing the contents of a receipt. The receipt analysis unit can recognize handwritten notes and additional information using, for example, OCR technology. For example, the receipt analysis unit can recognize handwritten notes added to a receipt and reflect them in the analysis. The receipt analysis unit can also recognize handwritten discount information and reflect them in the analysis. The receipt analysis unit can also recognize handwritten notes added by the purchaser and reflect them in the analysis. For example, the receipt analysis unit can recognize handwritten notes and additional information using an AI model that inputs handwritten characters and outputs text data. This improves the accuracy of the analysis by recognizing handwritten notes and additional information. Some or all of the above-described processing in the receipt analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0038] The receipt analysis unit can support multiple languages ​​when analyzing the contents of a receipt. The receipt analysis unit supports multiple languages ​​using, for example, natural language processing technology. For example, if the receipt is written in English, the receipt analysis unit recognizes and analyzes English. Also, if the receipt is written in Japanese, the receipt analysis unit can recognize and analyze Japanese. Furthermore, if the receipt is written in Chinese, the receipt analysis unit can recognize and analyze Chinese. For example, the receipt analysis unit can input multiple languages ​​and analyze the contents of the receipt using AI models corresponding to each language. This support for multiple languages ​​improves the versatility of the analysis. Some or all of the above-mentioned processing in the receipt analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0039] When analyzing the contents of a receipt, the receipt analysis unit can also recognize the logo and brand information of the store where the purchase was made. The receipt analysis unit can recognize the logo and brand information of the store where the purchase was made using, for example, image recognition technology. For example, the receipt analysis unit can recognize the store logo printed on the receipt and reflect this in the analysis. The receipt analysis unit can also recognize the brand information printed on the receipt and reflect this in the analysis. The receipt analysis unit can also recognize the store name printed on the receipt and reflect this in the analysis. For example, the receipt analysis unit can input the store logo and brand information and analyze it using an AI model to analyze the contents of the receipt. This improves the accuracy of the analysis by recognizing the logo and brand information of the store where the purchase was made. Some or all of the above-mentioned processing by the receipt analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0040] When analyzing the contents of a receipt, the receipt analysis unit can also acquire the purchase date and time and store location information. The receipt analysis unit acquires the purchase date and time and store location information, for example, by analyzing receipt information or using GPS data. For example, the receipt analysis unit recognizes the purchase date and time written on the receipt and reflects it in the analysis. The receipt analysis unit can also recognize the store location information written on the receipt and reflect it in the analysis. Furthermore, the receipt analysis unit can combine the purchase date and time with the location information and reflect this in the analysis. For example, the receipt analysis unit can input the purchase date and time and location information and analyze it using an AI model to analyze the contents of the receipt. In this way, acquiring the purchase date and time and store location information improves the accuracy of the analysis. Some or all of the above-mentioned processing by the receipt analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0041] When analyzing the contents of a receipt, the receipt analyzer can detect abnormal purchasing patterns by comparing the contents with past purchase histories. The receipt analyzer can detect abnormal purchasing patterns, for example, by comparing the contents with past purchase histories or using an outlier detection algorithm. For example, the receipt analyzer can detect abnormally expensive purchases by comparing the contents with past purchase histories. The receipt analyzer can also detect abnormally large purchases by comparing the contents with past purchase histories. Furthermore, the receipt analyzer can detect purchases with an abnormal frequency by comparing the contents with past purchase histories. For example, the receipt analyzer can detect abnormal purchasing patterns by using an AI model that inputs past purchase histories and analyzes them. This allows for the early detection of abnormal purchasing patterns. Some or all of the above-described processing by the receipt analyzer can be performed using, for example, AI, or without AI.

[0042] When proposing a menu, the menu suggestion unit can customize the proposed content by taking into account the user's past meal history. The menu suggestion unit, for example, proposes a menu by taking into account the user's past meal history. For example, the menu suggestion unit inputs the user's past meal history and proposes a menu based on it. The user's past meal history includes, for example, favorite dishes and avoided ingredients, but is not limited to such examples. For example, the menu suggestion unit can propose a menu based on the user's favorite dishes in the past. The menu suggestion unit can also propose a menu based on ingredients the user has avoided in the past. Furthermore, the user's past meal history can be analyzed to propose a balanced menu. In this way, by taking the user's past meal history into consideration, a more appropriate menu can be proposed. Some or all of the above-described processing in the menu suggestion unit may be performed, for example, using AI or without using AI.

[0043] When proposing a menu, the menu proposal unit can propose a menu that is appropriate for the season and weather. The menu proposal unit, for example, uses weather data to propose a menu that is appropriate for the season and weather. For example, the menu proposal unit can propose cold dishes and refreshing dishes in the summer. Also, it can propose hot dishes and stews in the winter. Furthermore, the menu proposal unit can propose dishes that can be enjoyed at home on rainy days. For example, the menu proposal unit can propose a menu using an AI model that inputs seasonal and weather data and proposes a menu based on that data. In this way, by proposing a menu that is appropriate for the season and weather, it is possible to propose a more appropriate menu. Some or all of the above-mentioned processing in the menu proposal unit may be performed, for example, using AI, or may be performed without using AI.

[0044] When proposing a menu, the menu proposal unit can adjust the proposal content by taking into account the user's allergy information. The menu proposal unit, for example, uses a database of allergens to consider the user's allergy information. For example, the menu proposal unit can propose a menu that avoids ingredients to which the user is allergic. The menu proposal unit can also propose a menu that uses alternative ingredients based on the user's allergy information. Furthermore, the menu proposal unit can propose a menu that uses safe ingredients by taking into account the user's allergy information. For example, the menu proposal unit can input allergy information and propose a menu using an AI model that proposes a menu based on that information. In this way, a safe menu can be proposed by taking into account the user's allergy information. Some or all of the above-mentioned processing in the menu proposal unit may be performed, for example, using AI, or may be performed without using AI.

[0045] When proposing a menu, the menu suggestion unit can learn the user's dietary preferences and tastes and optimize the proposed menu. The menu suggestion unit learns the user's dietary preferences and tastes, for example, by analyzing past selection history and collecting preference data. For example, the menu suggestion unit learns the types of dishes the user likes and reflects them in the proposed menu. The menu suggestion unit can also learn ingredients the user avoids and reflect this in the proposed menu. Furthermore, the menu suggestion unit can analyze the user's dietary preferences and propose optimal menus. For example, the menu suggestion unit can propose menus using an AI model that inputs the user's dietary preferences and tastes and proposes menus based on them. In this way, by learning the user's dietary preferences and tastes, it is possible to propose more appropriate menus. Some or all of the above-mentioned processing in the menu suggestion unit may be performed, for example, using AI, or may be performed without using AI.

[0046] When proposing a menu, the menu proposal unit can adjust the proposed content taking into account the nutritional balance of the user's diet. The menu proposal unit can consider the nutritional balance of the user's diet using, for example, nutrient standards and a balance evaluation method. For example, the menu proposal unit can consider the user's nutritional balance and propose a balanced menu. The menu proposal unit can also analyze the user's nutritional intake status and propose a menu that supplements deficient nutrients. Furthermore, the menu proposal unit can consider the user's health status and propose a menu with an appropriate nutritional balance. For example, the menu proposal unit can input nutritional balance and propose a menu using an AI model that proposes a menu based on that input. This makes it possible to propose a healthy menu by considering the user's nutritional balance. Some or all of the above-mentioned processing in the menu proposal unit may be performed, for example, using AI or without AI.

[0047] When proposing a menu, the menu proposal unit can adjust the proposed content by taking into account the user's meal budget. The menu proposal unit considers the user's meal budget, for example, by using a budget input method and cost-performance evaluation criteria. For example, the menu proposal unit proposes a cost-effective menu that fits within the user's budget. The menu proposal unit can also consider the user's budget and propose a cost-effective menu. Furthermore, the menu proposal unit can also propose ingredient options according to the user's budget. For example, the menu proposal unit can propose a menu using an AI model that inputs a budget and proposes a menu based on that budget. In this way, a cost-effective menu can be proposed by taking the user's budget into consideration. Some or all of the above-described processing in the menu proposal unit may be performed, for example, using AI, or may be performed without using AI.

[0048] The ingredient management unit can make suggestions for optimizing the placement of ingredients in the refrigerator during ingredient management. The ingredient management unit optimizes the placement of ingredients in the refrigerator using, for example, a placement algorithm or a storage condition evaluation criterion. For example, the ingredient management unit optimizes the placement of ingredients in the refrigerator and suggests a placement that makes them easy to access. The ingredient management unit can also optimize the placement of ingredients in the refrigerator and suggest a placement that maintains good storage conditions. Furthermore, the ingredient management unit can optimize the placement of ingredients in the refrigerator and suggest a placement that makes effective use of space. For example, the ingredient management unit can input the placement of ingredients in the refrigerator and suggest a placement using an AI model that optimizes it. In this way, optimizing the placement of ingredients in the refrigerator can improve ease of access and storage conditions. Some or all of the above-described processing in the ingredient management unit may be performed, for example, using AI, or may be performed without using AI.

[0049] The ingredient management unit can provide advice on ingredient storage methods during ingredient management. The ingredient management unit provides advice on ingredient storage methods using criteria such as storage temperature, storage period, and storage container. For example, the ingredient management unit provides advice on ingredient storage methods and suggests methods to maintain freshness. The ingredient management unit can also provide advice on ingredient storage methods and suggest methods for long-term storage. Furthermore, the ingredient management unit can provide advice on ingredient storage methods and suggest methods to maintain nutritional value. For example, the ingredient management unit can provide advice on storage methods using an AI model that inputs a storage method and provides advice based on that input. In this way, by providing advice on ingredient storage methods, freshness and nutritional value can be maintained. Some or all of the above-described processing in the ingredient management unit may be performed, for example, using AI, or may be performed without using AI.

[0050] During ingredient management, the ingredient management unit can analyze the frequency of ingredient use and suggest the optimal timing for purchasing. The ingredient management unit analyzes the frequency of ingredient use using, for example, a usage history recording method and frequency evaluation criteria. For example, the ingredient management unit analyzes the frequency of ingredient use and suggests the optimal timing for purchasing. The ingredient management unit can also analyze the frequency of ingredient use and suggest the timing for purchasing that will minimize waste. Furthermore, the ingredient management unit can analyze the frequency of ingredient use and suggest the efficient timing for purchasing. For example, the ingredient management unit can suggest the timing for purchasing using an AI model that inputs the frequency of use and suggests the timing for purchasing based on that. In this way, by analyzing the frequency of ingredient use, it is possible to suggest the timing for purchasing that will minimize waste. Some or all of the above-described processing in the ingredient management unit may be performed using, for example, AI, or may be performed without using AI.

[0051] The ingredient management unit can customize the ingredient management method by taking into account the user's family composition and meal amounts when managing ingredients. The ingredient management unit considers the user's family composition and meal amounts, for example, using the family composition input method and meal amount evaluation criteria. For example, the ingredient management unit considers the user's family composition and meal amounts to suggest appropriate ingredient amounts. The ingredient management unit can also consider the user's meal amounts to suggest waste-free ingredient management. Furthermore, the ingredient management unit can combine the user's family composition and meal amounts to suggest an optimal ingredient management method. For example, the ingredient management unit can input the family composition and meal amounts and suggest a management method using an AI model that suggests a management method based on the input. This enables waste-free ingredient management by taking the user's family composition and meal amounts into account. Some or all of the above-mentioned processing in the ingredient management unit may be performed, for example, using AI, or may be performed without using AI.

[0052] During ingredient management, the ingredient management unit can analyze the user's ingredient purchase history and propose an optimal management method. The ingredient management unit, for example, analyzes the user's ingredient purchase history using a history data storage method and an analysis algorithm. For example, the ingredient management unit analyzes the user's ingredient purchase history and proposes an optimal storage method. The ingredient management unit can also analyze the user's purchase history and propose a waste-free management method. Furthermore, the ingredient management unit can analyze the user's purchase history and propose an efficient management method. For example, the ingredient management unit can propose a management method using an AI model that inputs the purchase history and proposes a management method based on that history. In this way, a waste-free management method can be proposed by analyzing the user's purchase history. Some or all of the above-described processing in the ingredient management unit may be performed, for example, using AI, or may be performed without using AI.

[0053] During ingredient management, the ingredient management unit can learn the user's ingredient consumption patterns and optimize the management method. The ingredient management unit learns the user's ingredient consumption patterns, for example, using a consumption history recording method and pattern evaluation criteria. For example, the ingredient management unit learns the user's ingredient consumption patterns and suggests an optimal storage method. The ingredient management unit can also learn the user's consumption patterns and suggest a waste-free management method. Furthermore, the ingredient management unit can learn the user's consumption patterns and suggest an efficient management method. For example, the ingredient management unit can suggest a management method using an AI model that inputs the consumption patterns and suggests a management method based on them. In this way, by learning the user's consumption patterns, it is possible to suggest a waste-free management method. Some or all of the above-described processing in the ingredient management unit may be performed, for example, using AI, or may be performed without using AI.

[0054] The reminding unit can propose optimal reminder timing in consideration of the user's schedule when providing a reminder. The reminding unit considers the user's schedule, for example, using a method for acquiring schedule data and schedule evaluation criteria. For example, the reminding unit considers the user's schedule and proposes appropriate reminder timing. The reminding unit can also analyze the user's schedule and propose efficient reminder timing. Furthermore, the reminding unit can propose efficient reminder timing based on the user's schedule. For example, the reminding unit can input a schedule and propose reminder timing using an AI model that proposes reminder timing based on the schedule. This makes it possible to propose efficient reminder timing by considering the user's schedule. Some or all of the above-described processing in the reminding unit may be performed, for example, using AI, or may be performed without using AI.

[0055] The reminding unit can optimize the reminding method by analyzing the user's past reminder history when reminding. The reminding unit, for example, analyzes the user's past reminder history using a history data storage method or an analysis algorithm. For example, the reminding unit analyzes the user's past reminder history and proposes an optimal reminding method. The reminding unit can also propose an efficient reminding method based on the user's past reminder history. Furthermore, the reminding unit can also propose an efficient reminding method by referring to the user's past reminder history. For example, the reminding unit can propose a reminding method using an AI model that inputs the reminder history and proposes a reminding method based on the input. In this way, an efficient reminding method can be proposed by analyzing the user's past reminder history. Some or all of the above-mentioned processing in the reminding unit may be performed using, for example, AI, or may be performed without using AI.

[0056] The reminding unit can customize a reminder message according to the user's preferences when reminding. The reminding unit provides a reminder message according to the user's preferences, for example, using the message content and customization criteria. For example, the reminding unit provides a reminder message according to the user's preferences to draw attention. The reminding unit can also provide a reminder message according to the user's preferences to reduce the user's burden. Furthermore, the reminding unit can provide a reminder message according to the user's preferences to enable efficient response. For example, the reminding unit can provide a reminder message using an AI model that inputs preferences and customizes the reminder message based on the preferences. This allows for more appropriate reminders by providing a reminder message according to the user's preferences. Some or all of the above-described processing in the reminding unit may be performed, for example, using AI or without AI.

[0057] When providing a reminder, the reminding unit can select the optimal reminder method by taking into account the user's device information. The reminding unit can consider the user's device information, for example, using criteria for selecting the device type and the notification method. For example, if the user is using a smartphone, the reminding unit can provide a reminder method that matches the screen size. Furthermore, if the user is using a tablet, the reminding unit can provide a reminder method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the reminding unit can provide a simple and highly visible reminder method. For example, the reminding unit can provide a reminder method using an AI model that inputs device information and selects a reminder method based on that information. This makes it possible to provide a more appropriate reminder method by taking into account the user's device information. Some or all of the above-described processing in the reminding unit may be performed, for example, using AI or without AI.

[0058] The reminding unit can analyze the user's daily rhythm at the time of reminding and optimize the timing of the reminder. The reminding unit analyzes the user's daily rhythm, for example, using a data collection method for the daily rhythm and an evaluation standard for the rhythm. For example, the reminding unit analyzes the user's daily rhythm and suggests the optimal reminder timing. The reminding unit can also suggest an efficient reminder timing based on the user's daily rhythm. Furthermore, the reminding unit can also suggest an efficient reminder timing by taking the user's daily rhythm into consideration. For example, the reminding unit can suggest a reminder timing using an AI model that inputs the user's daily rhythm and suggests a reminder timing based on the input. In this way, efficient reminder timing can be suggested by analyzing the user's daily rhythm. Some or all of the above-mentioned processing in the reminding unit may be performed, for example, using AI, or may be performed without using AI.

[0059] The reminding unit can customize the reminder content by referring to the user's past reminder history when reminding. The reminding unit can refer to the user's past reminder history using, for example, a method for saving history data or criteria for selecting content based on the past history. For example, the reminding unit can refer to the user's past reminder history and provide optimal reminder content. The reminding unit can also provide efficient reminder content based on the user's past reminder history. Furthermore, the reminding unit can provide efficient reminder content by referring to the user's past reminder history. For example, the reminding unit can provide reminder content using an AI model that inputs the reminder history and customizes the reminder content based on the input. This makes it possible to provide efficient reminder content by referring to the user's past reminder history. Some or all of the above-described processing in the reminding unit may be performed, for example, using AI or without AI.

[0060] When making a priority suggestion, the priority suggestion unit can customize the suggestion content by taking into account the user's past meal history. The priority suggestion unit can consider the user's past meal history, for example, using a method for storing or analyzing history data. For example, the priority suggestion unit makes optimal suggestions based on the user's past meal history. The priority suggestion unit can also analyze the user's past meal history to make efficient suggestions. Furthermore, the priority suggestion unit can make efficient suggestions by referring to the user's past meal history. For example, the priority suggestion unit can provide the suggestion content using an AI model that inputs the user's past meal history and customizes the suggestion content based on the input. This enables more appropriate suggestions by taking the user's past meal history into consideration. Some or all of the above-mentioned processing in the priority suggestion unit may be performed, for example, using AI, or may be performed without using AI.

[0061] The priority suggestion unit can learn the user's dietary preferences and tastes and optimize the content of the suggestion when making a priority suggestion. The priority suggestion unit learns the user's dietary preferences and tastes, for example, by analyzing past selection history or by using a method for collecting preference data. For example, the priority suggestion unit learns the user's dietary preferences and makes optimal suggestions. The priority suggestion unit can also learn the user's dietary preferences and make efficient suggestions. Furthermore, the priority suggestion unit can analyze the user's dietary preferences and make efficient suggestions. For example, the priority suggestion unit can provide suggestions using an AI model that inputs the user's dietary preferences and optimizes the content of the suggestion based on the input. This enables more appropriate suggestions to be made by learning the user's dietary preferences and tastes. Some or all of the above-described processing in the priority suggestion unit may be performed, for example, using AI, or may be performed without using AI.

[0062] When making a priority suggestion, the priority suggestion unit can adjust the suggestion content taking into account the nutritional balance of the user's diet. The priority suggestion unit can consider the nutritional balance of the user's diet using, for example, nutrient standards and a balance evaluation method. For example, the priority suggestion unit can make a balanced suggestion taking into account the user's nutritional balance. The priority suggestion unit can also analyze the user's nutritional intake status and make suggestions to supplement deficient nutrients. Furthermore, the priority suggestion unit can make suggestions with appropriate nutritional balance taking into account the user's health status. For example, the priority suggestion unit can provide the suggestion content using an AI model that inputs nutritional balance and adjusts the suggestion content based on that. This enables healthy suggestions to be made by taking into account the user's nutritional balance. Some or all of the above-mentioned processing in the priority suggestion unit may be performed, for example, using AI, or may be performed without using AI.

[0063] When making a priority suggestion, the priority suggestion unit can adjust the suggestion content taking into account the user's meal budget. The priority suggestion unit considers the user's meal budget using, for example, a budget input method or cost-performance evaluation criteria. For example, the priority suggestion unit makes cost-effective suggestions to fit within the user's budget. The priority suggestion unit can also consider the user's budget and make suggestions with high cost performance. Furthermore, the priority suggestion unit can suggest ingredient options according to the user's budget. For example, the priority suggestion unit can provide suggestions using an AI model that inputs a budget and adjusts the suggestion content based on the budget. This makes it possible to make suggestions with high cost performance by considering the user's budget. Some or all of the above-described processing in the priority suggestion unit may be performed using, for example, AI, or may be performed without using AI.

[0064] The priority suggestion unit can customize the suggestion content by taking into account the amount of food the user eats when making a priority suggestion. The priority suggestion unit can consider the amount of food the user eats, for example, by using an input method for the amount of food and an evaluation criterion for the amount. For example, the priority suggestion unit can consider the amount of food the user eats and suggest appropriate portions. The priority suggestion unit can also make suggestions that do not waste food based on the amount of food the user eats. Furthermore, the priority suggestion unit can make efficient suggestions by referring to the amount of food the user eats. For example, the priority suggestion unit can provide suggestions using an AI model that inputs the amount of food and customizes the suggestion content based on that. This makes it possible to make suggestions that do not waste food by taking into account the amount of food the user eats. Some or all of the above-mentioned processing in the priority suggestion unit may be performed, for example, using AI, or may be performed without using AI.

[0065] The priority suggestion unit can adjust the proposal content taking into account the user's mealtimes when making a priority suggestion. The priority suggestion unit can consider the user's mealtimes, for example, by using a time-of-day input method or suggestion criteria according to the time of day. For example, the priority suggestion unit makes appropriate suggestions taking into account the user's mealtimes. The priority suggestion unit can also make efficient suggestions based on the user's mealtimes. Furthermore, the priority suggestion unit can make efficient suggestions by referring to the user's mealtimes. For example, the priority suggestion unit can provide proposal content using an AI model that inputs mealtimes and adjusts the proposal content based on the input. This enables more appropriate suggestions by taking the user's mealtimes into consideration. Some or all of the above-described processing in the priority suggestion unit may be performed using, for example, AI, or may be performed without using AI.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The food ingredient management system can also include a nutritional analysis unit that analyzes the nutritional value of ingredients. The nutritional analysis unit calculates the nutritional value of each ingredient based on the ingredient information obtained from the receipt analysis unit and provides the result to the user. For example, the nutritional analysis unit analyzes nutrients such as vitamins, minerals, proteins, lipids, and carbohydrates, and suggests balanced meals to the user. The nutritional analysis unit can also suggest menus that emphasize specific nutrients based on the user's health condition and goals (e.g., dieting or muscle building). Furthermore, the nutritional analysis unit can record the history of nutrients consumed by the user to support long-term health management. This allows the food ingredient management system to provide more comprehensive support for the user's health.

[0068] The receipt analysis unit can also analyze the origin information of purchased ingredients. For example, it can extract the origin information written on the receipt and provide it to the user. This allows the user to understand the origin of the purchased ingredients and prioritize using locally produced ingredients. The receipt analysis unit can also estimate the freshness of ingredients based on the origin information and notify the user. For example, ingredients transported from a distant location may be less fresh, so the receipt analysis unit can remind the user to use them sooner. Furthermore, the receipt analysis unit can evaluate the quality of ingredients based on the origin information and provide it to the user. This allows the user to select high-quality ingredients.

[0069] The menu suggestion unit can also collect the user's satisfaction with the meal as feedback and reflect it in the next suggestion. For example, the user actually prepares the suggested menu and inputs their satisfaction level after the meal. The menu suggestion unit learns the user's tastes and preferences based on this feedback and optimizes the next suggestion. The menu suggestion unit can also adjust the frequency of specific ingredients and dishes based on the user's satisfaction. Furthermore, the menu suggestion unit can also suggest new recipes and ingredients based on the user's satisfaction. This allows the user to always enjoy new dishes.

[0070] The food management unit can also be equipped with sensors that monitor the storage conditions of food ingredients in real time. For example, sensors that measure temperature and humidity can be installed inside the refrigerator to monitor the storage conditions of food ingredients. The food management unit can evaluate the storage conditions of food ingredients based on data from the sensors and suggest appropriate storage methods. The food management unit can also notify the user if the storage conditions deteriorate, reminding them to use the food ingredients sooner. Furthermore, the food management unit can predict food deterioration based on storage condition data and suggest the optimal time to use the food ingredients. This can further reduce food waste.

[0071] The reminder unit can also learn the user's lifestyle rhythm and suggest optimal reminder timing. For example, it can learn the time the user eats breakfast or cooks dinner and send reminders based on those times. The reminder unit can adjust the frequency and timing of reminders based on the user's lifestyle rhythm. The reminder unit can also automatically update the reminder timing if the user's lifestyle rhythm changes. Furthermore, the reminder unit can also suggest menus suitable for specific time periods based on the user's lifestyle rhythm. This allows the user to use ingredients without straining themselves.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The receipt analysis unit analyzes the contents of the receipt. The contents of the receipt include the purchased items, price, purchase date and time, etc. The receipt analysis unit uses OCR technology to convert the text information on the receipt into digital data, and then uses natural language processing technology to extract ingredient information. For example, it uses an AI model that takes an image of the receipt as input and outputs ingredient information. Step 2: The menu suggestion unit proposes a menu based on the information analyzed by the receipt analysis unit. The menu suggestion unit considers nutritional balance, calories, and ingredient combinations when proposing a menu. For example, it uses an AI model that inputs the extracted ingredient information and outputs a menu. Step 3: The ingredient management unit manages ingredients used based on the menu proposed by the menu proposal unit, as well as ingredients purchased in the past but not used. The ingredient management unit manages inventory and records usage history, records ingredients used based on the menu information entered by the user, and reflects this in the next menu proposal. Step 4: The reminder unit reminds the user about ingredients whose expiration date is approaching based on the ingredient information managed by the ingredient management unit. The reminder unit sets the notification method and reminder timing, and notifies the user of ingredients whose expiration date is approaching to encourage them to use them. Step 5: The priority suggestion unit proposes a menu that prioritizes the use of ingredients that have been reminded by the reminder unit. The priority suggestion unit sets criteria to prioritize ingredients with an approaching expiration date, and uses an AI model that inputs the reminded ingredients and outputs a menu that prioritizes the use of ingredients.

[0074] (Example 2) A system according to an embodiment of the present invention allows an AI to create a menu for the day simply by taking a photo of the receipt from food purchases with a smartphone. This system allows users to take a photo of the receipt with their smartphone, and the AI ​​analyzes the receipt and suggests a menu for the day. Furthermore, by inputting a menu, the system manages the ingredients used and previously purchased but unused ingredients, as well as the contents of the refrigerator. It also reminds users of ingredients that are close to their expiration date and suggests menus to prioritize. Furthermore, by inputting family medical history (e.g., obesity, diabetes), the system suggests menus that take health into consideration. For example, a user takes a photo of a receipt from food purchases with their smartphone. It is important to take a photo that clearly captures the contents of the receipt. Next, the AI ​​analyzes the contents of the input receipt, extracts information about the ingredients listed on the receipt, and suggests a menu for the day. Furthermore, by inputting a menu, the system manages the ingredients used and previously purchased but unused ingredients. It also manages the contents of the refrigerator, reminds users of ingredients that are close to their expiration date, and suggests menus to prioritize. Furthermore, by entering the family's medical history (obesity, diabetes, etc.), the system will suggest menus that take health conditions into consideration. This allows the system to reduce food waste and easily suggest menus that take health conditions into consideration. This allows the system to reduce food waste and easily suggest menus that take health conditions into consideration.

[0075] The ingredient management system according to the embodiment includes a receipt analysis unit, a menu proposal unit, an ingredient management unit, a reminder unit, and a priority proposal unit. The receipt analysis unit analyzes the contents of a receipt. The contents of a receipt include, but are not limited to, the purchased items, price, and purchase date and time. The receipt analysis unit converts the text information on the receipt into digital data using, for example, OCR technology. The receipt analysis unit can also extract ingredient information from the receipt using natural language processing technology. For example, the receipt analysis unit can extract ingredient information using an AI model that takes an image of the receipt as input and outputs ingredient information. The menu proposal unit proposes a menu based on the information analyzed by the receipt analysis unit. The menu proposal unit proposes a menu taking into consideration, for example, nutritional balance, calories, and ingredient combinations. For example, the menu proposal unit can propose a menu using an AI model that takes the extracted ingredient information as input and outputs a menu. The ingredient management unit manages ingredients used in the menu proposed by the menu proposal unit as well as ingredients purchased in the past but not used. The ingredient management unit, for example, manages inventory and records usage history. For example, the ingredient management unit can record ingredients used based on menu information entered by the user and reflect this in the next menu proposal. The reminder unit reminds users about ingredients whose expiration date is approaching based on the ingredient information managed by the ingredient management unit. The reminder unit, for example, sets the notification method and timing of the reminder. For example, the reminder unit can notify the user of ingredients whose expiration date is approaching and encourage them to use them. The priority suggestion unit proposes menus that prioritize ingredients that have been reminded by the reminder unit. The priority suggestion unit, for example, sets criteria for prioritizing ingredients that are close to their expiration date. For example, the priority suggestion unit can propose menus using an AI model that inputs ingredients that have been reminded and outputs menus that will be prioritized. This allows the ingredient management system according to the embodiment to reduce ingredient waste and propose menus that take health conditions into consideration.

[0076] The receipt analysis unit can analyze the contents of a receipt and extract ingredient information. The receipt analysis unit can convert the text information on a receipt into digital data using, for example, OCR technology. For example, the receipt analysis unit can scan an image of a receipt and extract the text information. The receipt analysis unit can also extract ingredient information listed on a receipt using natural language processing technology. For example, the receipt analysis unit can extract ingredient information using an AI model that inputs an image of a receipt and outputs ingredient information. This analysis of the contents of a receipt and extraction of ingredient information improves the accuracy of menu suggestions. Some or all of the above-described processing by the receipt analysis unit can be performed using, for example, AI, or without AI.

[0077] The menu proposal unit can propose a menu based on the extracted ingredient information. The menu proposal unit proposes a menu taking into consideration, for example, nutritional balance, calories, and ingredient combinations. For example, the menu proposal unit can propose a menu using an AI model that takes the extracted ingredient information as input and outputs a menu. The menu proposal unit can also propose a menu taking into consideration the user's preferences and allergy information. For example, the menu proposal unit takes the user's preferences and allergy information as input and proposes a menu based on that. In this way, by proposing a menu based on the extracted ingredient information, ingredients can be used without waste. Some or all of the above-mentioned processing in the menu proposal unit may be performed, for example, using AI, or may be performed without using AI.

[0078] The ingredient management unit can manage ingredients used based on the menu entered by the user and ingredients purchased in the past but not used. The ingredient management unit performs, for example, inventory management and recording of usage history. For example, the ingredient management unit can record ingredients used based on the menu information entered by the user and reflect this in the next menu proposal. The ingredient management unit can also manage ingredients purchased in the past but not used and use this information in the next menu proposal. For example, the ingredient management unit can record information on ingredients purchased in the past but not used and propose a menu based on this information. In this way, ingredient waste can be reduced by managing ingredients based on the menu entered by the user. Some or all of the above-mentioned processing in the ingredient management unit may be performed, for example, using AI or without AI.

[0079] The reminding unit can remind the user of ingredients whose expiration date is approaching. The reminding unit, for example, sets the notification method and timing of the reminder. For example, the reminding unit can notify the user of ingredients whose expiration date is approaching and encourage them to use them. The reminding unit can also set how many days before the expiration date the reminder will be sent. For example, the reminding unit can start reminding one week before the expiration date and send notifications every day. In this way, by being reminded of ingredients whose expiration date is approaching, it is possible to reduce food waste. Some or all of the above-mentioned processing in the reminding unit may be performed, for example, using AI, or may be performed without using AI.

[0080] The priority suggestion unit can suggest a menu that prioritizes the use of the reminded ingredients. The priority suggestion unit, for example, sets a criterion for prioritizing ingredients with an approaching expiration date. For example, the priority suggestion unit can suggest a menu using an AI model that inputs the reminded ingredients and outputs a menu that prioritizes the use of the reminded ingredients. The priority suggestion unit can also suggest a menu taking into consideration the frequency of use and usage history of the reminded ingredients. For example, the priority suggestion unit inputs the frequency of use of the reminded ingredients and suggests a menu based on that. In this way, by suggesting a menu that prioritizes the use of the reminded ingredients, it is possible to reduce ingredient waste. Some or all of the above-mentioned processing in the priority suggestion unit may be performed, for example, using AI, or may be performed without using AI.

[0081] The menu suggestion unit can suggest a menu that takes into account the medical history of the family members. The menu suggestion unit, for example, suggests a menu that takes into account the medical history of the family members. For example, the menu suggestion unit inputs the medical history of the family members and suggests a menu based on that. The medical history of the family members includes, but is not limited to, allergy information and medical history. For example, if a family member has a history of diabetes, the menu suggestion unit can suggest a menu that is low in carbohydrates. Furthermore, if a family member has a history of obesity, the menu suggestion unit can also suggest a menu that is low in calories. In this way, by suggesting a menu that takes into account the medical history of the family members, it is possible to provide meals that take into account their health status. Some or all of the above-mentioned processing in the menu suggestion unit may be performed, for example, using AI or without using AI.

[0082] The receipt analysis unit can estimate the user's emotions and adjust the accuracy of the receipt analysis based on the estimated user emotions. The receipt analysis unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the receipt analysis unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The receipt analysis unit can also estimate the user's emotions using voice analysis technology. For example, the receipt analysis unit can analyze the tone and speed of the user's voice to estimate the emotions. The receipt analysis unit can also adjust the accuracy of the receipt analysis based on the estimated user emotions. For example, if the user is stressed, the receipt analysis unit can increase the analysis accuracy and provide results more quickly. On the other hand, if the user is relaxed, the receipt analysis unit can perform a more detailed analysis and provide more information. This allows for adjusting the accuracy of the receipt analysis based on the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the receipt analysis unit may be performed using AI, for example, or may be performed without using AI.

[0083] The receipt analysis unit can automatically correct light reflections and shadows when photographing a receipt. The receipt analysis unit corrects light reflections and shadows, for example, using image processing technology. For example, the receipt analysis unit detects light reflections when photographing a receipt and automatically corrects them. The receipt analysis unit can also detect shadows and automatically adjust brightness. The receipt analysis unit can also perform image processing to make the overall brightness uniform. For example, the receipt analysis unit corrects light reflections and shadows using filtering technology that makes the image brightness uniform. This improves analysis accuracy by correcting light reflections and shadows when photographing a receipt. Some or all of the above-described processing in the receipt analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0084] The receipt analysis unit can also recognize handwritten notes and additional information when analyzing the contents of a receipt. The receipt analysis unit can recognize handwritten notes and additional information using, for example, OCR technology. For example, the receipt analysis unit can recognize handwritten notes added to a receipt and reflect them in the analysis. The receipt analysis unit can also recognize handwritten discount information and reflect them in the analysis. The receipt analysis unit can also recognize handwritten notes added by the purchaser and reflect them in the analysis. For example, the receipt analysis unit can recognize handwritten notes and additional information using an AI model that inputs handwritten characters and outputs text data. This improves the accuracy of the analysis by recognizing handwritten notes and additional information. Some or all of the above-described processing in the receipt analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0085] The receipt analysis unit can support multiple languages ​​when analyzing the contents of a receipt. The receipt analysis unit supports multiple languages ​​using, for example, natural language processing technology. For example, if the receipt is written in English, the receipt analysis unit recognizes and analyzes English. Also, if the receipt is written in Japanese, the receipt analysis unit can recognize and analyze Japanese. Furthermore, if the receipt is written in Chinese, the receipt analysis unit can recognize and analyze Chinese. For example, the receipt analysis unit can input multiple languages ​​and analyze the contents of the receipt using AI models corresponding to each language. This support for multiple languages ​​improves the versatility of the analysis. Some or all of the above-mentioned processing in the receipt analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0086] The receipt analysis unit can estimate the user's emotions and adjust the display method of the receipt analysis results based on the estimated user emotions. The receipt analysis unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the receipt analysis unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The receipt analysis unit can also estimate the user's emotions using voice analysis technology. For example, the receipt analysis unit can analyze the tone and speed of the user's voice to estimate the emotions. The receipt analysis unit can also adjust the display method of the receipt analysis results based on the estimated user emotions. For example, if the user is stressed, the receipt analysis unit can provide a simple, highly visible display method. On the other hand, if the user is relaxed, the receipt analysis unit can provide a display method that includes detailed information. This allows for more appropriate information to be provided by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the receipt analysis unit may be performed using AI, for example, or may be performed without using AI.

[0087] When analyzing the contents of a receipt, the receipt analysis unit can also recognize the logo and brand information of the store where the purchase was made. The receipt analysis unit can recognize the logo and brand information of the store where the purchase was made using, for example, image recognition technology. For example, the receipt analysis unit can recognize the store logo printed on the receipt and reflect this in the analysis. The receipt analysis unit can also recognize the brand information printed on the receipt and reflect this in the analysis. The receipt analysis unit can also recognize the store name printed on the receipt and reflect this in the analysis. For example, the receipt analysis unit can input the store logo and brand information and analyze it using an AI model to analyze the contents of the receipt. This improves the accuracy of the analysis by recognizing the logo and brand information of the store where the purchase was made. Some or all of the above-mentioned processing by the receipt analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0088] When analyzing the contents of a receipt, the receipt analysis unit can also acquire the purchase date and time and store location information. The receipt analysis unit acquires the purchase date and time and store location information, for example, by analyzing receipt information or using GPS data. For example, the receipt analysis unit recognizes the purchase date and time written on the receipt and reflects it in the analysis. The receipt analysis unit can also recognize the store location information written on the receipt and reflect it in the analysis. Furthermore, the receipt analysis unit can combine the purchase date and time with the location information and reflect this in the analysis. For example, the receipt analysis unit can input the purchase date and time and location information and analyze it using an AI model to analyze the contents of the receipt. In this way, acquiring the purchase date and time and store location information improves the accuracy of the analysis. Some or all of the above-mentioned processing by the receipt analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0089] When analyzing the contents of a receipt, the receipt analyzer can detect abnormal purchasing patterns by comparing the contents with past purchase histories. The receipt analyzer can detect abnormal purchasing patterns, for example, by comparing the contents with past purchase histories or using an outlier detection algorithm. For example, the receipt analyzer can detect abnormally expensive purchases by comparing the contents with past purchase histories. The receipt analyzer can also detect abnormally large purchases by comparing the contents with past purchase histories. Furthermore, the receipt analyzer can detect purchases with an abnormal frequency by comparing the contents with past purchase histories. For example, the receipt analyzer can detect abnormal purchasing patterns by using an AI model that inputs past purchase histories and analyzes them. This allows for the early detection of abnormal purchasing patterns. Some or all of the above-described processing by the receipt analyzer can be performed using, for example, AI, or without AI.

[0090] The menu suggestion unit can estimate the user's emotions and adjust the menu suggestions based on the estimated user emotions. The menu suggestion unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the menu suggestion unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The menu suggestion unit can also estimate the user's emotions using voice analysis technology. For example, the menu suggestion unit analyzes the tone and speed of the user's voice to estimate the emotions. Furthermore, the menu suggestion unit adjusts the menu suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the menu suggestion unit can suggest a simple and hassle-free menu. On the other hand, if the user is relaxed, the menu suggestion unit can suggest a menu that can be enjoyed over a long period of time. This allows the menu suggestions to be adjusted according to the user's emotions, resulting in more appropriate menu suggestions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the menu suggestion unit may be performed using AI, or may be performed without using AI.

[0091] When proposing a menu, the menu suggestion unit can customize the proposed content by taking into account the user's past meal history. The menu suggestion unit, for example, proposes a menu by taking into account the user's past meal history. For example, the menu suggestion unit inputs the user's past meal history and proposes a menu based on it. The user's past meal history includes, for example, favorite dishes and avoided ingredients, but is not limited to such examples. For example, the menu suggestion unit can propose a menu based on the user's favorite dishes in the past. The menu suggestion unit can also propose a menu based on ingredients the user has avoided in the past. Furthermore, the user's past meal history can be analyzed to propose a balanced menu. In this way, by taking the user's past meal history into consideration, a more appropriate menu can be proposed. Some or all of the above-described processing in the menu suggestion unit may be performed, for example, using AI or without using AI.

[0092] When proposing a menu, the menu proposal unit can propose a menu that is appropriate for the season and weather. The menu proposal unit, for example, uses weather data to propose a menu that is appropriate for the season and weather. For example, the menu proposal unit can propose cold dishes and refreshing dishes in the summer. Also, it can propose hot dishes and stews in the winter. Furthermore, the menu proposal unit can propose dishes that can be enjoyed at home on rainy days. For example, the menu proposal unit can propose a menu using an AI model that inputs seasonal and weather data and proposes a menu based on that data. In this way, by proposing a menu that is appropriate for the season and weather, it is possible to propose a more appropriate menu. Some or all of the above-mentioned processing in the menu proposal unit may be performed, for example, using AI, or may be performed without using AI.

[0093] When proposing a menu, the menu proposal unit can adjust the proposal content by taking into account the user's allergy information. The menu proposal unit, for example, uses a database of allergens to consider the user's allergy information. For example, the menu proposal unit can propose a menu that avoids ingredients to which the user is allergic. The menu proposal unit can also propose a menu that uses alternative ingredients based on the user's allergy information. Furthermore, the menu proposal unit can propose a menu that uses safe ingredients by taking into account the user's allergy information. For example, the menu proposal unit can input allergy information and propose a menu using an AI model that proposes a menu based on that information. In this way, a safe menu can be proposed by taking into account the user's allergy information. Some or all of the above-mentioned processing in the menu proposal unit may be performed, for example, using AI, or may be performed without using AI.

[0094] The menu suggestion unit can estimate the user's emotions and adjust the frequency of menu suggestions based on the estimated user emotions. The menu suggestion unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the menu suggestion unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The menu suggestion unit can also estimate the user's emotions using voice analysis technology. For example, the menu suggestion unit analyzes the tone and speed of the user's voice to estimate the emotions. Furthermore, the menu suggestion unit adjusts the frequency of menu suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the menu suggestion unit reduces the frequency of suggestions to reduce the burden. On the other hand, if the user is relaxed, the menu suggestion unit increases the frequency of suggestions to expand the options. This enables more appropriate menu suggestions by adjusting the suggestion frequency according to the user's emotions. Emotion estimation is realized 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. Some or all of the above-described processing in the menu suggestion unit may be performed using AI, for example, or may be performed without using AI.

[0095] When proposing a menu, the menu suggestion unit can learn the user's dietary preferences and tastes and optimize the proposed menu. The menu suggestion unit learns the user's dietary preferences and tastes, for example, by analyzing past selection history and collecting preference data. For example, the menu suggestion unit learns the types of dishes the user likes and reflects them in the proposed menu. The menu suggestion unit can also learn ingredients the user avoids and reflect this in the proposed menu. Furthermore, the menu suggestion unit can analyze the user's dietary preferences and propose optimal menus. For example, the menu suggestion unit can propose menus using an AI model that inputs the user's dietary preferences and tastes and proposes menus based on them. In this way, by learning the user's dietary preferences and tastes, it is possible to propose more appropriate menus. Some or all of the above-mentioned processing in the menu suggestion unit may be performed, for example, using AI, or may be performed without using AI.

[0096] When proposing a menu, the menu proposal unit can adjust the proposed content taking into account the nutritional balance of the user's diet. The menu proposal unit can consider the nutritional balance of the user's diet using, for example, nutrient standards and a balance evaluation method. For example, the menu proposal unit can consider the user's nutritional balance and propose a balanced menu. The menu proposal unit can also analyze the user's nutritional intake status and propose a menu that supplements deficient nutrients. Furthermore, the menu proposal unit can consider the user's health status and propose a menu with an appropriate nutritional balance. For example, the menu proposal unit can input nutritional balance and propose a menu using an AI model that proposes a menu based on that input. This makes it possible to propose a healthy menu by considering the user's nutritional balance. Some or all of the above-mentioned processing in the menu proposal unit may be performed, for example, using AI or without AI.

[0097] When proposing a menu, the menu proposal unit can adjust the proposed content by taking into account the user's meal budget. The menu proposal unit considers the user's meal budget, for example, by using a budget input method and cost-performance evaluation criteria. For example, the menu proposal unit proposes a cost-effective menu that fits within the user's budget. The menu proposal unit can also consider the user's budget and propose a cost-effective menu. Furthermore, the menu proposal unit can also propose ingredient options according to the user's budget. For example, the menu proposal unit can propose a menu using an AI model that inputs a budget and proposes a menu based on that budget. In this way, a cost-effective menu can be proposed by taking the user's budget into consideration. Some or all of the above-described processing in the menu proposal unit may be performed, for example, using AI, or may be performed without using AI.

[0098] The ingredient management unit can estimate the user's emotions and adjust the ingredient management method based on the estimated user emotions. The ingredient management unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the ingredient management unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The ingredient management unit can also estimate the user's emotions using voice analysis technology. For example, the ingredient management unit analyzes the tone and speed of the user's voice to estimate the emotions. Furthermore, the ingredient management unit adjusts the ingredient management method based on the estimated user emotions. For example, if the user is feeling stressed, the ingredient management unit can suggest a simple and hassle-free ingredient management method. On the other hand, if the user is relaxed, the ingredient management unit can suggest a detailed ingredient management method. This allows for more appropriate ingredient management by adjusting the ingredient management method 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. Some or all of the above-described processing in the ingredient management unit may be performed using AI, for example, or may be performed without using AI.

[0099] The ingredient management unit can make suggestions for optimizing the placement of ingredients in the refrigerator during ingredient management. The ingredient management unit optimizes the placement of ingredients in the refrigerator using, for example, a placement algorithm or a storage condition evaluation criterion. For example, the ingredient management unit optimizes the placement of ingredients in the refrigerator and suggests a placement that makes them easy to access. The ingredient management unit can also optimize the placement of ingredients in the refrigerator and suggest a placement that maintains good storage conditions. Furthermore, the ingredient management unit can optimize the placement of ingredients in the refrigerator and suggest a placement that makes effective use of space. For example, the ingredient management unit can input the placement of ingredients in the refrigerator and suggest a placement using an AI model that optimizes it. In this way, optimizing the placement of ingredients in the refrigerator can improve ease of access and storage conditions. Some or all of the above-described processing in the ingredient management unit may be performed, for example, using AI, or may be performed without using AI.

[0100] The ingredient management unit can provide advice on ingredient storage methods during ingredient management. The ingredient management unit provides advice on ingredient storage methods using criteria such as storage temperature, storage period, and storage container. For example, the ingredient management unit provides advice on ingredient storage methods and suggests methods to maintain freshness. The ingredient management unit can also provide advice on ingredient storage methods and suggest methods for long-term storage. Furthermore, the ingredient management unit can provide advice on ingredient storage methods and suggest methods to maintain nutritional value. For example, the ingredient management unit can provide advice on storage methods using an AI model that inputs a storage method and provides advice based on that input. In this way, by providing advice on ingredient storage methods, freshness and nutritional value can be maintained. Some or all of the above-described processing in the ingredient management unit may be performed, for example, using AI, or may be performed without using AI.

[0101] During ingredient management, the ingredient management unit can analyze the frequency of ingredient use and suggest the optimal timing for purchasing. The ingredient management unit analyzes the frequency of ingredient use using, for example, a usage history recording method and frequency evaluation criteria. For example, the ingredient management unit analyzes the frequency of ingredient use and suggests the optimal timing for purchasing. The ingredient management unit can also analyze the frequency of ingredient use and suggest the timing for purchasing that will minimize waste. Furthermore, the ingredient management unit can analyze the frequency of ingredient use and suggest the efficient timing for purchasing. For example, the ingredient management unit can suggest the timing for purchasing using an AI model that inputs the frequency of use and suggests the timing for purchasing based on that. In this way, by analyzing the frequency of ingredient use, it is possible to suggest the timing for purchasing that will minimize waste. Some or all of the above-described processing in the ingredient management unit may be performed using, for example, AI, or may be performed without using AI.

[0102] The ingredient management unit can estimate the user's emotions and determine the priority of ingredient management based on the estimated user emotions. The ingredient management unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the ingredient management unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The ingredient management unit can also estimate the user's emotions using voice analysis technology. For example, the ingredient management unit analyzes the tone and speed of the user's voice to estimate the emotions. Furthermore, the ingredient management unit determines the priority of ingredient management based on the estimated user emotions. For example, if the user is stressed, the ingredient management unit prioritizes the management of important ingredients. On the other hand, if the user is relaxed, the ingredient management unit can perform detailed ingredient management. This enables more appropriate ingredient management by determining the priority of ingredient management according to the user's emotions. Emotion estimation is realized 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. Some or all of the above-described processing in the ingredient management unit may be performed using AI, for example, or may be performed without using AI.

[0103] The ingredient management unit can customize the ingredient management method by taking into account the user's family composition and meal amounts when managing ingredients. The ingredient management unit considers the user's family composition and meal amounts, for example, using the family composition input method and meal amount evaluation criteria. For example, the ingredient management unit considers the user's family composition and meal amounts to suggest appropriate ingredient amounts. The ingredient management unit can also consider the user's meal amounts to suggest waste-free ingredient management. Furthermore, the ingredient management unit can combine the user's family composition and meal amounts to suggest an optimal ingredient management method. For example, the ingredient management unit can input the family composition and meal amounts and suggest a management method using an AI model that suggests a management method based on the input. This enables waste-free ingredient management by taking the user's family composition and meal amounts into account. Some or all of the above-mentioned processing in the ingredient management unit may be performed, for example, using AI, or may be performed without using AI.

[0104] During ingredient management, the ingredient management unit can analyze the user's ingredient purchase history and propose an optimal management method. The ingredient management unit, for example, analyzes the user's ingredient purchase history using a history data storage method and an analysis algorithm. For example, the ingredient management unit analyzes the user's ingredient purchase history and proposes an optimal storage method. The ingredient management unit can also analyze the user's purchase history and propose a waste-free management method. Furthermore, the ingredient management unit can analyze the user's purchase history and propose an efficient management method. For example, the ingredient management unit can propose a management method using an AI model that inputs the purchase history and proposes a management method based on that history. In this way, a waste-free management method can be proposed by analyzing the user's purchase history. Some or all of the above-described processing in the ingredient management unit may be performed, for example, using AI, or may be performed without using AI.

[0105] During ingredient management, the ingredient management unit can learn the user's ingredient consumption patterns and optimize the management method. The ingredient management unit learns the user's ingredient consumption patterns, for example, using a consumption history recording method and pattern evaluation criteria. For example, the ingredient management unit learns the user's ingredient consumption patterns and suggests an optimal storage method. The ingredient management unit can also learn the user's consumption patterns and suggest a waste-free management method. Furthermore, the ingredient management unit can learn the user's consumption patterns and suggest an efficient management method. For example, the ingredient management unit can suggest a management method using an AI model that inputs the consumption patterns and suggests a management method based on them. In this way, by learning the user's consumption patterns, it is possible to suggest a waste-free management method. Some or all of the above-described processing in the ingredient management unit may be performed, for example, using AI, or may be performed without using AI.

[0106] The reminder unit can estimate the user's emotions and adjust the timing of reminders based on the estimated user emotions. The reminder unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the reminder unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The reminder unit can also estimate the user's emotions using voice analysis technology. For example, the reminder unit analyzes the tone and speed of the user's voice to estimate the emotions. The reminder unit also adjusts the timing of reminders based on the estimated user emotions. For example, if the user is feeling stressed, the reminder unit can reduce the frequency of reminders to reduce the burden. Also, if the user is relaxed, the reminder unit can increase the frequency of reminders to encourage attention. This allows for more appropriate reminders by adjusting the timing of reminders 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. Some or all of the above-described processing in the reminding unit may be performed using AI, for example, or may be performed without using AI.

[0107] The reminding unit can propose optimal reminder timing in consideration of the user's schedule when providing a reminder. The reminding unit considers the user's schedule, for example, using a method for acquiring schedule data and schedule evaluation criteria. For example, the reminding unit considers the user's schedule and proposes appropriate reminder timing. The reminding unit can also analyze the user's schedule and propose efficient reminder timing. Furthermore, the reminding unit can propose efficient reminder timing based on the user's schedule. For example, the reminding unit can input a schedule and propose reminder timing using an AI model that proposes reminder timing based on the schedule. This makes it possible to propose efficient reminder timing by considering the user's schedule. Some or all of the above-described processing in the reminding unit may be performed, for example, using AI, or may be performed without using AI.

[0108] The reminding unit can optimize the reminding method by analyzing the user's past reminder history when reminding. The reminding unit, for example, analyzes the user's past reminder history using a history data storage method or an analysis algorithm. For example, the reminding unit analyzes the user's past reminder history and proposes an optimal reminding method. The reminding unit can also propose an efficient reminding method based on the user's past reminder history. Furthermore, the reminding unit can also propose an efficient reminding method by referring to the user's past reminder history. For example, the reminding unit can propose a reminding method using an AI model that inputs the reminder history and proposes a reminding method based on the input. In this way, an efficient reminding method can be proposed by analyzing the user's past reminder history. Some or all of the above-mentioned processing in the reminding unit may be performed using, for example, AI, or may be performed without using AI.

[0109] The reminding unit can customize a reminder message according to the user's preferences when reminding. The reminding unit provides a reminder message according to the user's preferences, for example, using the message content and customization criteria. For example, the reminding unit provides a reminder message according to the user's preferences to draw attention. The reminding unit can also provide a reminder message according to the user's preferences to reduce the user's burden. Furthermore, the reminding unit can provide a reminder message according to the user's preferences to enable efficient response. For example, the reminding unit can provide a reminder message using an AI model that inputs preferences and customizes the reminder message based on the preferences. This allows for more appropriate reminders by providing a reminder message according to the user's preferences. Some or all of the above-described processing in the reminding unit may be performed, for example, using AI or without AI.

[0110] The reminder unit can estimate the user's emotions and adjust the content of the reminder based on the estimated user's emotions. The reminder unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the reminder unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The reminder unit can also estimate the user's emotions using voice analysis technology. For example, the reminder unit analyzes the tone and speed of the user's voice to estimate the emotions. The reminder unit further adjusts the content of the reminder based on the estimated user's emotions. For example, if the user is feeling stressed, the reminder unit can provide simple, highly visible reminder content. On the other hand, if the user is relaxed, the reminder unit can provide reminder content that includes detailed information. This allows for more appropriate reminders by adjusting the content of the reminder based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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. Some or all of the above-described processing in the reminding unit may be performed using AI, for example, or may be performed without using AI.

[0111] When providing a reminder, the reminding unit can select the optimal reminder method by taking into account the user's device information. The reminding unit can consider the user's device information, for example, using criteria for selecting the device type and the notification method. For example, if the user is using a smartphone, the reminding unit can provide a reminder method that matches the screen size. Furthermore, if the user is using a tablet, the reminding unit can provide a reminder method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the reminding unit can provide a simple and highly visible reminder method. For example, the reminding unit can provide a reminder method using an AI model that inputs device information and selects a reminder method based on that information. This makes it possible to provide a more appropriate reminder method by taking into account the user's device information. Some or all of the above-described processing in the reminding unit may be performed, for example, using AI or without AI.

[0112] The reminding unit can analyze the user's daily rhythm at the time of reminding and optimize the timing of the reminder. The reminding unit analyzes the user's daily rhythm, for example, using a data collection method for the daily rhythm and an evaluation standard for the rhythm. For example, the reminding unit analyzes the user's daily rhythm and suggests the optimal reminder timing. The reminding unit can also suggest an efficient reminder timing based on the user's daily rhythm. Furthermore, the reminding unit can also suggest an efficient reminder timing by taking the user's daily rhythm into consideration. For example, the reminding unit can suggest a reminder timing using an AI model that inputs the user's daily rhythm and suggests a reminder timing based on the input. In this way, efficient reminder timing can be suggested by analyzing the user's daily rhythm. Some or all of the above-mentioned processing in the reminding unit may be performed, for example, using AI, or may be performed without using AI.

[0113] The reminding unit can customize the reminder content by referring to the user's past reminder history when reminding. The reminding unit can refer to the user's past reminder history using, for example, a method for saving history data or criteria for selecting content based on the past history. For example, the reminding unit can refer to the user's past reminder history and provide optimal reminder content. The reminding unit can also provide efficient reminder content based on the user's past reminder history. Furthermore, the reminding unit can provide efficient reminder content by referring to the user's past reminder history. For example, the reminding unit can provide reminder content using an AI model that inputs the reminder history and customizes the reminder content based on the input. This makes it possible to provide efficient reminder content by referring to the user's past reminder history. Some or all of the above-described processing in the reminding unit may be performed, for example, using AI or without AI.

[0114] The priority suggestion unit can estimate the user's emotions and adjust the content of the priority suggestion based on the estimated user's emotions. The priority suggestion unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the priority suggestion unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The priority suggestion unit can also estimate the user's emotions using voice analysis technology. For example, the priority suggestion unit analyzes the tone and speed of the user's voice to estimate the emotions. Furthermore, the priority suggestion unit adjusts the content of the priority suggestion based on the estimated user's emotions. For example, if the user is feeling stressed, the priority suggestion unit can make simple and hassle-free suggestions. On the other hand, if the user is relaxed, the priority suggestion unit can make suggestions that are enjoyable and take time to enjoy. This enables more appropriate suggestions by adjusting the content of the priority suggestion according to the user's emotions. Emotion estimation is realized using an emotion estimation function using, for example, 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. Some or all of the above-described processing in the priority proposal unit may be performed using AI, for example, or may be performed without using AI.

[0115] When making a priority suggestion, the priority suggestion unit can customize the suggestion content by taking into account the user's past meal history. The priority suggestion unit can consider the user's past meal history, for example, using a method for storing or analyzing history data. For example, the priority suggestion unit makes optimal suggestions based on the user's past meal history. The priority suggestion unit can also analyze the user's past meal history to make efficient suggestions. Furthermore, the priority suggestion unit can make efficient suggestions by referring to the user's past meal history. For example, the priority suggestion unit can provide the suggestion content using an AI model that inputs the user's past meal history and customizes the suggestion content based on the input. This enables more appropriate suggestions by taking the user's past meal history into consideration. Some or all of the above-mentioned processing in the priority suggestion unit may be performed, for example, using AI, or may be performed without using AI.

[0116] The priority suggestion unit can learn the user's dietary preferences and tastes and optimize the content of the suggestion when making a priority suggestion. The priority suggestion unit learns the user's dietary preferences and tastes, for example, by analyzing past selection history or by using a method for collecting preference data. For example, the priority suggestion unit learns the user's dietary preferences and makes optimal suggestions. The priority suggestion unit can also learn the user's dietary preferences and make efficient suggestions. Furthermore, the priority suggestion unit can analyze the user's dietary preferences and make efficient suggestions. For example, the priority suggestion unit can provide suggestions using an AI model that inputs the user's dietary preferences and optimizes the content of the suggestion based on the input. This enables more appropriate suggestions to be made by learning the user's dietary preferences and tastes. Some or all of the above-described processing in the priority suggestion unit may be performed, for example, using AI, or may be performed without using AI.

[0117] When making a priority suggestion, the priority suggestion unit can adjust the suggestion content taking into account the nutritional balance of the user's diet. The priority suggestion unit can consider the nutritional balance of the user's diet using, for example, nutrient standards and a balance evaluation method. For example, the priority suggestion unit can make a balanced suggestion taking into account the user's nutritional balance. The priority suggestion unit can also analyze the user's nutritional intake status and make suggestions to supplement deficient nutrients. Furthermore, the priority suggestion unit can make suggestions with appropriate nutritional balance taking into account the user's health status. For example, the priority suggestion unit can provide the suggestion content using an AI model that inputs nutritional balance and adjusts the suggestion content based on that. This enables healthy suggestions to be made by taking into account the user's nutritional balance. Some or all of the above-mentioned processing in the priority suggestion unit may be performed, for example, using AI, or may be performed without using AI.

[0118] The priority suggestion unit can estimate the user's emotions and adjust the frequency of priority suggestions based on the estimated user emotions. The priority suggestion unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the priority suggestion unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The priority suggestion unit can also estimate the user's emotions using voice analysis technology. For example, the priority suggestion unit can analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the priority suggestion unit can adjust the frequency of priority suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the priority suggestion unit can reduce the suggestion frequency to reduce the burden. Also, if the user is relaxed, the priority suggestion unit can increase the suggestion frequency to expand the options. This enables more appropriate suggestions by adjusting the suggestion frequency according to the user's emotions. Emotion estimation is realized using an emotion estimation function using, for example, 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. Some or all of the above-described processing in the priority proposal unit may be performed using AI, for example, or may be performed without using AI.

[0119] When making a priority suggestion, the priority suggestion unit can adjust the suggestion content taking into account the user's meal budget. The priority suggestion unit considers the user's meal budget using, for example, a budget input method or cost-performance evaluation criteria. For example, the priority suggestion unit makes cost-effective suggestions to fit within the user's budget. The priority suggestion unit can also consider the user's budget and make suggestions with high cost performance. Furthermore, the priority suggestion unit can suggest ingredient options according to the user's budget. For example, the priority suggestion unit can provide suggestions using an AI model that inputs a budget and adjusts the suggestion content based on the budget. This makes it possible to make suggestions with high cost performance by considering the user's budget. Some or all of the above-described processing in the priority suggestion unit may be performed using, for example, AI, or may be performed without using AI.

[0120] The priority suggestion unit can customize the suggestion content by taking into account the amount of food the user eats when making a priority suggestion. The priority suggestion unit can consider the amount of food the user eats, for example, by using an input method for the amount of food and an evaluation criterion for the amount. For example, the priority suggestion unit can consider the amount of food the user eats and suggest appropriate portions. The priority suggestion unit can also make suggestions that do not waste food based on the amount of food the user eats. Furthermore, the priority suggestion unit can make efficient suggestions by referring to the amount of food the user eats. For example, the priority suggestion unit can provide suggestions using an AI model that inputs the amount of food and customizes the suggestion content based on that. This makes it possible to make suggestions that do not waste food by taking into account the amount of food the user eats. Some or all of the above-mentioned processing in the priority suggestion unit may be performed, for example, using AI, or may be performed without using AI.

[0121] The priority suggestion unit can adjust the proposal content taking into account the user's mealtimes when making a priority suggestion. The priority suggestion unit can consider the user's mealtimes, for example, by using a time-of-day input method or suggestion criteria according to the time of day. For example, the priority suggestion unit makes appropriate suggestions taking into account the user's mealtimes. The priority suggestion unit can also make efficient suggestions based on the user's mealtimes. Furthermore, the priority suggestion unit can make efficient suggestions by referring to the user's mealtimes. For example, the priority suggestion unit can provide proposal content using an AI model that inputs mealtimes and adjusts the proposal content based on the input. This enables more appropriate suggestions by taking the user's mealtimes into consideration. Some or all of the above-described processing in the priority suggestion unit may be performed using, for example, AI, or may be performed without using AI. === Hard Collateral 1-1 === Each of the multiple elements, including the receipt analysis unit, menu suggestion unit, ingredient management unit, reminder unit, and priority suggestion unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the receipt analysis unit photographs a receipt using the camera 42 of the smart device 14 and analyzes it using the specific processing unit 290 of the data processing device 12. The menu suggestion unit is implemented by the specific processing unit 290 of the data processing device 12 and proposes a menu based on the analyzed ingredient information. The ingredient management unit is implemented by the specific processing unit 290 of the data processing device 12 and manages used and unused ingredients. The reminder unit is implemented by the specific processing unit 290 of the data processing device 12 and notifies the user of ingredients that are close to their expiration date. The priority suggestion unit is implemented by the specific processing unit 290 of the data processing device 12 and proposes a menu that prioritizes the use of ingredients that have been reminded. Furthermore, the receipt analysis unit can photograph the user's facial expression using the camera 42 of the smart device 14 and estimate their emotion using an emotion estimation algorithm. === Hard Collateral 1-2 === Each of the multiple elements, including the receipt analysis unit, menu suggestion unit, ingredient management unit, reminder unit, and priority suggestion unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the receipt analysis unit photographs a receipt using the camera 42 of the smart glasses 214, which is then analyzed by the specific processing unit 290 of the data processing device 12. The menu suggestion unit is implemented by the specific processing unit 290 of the data processing device 12 and proposes a menu based on the analyzed ingredient information. The ingredient management unit is implemented by the specific processing unit 290 of the data processing device 12 and manages used and unused ingredients. The reminder unit is implemented by the specific processing unit 290 of the data processing device 12 and notifies the user of ingredients that are close to their expiration date. The priority suggestion unit is implemented by the specific processing unit 290 of the data processing device 12 and proposes a menu that prioritizes the use of ingredients that have been reminded. Furthermore, the receipt analysis unit can photograph the user's facial expression using the camera 42 of the smart glasses 214 and estimate their emotion using an emotion estimation algorithm. === Hard Collateral 1-3 === Each of the multiple elements, including the receipt analysis unit, menu suggestion unit, ingredient management unit, reminder unit, and priority suggestion unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the receipt analysis unit photographs a receipt using the camera 42 of the headset terminal 314, which is then analyzed by the specific processing unit 290 of the data processing device 12. The menu suggestion unit is implemented by the specific processing unit 290 of the data processing device 12 and proposes a menu based on the analyzed ingredient information. The ingredient management unit is implemented by the specific processing unit 290 of the data processing device 12 and manages used and unused ingredients. The reminder unit is implemented by the specific processing unit 290 of the data processing device 12 and notifies the user of ingredients that are close to their expiration date. The priority suggestion unit is implemented by the specific processing unit 290 of the data processing device 12 and proposes a menu that prioritizes the use of ingredients that have been reminded. Furthermore, the receipt analysis unit can photograph the user's facial expression using the camera 42 of the headset terminal 314 and estimate their emotion using an emotion estimation algorithm. === Hard Collateral 1-4 === Each of the multiple elements, including the receipt analysis unit, menu suggestion unit, ingredient management unit, reminder unit, and priority suggestion unit, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the receipt analysis unit photographs a receipt using the camera 42 of the robot 414, which is then analyzed by the specific processing unit 290 of the data processing device 12. The menu suggestion unit is implemented by the specific processing unit 290 of the data processing device 12 and proposes a menu based on the analyzed ingredient information. The ingredient management unit is implemented by the specific processing unit 290 of the data processing device 12 and manages used and unused ingredients. The reminder unit is implemented by the specific processing unit 290 of the data processing device 12 and notifies the user of ingredients that are close to their expiration date. The priority suggestion unit is implemented by the specific processing unit 290 of the data processing device 12 and proposes a menu that prioritizes the use of ingredients that have been reminded. Furthermore, the receipt analysis unit can photograph the user's facial expression using the camera 42 of the robot 414 and estimate their emotions using an emotion estimation algorithm.

[0122] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0123] The food ingredient management system can also include a nutritional analysis unit that analyzes the nutritional value of ingredients. The nutritional analysis unit calculates the nutritional value of each ingredient based on the ingredient information obtained from the receipt analysis unit and provides the result to the user. For example, the nutritional analysis unit analyzes nutrients such as vitamins, minerals, proteins, lipids, and carbohydrates, and suggests balanced meals to the user. The nutritional analysis unit can also suggest menus that emphasize specific nutrients based on the user's health condition and goals (e.g., dieting or muscle building). Furthermore, the nutritional analysis unit can record the history of nutrients consumed by the user to support long-term health management. This allows the food ingredient management system to provide more comprehensive support for the user's health.

[0124] The receipt analysis unit can also analyze the origin information of purchased ingredients. For example, it can extract the origin information written on the receipt and provide it to the user. This allows the user to understand the origin of the purchased ingredients and prioritize using locally produced ingredients. The receipt analysis unit can also estimate the freshness of ingredients based on the origin information and notify the user. For example, ingredients transported from a distant location may be less fresh, so the receipt analysis unit can remind the user to use them sooner. Furthermore, the receipt analysis unit can evaluate the quality of ingredients based on the origin information and provide it to the user. This allows the user to select high-quality ingredients.

[0125] The menu suggestion unit can also collect the user's satisfaction with the meal as feedback and reflect it in the next suggestion. For example, the user actually prepares the suggested menu and inputs their satisfaction level after the meal. The menu suggestion unit learns the user's tastes and preferences based on this feedback and optimizes the next suggestion. The menu suggestion unit can also adjust the frequency of specific ingredients and dishes based on the user's satisfaction. Furthermore, the menu suggestion unit can also suggest new recipes and ingredients based on the user's satisfaction. This allows the user to always enjoy new dishes.

[0126] The food management unit can also be equipped with sensors that monitor the storage conditions of food ingredients in real time. For example, sensors that measure temperature and humidity can be installed inside the refrigerator to monitor the storage conditions of food ingredients. The food management unit can evaluate the storage conditions of food ingredients based on data from the sensors and suggest appropriate storage methods. The food management unit can also notify the user if the storage conditions deteriorate, reminding them to use the food ingredients sooner. Furthermore, the food management unit can predict food deterioration based on storage condition data and suggest the optimal time to use the food ingredients. This can further reduce food waste.

[0127] The reminder unit can also learn the user's lifestyle rhythm and suggest optimal reminder timing. For example, it can learn the time the user eats breakfast or cooks dinner and send reminders based on those times. The reminder unit can adjust the frequency and timing of reminders based on the user's lifestyle rhythm. The reminder unit can also automatically update the reminder timing if the user's lifestyle rhythm changes. Furthermore, the reminder unit can also suggest menus suitable for specific time periods based on the user's lifestyle rhythm. This allows the user to use ingredients without straining themselves.

[0128] The priority suggestion unit can further estimate the user's emotions and adjust the content of the priority suggestions based on the estimated user's emotions. For example, if the user is feeling stressed, the priority suggestion unit can suggest a simple and hassle-free menu. Also, if the user is relaxed, the priority suggestion unit can suggest a menu that can be enjoyed over time. Furthermore, the priority suggestion unit can adjust the frequency and combination of ingredients used according to the user's emotions. For example, if the user is tired, the priority suggestion unit can suggest a menu using ingredients that replenish energy. In this way, it is possible to suggest an optimal menu according to the user's emotions.

[0129] The menu suggestion unit can further estimate the user's emotions and adjust the menu suggestion content based on the estimated user's emotions. For example, if the user is feeling stressed, the menu suggestion unit can suggest a simple and hassle-free menu. Also, if the user is relaxed, the menu suggestion unit can suggest a menu that can be enjoyed over time. Furthermore, the menu suggestion unit can adjust the frequency and combination of ingredients used according to the user's emotions. For example, if the user is tired, the menu suggestion unit can suggest a menu that uses ingredients that replenish energy. In this way, it is possible to suggest an optimal menu according to the user's emotions.

[0130] The receipt analysis unit can further estimate the user's emotions and adjust the accuracy of the receipt analysis based on the estimated user emotions. For example, if the user is feeling stressed, the receipt analysis unit will increase the analysis accuracy and provide results quickly. On the other hand, if the user is relaxed, the receipt analysis unit will perform a more detailed analysis and provide more information. Furthermore, the receipt analysis unit can adjust the display method of the analysis results according to the user's emotions. For example, if the user is tired, the receipt analysis unit will provide a simple, highly visible display method. This makes it possible to provide optimal analysis results according to the user's emotions.

[0131] The reminder unit can further estimate the user's emotions and adjust the timing of reminders based on the estimated user emotions. For example, if the user is feeling stressed, the reminder unit can reduce the frequency of reminders to reduce the burden. Also, if the user is relaxed, the reminder unit can increase the frequency of reminders to encourage attention. Furthermore, the reminder unit can adjust the content of reminders according to the user's emotions. For example, if the user is tired, the reminder unit can provide simple, highly visible reminder content. This makes it possible to provide optimal reminders according to the user's emotions.

[0132] The ingredient management unit can further estimate the user's emotions and adjust the ingredient management method based on the estimated user's emotions. For example, if the user is feeling stressed, the ingredient management unit can suggest a simple and hassle-free ingredient management method. On the other hand, if the user is relaxed, the ingredient management unit can suggest a detailed ingredient management method. Furthermore, the ingredient management unit can adjust the ingredient storage method and frequency of use according to the user's emotions. For example, if the user is tired, the ingredient management unit can suggest that ingredients that are easy to store and last a long time be used preferentially. This makes it possible to provide optimal ingredient management according to the user's emotions.

[0133] The processing flow of the second embodiment will be briefly explained below.

[0134] Step 1: The receipt analysis unit analyzes the contents of the receipt. The contents of the receipt include the purchased items, price, purchase date and time, etc. The receipt analysis unit uses OCR technology to convert the text information on the receipt into digital data, and then uses natural language processing technology to extract ingredient information. For example, it uses an AI model that takes an image of the receipt as input and outputs ingredient information. Step 2: The menu suggestion unit proposes a menu based on the information analyzed by the receipt analysis unit. The menu suggestion unit considers nutritional balance, calories, and ingredient combinations when proposing a menu. For example, it uses an AI model that inputs the extracted ingredient information and outputs a menu. Step 3: The ingredient management unit manages ingredients used based on the menu proposed by the menu proposal unit, as well as ingredients purchased in the past but not used. The ingredient management unit manages inventory and records usage history, records ingredients used based on the menu information entered by the user, and reflects this in the next menu proposal. Step 4: The reminder unit reminds the user about ingredients whose expiration date is approaching based on the ingredient information managed by the ingredient management unit. The reminder unit sets the notification method and reminder timing, and notifies the user of ingredients whose expiration date is approaching to encourage them to use them. Step 5: The priority suggestion unit proposes a menu that prioritizes the use of ingredients that have been reminded by the reminder unit. The priority suggestion unit sets criteria to prioritize ingredients with an approaching expiration date, and uses an AI model that inputs the reminded ingredients and outputs a menu that prioritizes the use of ingredients.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0156] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

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

[0181] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0182] 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.

[0183] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0184] 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.

[0185] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. 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.

[0186] 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.

[0187] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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).

[0192] 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.

[0193] 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."

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] [Explanation of symbols]

[0207] 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 receipt analysis unit that analyzes the contents of a receipt; a menu suggestion unit that suggests a menu based on the information analyzed by the receipt analysis unit; an ingredient management unit that manages ingredients used based on the menu proposed by the menu proposal unit and ingredients purchased in the past but not used; a reminder unit that reminds the user of ingredients whose expiration date is approaching based on the ingredient information managed by the ingredient management unit; a priority suggestion unit that suggests a menu that uses the ingredients reminded by the reminding unit with priority A system characterized by:

2. The receipt analysis unit Analyze the receipt contents and extract ingredient information 2. The system of claim 1.

3. The menu suggestion unit Suggest a menu based on extracted ingredient information 2. The system of claim 1.

4. The ingredient management unit Manage ingredients used based on the menu entered by the user and ingredients purchased in the past but not used 2. The system of claim 1.

5. The reminding unit Reminds you about food that is nearing its expiration date 2. The system of claim 1.

6. The priority proposal unit Suggest a menu that prioritizes the ingredients you have been reminded of 2. The system of claim 1.

7. The menu suggestion unit Propose menus that take into account the family's medical history 2. The system of claim 1.

8. The receipt analysis unit Estimate user emotions and adjust receipt analysis accuracy based on the estimated user emotions 2. The system of claim 1.

9. The receipt analysis unit Automatically corrects light reflections and shadows when photographing receipts 2. The system of claim 1.

10. The receipt analysis unit Recognizes handwritten notes and additional information when parsing receipts 2. The system of claim 1.

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