System

Abstract

To provide a system that effectively utilizes surplus ingredients and reduces food loss.SOLUTION: A system comprises: a registration unit; a recipe creation unit; and a price proposal unit. The registration unit registers information about ingredients. The recipe creation unit analyzes the information registered by the registration unit and creates recipes. The price proposal unit proposes set selling prices on the basis of the recipes created by the recipe creation unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

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

[0004] Conventional technology does not efficiently create recipes or propose prices to make effective use of surplus ingredients, so there is room for improvement in reducing food waste.

[0005] The system according to the embodiment aims to effectively utilize surplus food ingredients and reduce food waste. [Means for solving the problem]

[0006] The system according to the embodiment includes a registration unit, a recipe creation unit, and a price proposal unit. The registration unit registers information about ingredients. The recipe creation unit analyzes the information registered by the registration unit and creates recipes. The price proposal unit proposes a set sales price based on the recipe created by the recipe creation unit. Effect of the Invention

[0007] The system according to the embodiment can effectively utilize surplus ingredients and reduce food waste. [Brief description of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The food waste reduction system according to the embodiment of the present invention is a system in which information on ingredients likely to be left over at supermarkets and food stores is registered, AI automatically creates recipes, and suggests a set selling price for the ingredients. This food waste reduction system reduces local food waste and promotes the effective use of ingredients by registering information on ingredients, analyzing it, creating recipes with AI, and proposing a set selling price. For example, if there are ingredients such as tomatoes, cabbage, and chicken that are likely to be left over at supermarkets and food stores, the information on them is entered into the AI. At this time, detailed information such as the type and amount of ingredients and the expiration date is entered. Next, the AI ​​analyzes the information on the registered ingredients and automatically creates the optimal recipe. For example, when creating a recipe using tomatoes, cabbage, and chicken, the AI ​​combines these ingredients and suggests recipes such as tomato and cabbage soup and chicken stewed in tomato. In addition, more specific recipes can be created by setting conditions such as the amount of the recipe, the genre of the dish, and the cooking time. In addition, the AI ​​also suggests a set selling price for the ingredients based on the created recipe. For example, when a recipe using tomatoes, cabbage, and chicken is proposed, the AI ​​calculates and suggests the price at which these ingredients are sold as a set. This allows consumers to easily purchase ingredients needed for a recipe, and allows supermarkets and grocery stores to efficiently sell surplus ingredients. This system reduces local food waste and leads to effective use of ingredients. For example, food waste can be reduced by providing surplus ingredients at supermarkets and grocery stores to consumers rather than wasting them. Also, consumers can easily purchase ingredients needed for a recipe and prepare delicious dishes at home. In this way, the food waste reduction system can reduce local food waste and promote effective use of ingredients.

[0029] The food waste reduction system according to the embodiment includes a registration unit, a recipe creation unit, and a price proposal unit. The registration unit registers information on ingredients that are likely to be surplus at supermarkets and food stores. The information on ingredients includes, for example, the type, amount, freshness, place of origin, and expiration date of the ingredients, but is not limited to such examples. The registration unit can register the information on ingredients, for example, by manual input, automatic scanning, or a method of saving in a database. The recipe creation unit uses AI to analyze the information on ingredients registered by the registration unit and create a recipe. The recipe includes, for example, an ingredient list, cooking steps, cooking time, and nutritional information, but is not limited to such examples. The recipe creation unit analyzes the information on ingredients using methods such as data mining, statistical analysis, and machine learning algorithms, and creates an optimal recipe. The price proposal unit proposes a set sales price for ingredients based on the recipe created by the recipe creation unit. The set sales price includes, for example, cost calculation, profit margin, discount rate, and the like, but is not limited to such examples. The price proposal unit proposes a set sales price for ingredients based on, for example, cost calculation. The price proposal unit can also propose a price taking into account a profit margin. The price proposal unit can also propose a price by applying a discount rate. As a result, the food waste reduction system according to the embodiment can register information on ingredients that are likely to be left over at supermarkets and grocery stores, have AI automatically create recipes, and propose a set selling price for the ingredients. Some or all of the above-mentioned processing in the price proposal unit can be performed, for example, using AI, or can be performed without using AI. For example, the price proposal unit can propose a price using an AI model that inputs a recipe created by the recipe creation unit and outputs a set selling price. As a result, the food waste reduction system can reduce local food waste and promote the effective use of ingredients.

[0030] The registration unit registers information on ingredients that are likely to be left over at supermarkets and food stores. The information on ingredients includes, but is not limited to, the type, amount, freshness, place of origin, and expiration date of the ingredients. The registration unit can register the information on ingredients by, for example, manual input, automatic scanning, or a method of saving to a database. Specifically, in the case of manual input, a store clerk uses a dedicated terminal or application to input the information on ingredients one by one. In the case of automatic scanning, a barcode reader or a two-dimensional code (e.g., QR Code (registered trademark)) reader is used to read the code printed on the packaging of the ingredients and automatically obtain the information. Furthermore, it is also possible to read the information on ingredients without contact using an RFID tag. As a method of saving to a database, a cloud-based database can be used to centrally manage information from multiple stores and update it in real time. This allows the registration unit to efficiently and accurately collect information on ingredients and build the foundation of a food loss reduction system. In addition, the registration unit can periodically update the information on ingredients to respond to changes in freshness and expiration date. For example, if an ingredient is nearing its expiration date, an alert can be issued and measures can be taken at an early stage. This allows the registration unit to minimize ingredient waste and achieve efficient inventory management.

[0031] The recipe creation unit uses AI to analyze the information of the ingredients registered by the registration unit and create a recipe. The recipe includes, for example, an ingredient list, cooking steps, cooking time, nutritional information, etc., but is not limited to such examples. The recipe creation unit analyzes the information of the ingredients using methods such as data mining, statistical analysis, and machine learning algorithms, and creates an optimal recipe. Specifically, the AI ​​finds the optimal combination based on information such as the type and amount of the registered ingredients, freshness, and expiration date, and generates a balanced recipe. For example, the AI ​​can use a machine learning algorithm to learn past recipe data and user preference data and propose new recipes. Furthermore, the AI ​​can also create recipes for providing healthy meals by taking into account nutritional information. For example, a nutritionally balanced recipe is provided by preferentially using ingredients that contain a large amount of a specific nutrient. In addition, the recipe creation unit can propose recipes that meet the needs of the user by taking into account cooking time and difficulty. For example, a recipe that can be cooked in a short time is provided to a busy user, and a recipe that can be made with simple steps is provided to a beginner cook. In this way, the recipe creation unit can respond to the diverse needs of users and promote the effective use of ingredients.

[0032] The price proposal unit proposes a set selling price of ingredients based on the recipe created by the recipe creation unit. The set selling price includes, but is not limited to, cost calculation, profit margin, discount rate, etc., for example. The price proposal unit proposes a set selling price of ingredients based on cost calculation, for example. Specifically, the total cost is calculated taking into account the purchase price of each ingredient, transportation cost, storage cost, etc., and an appropriate selling price is set based on the total cost. The price proposal unit can also propose a price taking into account the profit margin. For example, in order to ensure a certain profit margin, an appropriate margin is added to the cost and the selling price is set. Furthermore, the price proposal unit can also propose a price by applying a discount rate. For example, for ingredients whose expiration date is approaching, a high discount rate can be set and the ingredients can be sold early to prevent food loss. A part or all of the above-mentioned processing in the price proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the price proposal unit can propose a price using an AI model that inputs the recipe created by the recipe creation unit and outputs a set selling price. The AI ​​model can learn from past sales data and market trends to predict optimal prices, allowing the price proposal department to set prices efficiently and effectively and promote sales of ingredients.

[0033] The food loss reduction system includes a condition setting unit that sets the recipe quantity, food genre, and cooking time conditions. The condition setting unit sets conditions such as the recipe quantity, food genre, and cooking time. The quantity includes, for example, units such as grams, cups, and number, but is not limited to such examples. The food genre includes, for example, classifications such as Japanese food, Western food, and Chinese food, but is not limited to such examples. The cooking time includes, for example, preparation time, cooking time, and total time, but is not limited to such examples. The condition setting unit sets the recipe quantity, for example, by inputting a desired quantity by the user. The condition setting unit can also set the recipe genre by selecting a desired food genre by the user. The condition setting unit can also set the cooking time of the recipe by inputting a desired cooking time by the user. In this way, the condition setting unit can provide a recipe that meets the needs of the user. A part or all of the above-mentioned processing in the condition setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the condition setting unit can input the user's input as a prompt to the AI, and the AI ​​can create a recipe based on the conditions. This allows the condition setting unit to provide recipes that meet the needs of the user.

[0034] The food loss reduction system includes a providing unit that provides the created recipe to the user. The providing unit provides the created recipe to the user. The providing method includes, for example, email, app notification, printed matter, and the like, but is not limited to such examples. For example, the providing unit transmits the created recipe to the user by email. The providing unit can also provide the created recipe to the user by app notification. The providing unit can also provide the created recipe as a printed matter. As a result, the providing unit can promote effective use of ingredients by providing the recipe to the user. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs the created recipe to the AI, and the AI ​​can select the optimal providing method for the user. As a result, the providing unit can promote effective use of ingredients by providing the recipe to the user.

[0035] The food waste reduction system includes a notification unit that notifies the user of the set sales price of the ingredients. The notification unit notifies the user of the set sales price of the ingredients. Notification methods include, for example, push notification, SMS, email, and the like, but are not limited to such examples. For example, the notification unit notifies the user of the set sales price of the ingredients by push notification. The notification unit can also notify the user of the set sales price of the ingredients by SMS. The notification unit can also notify the user of the set sales price of the ingredients by email. In this way, the notification unit can promote the purchase of ingredients by notifying the user of the set sales price. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit inputs the set sales price of the ingredients into AI, and the AI ​​can select the optimal notification method for the user. In this way, the notification unit can promote the purchase of ingredients by notifying the user of the set sales price.

[0036] The recipe creation unit can create recipes using tomatoes, cabbage, or chicken. The recipe creation unit creates recipes using tomatoes, cabbage, or chicken, for example. Tomatoes include, for example, information such as variety, place of origin, and freshness, but are not limited to such examples. Cabbage includes, for example, information such as variety, place of origin, and freshness, but are not limited to such examples. Chicken includes, for example, information such as variety, place of origin, and freshness, but are not limited to such examples. The recipe creation unit creates, for example, tomato soup or tomato salad as recipes using tomatoes. The recipe creation unit can also create cabbage soup or stir-fried cabbage as recipes using cabbage. The recipe creation unit can also create chicken stewed in tomato sauce or chicken wrapped in cabbage as recipes using chicken. In this way, the recipe creation unit can promote effective use of ingredients by creating recipes using specific ingredients. A part or all of the above-mentioned processing in the recipe creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the recipe creation unit can input information about tomatoes, cabbage, or chicken into the AI, which can then create the optimal recipe. This allows the recipe creation unit to promote the effective use of ingredients by creating recipes that use specific ingredients.

[0037] The price proposal unit can propose a set selling price of ingredients based on the created recipe. The price proposal unit, for example, proposes a set selling price of ingredients based on the created recipe. The set selling price includes, for example, cost calculation, profit margin, discount rate, etc., but is not limited to such examples. The price proposal unit, for example, proposes a set selling price of ingredients based on cost calculation. The price proposal unit can also propose a price taking into account a profit margin. The price proposal unit can also propose a price by applying a discount rate. In this way, the price proposal unit can promote sales of ingredients by proposing a price based on the recipe. A part or all of the above-mentioned processing in the price proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the price proposal unit can input the created recipe into AI, and the AI ​​can propose an optimal set selling price. In this way, the price proposal unit can promote sales of ingredients by proposing a price based on the recipe.

[0038] The registration unit can analyze the user's past registration history and select the optimal registration method when registering ingredients. For example, the registration unit automatically displays ingredients that the user has frequently registered in the past as candidates. The registration unit can also preferentially suggest registration methods (such as voice and text) that the user has used in the past. The registration unit can also predict and suggest ingredients to be registered in a specific time period from the user's past registration history. In this way, the registration unit can provide the user with the optimal registration method by analyzing the past registration history. A part or all of the above-mentioned processing in the registration unit may be performed, for example, using AI, or may be performed without using AI. For example, the registration unit can input the user's past registration history into AI and cause AI to select the optimal registration method. In this way, the registration unit can provide the user with the optimal registration method by analyzing the past registration history.

[0039] The registration unit can filter the registration information based on the freshness and quality of the ingredients when registering the ingredients. For example, if the freshness of the ingredients is low, the registration unit suggests refraining from registering the ingredients. In addition, the registration unit can also suggest that the ingredients be registered preferentially if the quality of the ingredients is high. In addition, the registration unit can also suggest that the ingredients be registered as soon as possible if the expiration date of the ingredients is approaching. In this way, the registration unit can register appropriate ingredient information by filtering the registration information based on the freshness and quality of the ingredients. A part or all of the above-mentioned processing in the registration unit may be performed, for example, using AI, or may be performed without using AI. For example, the registration unit can input data on the freshness and quality of the ingredients to the AI ​​and cause the AI ​​to filter the registration information. In this way, the registration unit can register appropriate ingredient information by filtering the registration information based on the freshness and quality of the ingredients.

[0040] The registration unit can preferentially register highly relevant ingredient information in consideration of the geographical location information of the user when registering ingredients. For example, when the user is in a specific area, the registration unit preferentially registers ingredient information that is often used in that area. In addition, when the user is traveling, the registration unit can also preferentially register ingredient information available in the travel destination area. In addition, when the user is at home, the registration unit can also preferentially register ingredient information available around the home. In this way, the registration unit can preferentially register highly relevant ingredient information by considering the geographical location information. A part or all of the above-mentioned processing in the registration unit may be performed, for example, using AI, or may be performed without using AI. For example, the registration unit can input the geographical location information of the user to the AI ​​and cause the AI ​​to select highly relevant ingredient information. In this way, the registration unit can preferentially register highly relevant ingredient information by considering the geographical location information.

[0041] The registration unit can analyze the user's social media activity and register related ingredient information when registering ingredients. For example, the registration unit automatically registers ingredient information shared by the user on social media. The registration unit can also preferentially register ingredient information of cooking accounts that the user follows on social media. The registration unit can also automatically register ingredient information that the user has "liked" on social media. This allows the registration unit to automatically register related ingredient information by analyzing social media activity. A part or all of the above-mentioned processing in the registration unit may be performed, for example, using AI, or may be performed without using AI. For example, the registration unit can input the user's social media activity data into AI and cause AI to register related ingredient information. This allows the registration unit to automatically register related ingredient information by analyzing social media activity.

[0042] The recipe creation unit can adjust the level of detail of the recipe based on the importance of the ingredients when creating the recipe. The recipe creation unit, for example, provides detailed cooking instructions for main ingredients. The recipe creation unit can also provide concise cooking instructions for supplementary ingredients. The recipe creation unit can also adjust the number of steps of the recipe according to the importance of the ingredients. As a result, the recipe creation unit can provide an appropriate recipe by adjusting the level of detail of the recipe based on the importance of the ingredients. Some or all of the above-mentioned processing in the recipe creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the recipe creation unit can input the importance data of ingredients to the AI ​​and cause the AI ​​to adjust the level of detail of the recipe. As a result, the recipe creation unit can provide an appropriate recipe by adjusting the level of detail of the recipe based on the importance of the ingredients.

[0043] The recipe creation unit can apply different recipe algorithms depending on the category of ingredients when creating a recipe. For example, the recipe creation unit applies a recipe algorithm dedicated to vegetables to ingredients in the vegetable category. The recipe creation unit can also apply a recipe algorithm dedicated to meat to ingredients in the meat category. The recipe creation unit can also apply a recipe algorithm dedicated to seafood to ingredients in the seafood category. In this way, the recipe creation unit can provide an appropriate recipe by applying different recipe algorithms depending on the category of ingredients. Some or all of the above-mentioned processes in the recipe creation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recipe creation unit can input ingredient category data to the AI ​​and cause the AI ​​to apply different recipe algorithms. In this way, the recipe creation unit can provide an appropriate recipe by applying different recipe algorithms depending on the category of ingredients.

[0044] The recipe creation unit can determine the priority of recipes based on the time when ingredients are obtained when creating a recipe. The recipe creation unit, for example, provides a recipe that prioritizes the use of seasonal ingredients. The recipe creation unit can also provide a recipe that prioritizes the use of ingredients that are close to their expiration date. The recipe creation unit can also provide a recipe that prioritizes the use of seasonal ingredients. In this way, the recipe creation unit can provide an appropriate recipe by determining the priority of recipes based on the time when ingredients are obtained. A part or all of the above-mentioned processing in the recipe creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the recipe creation unit can input data on the time when ingredients are obtained to AI and cause AI to determine the priority of recipes. In this way, the recipe creation unit can provide an appropriate recipe by determining the priority of recipes based on the time when ingredients are obtained.

[0045] The recipe creation unit can adjust the order of the recipe based on the relevance of ingredients when creating a recipe. For example, the recipe creation unit provides a recipe that uses a main ingredient first. The recipe creation unit can also provide a recipe that uses a supplementary ingredient later. The recipe creation unit can also adjust the order of steps of the recipe according to the relevance of the ingredients. As a result, the recipe creation unit can provide an appropriate recipe by adjusting the order of the recipe based on the relevance of the ingredients. Some or all of the above-mentioned processing in the recipe creation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recipe creation unit can input the relevance data of ingredients to the AI ​​and cause the AI ​​to adjust the order of the recipe. As a result, the recipe creation unit can provide an appropriate recipe by adjusting the order of the recipe based on the relevance of the ingredients.

[0046] The price proposal unit can analyze the market price of the ingredient and propose an appropriate price when proposing a price. For example, the price proposal unit obtains the market price of the ingredient in real time and proposes an optimal price. The price proposal unit can also analyze fluctuations in the market price of the ingredient and propose an appropriate price. The price proposal unit can also propose an optimal price taking into account the market price and inventory status of the ingredient. This allows the price proposal unit to propose an optimal price by analyzing the market price. A part or all of the above-mentioned processing in the price proposal unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the price proposal unit can input market price data of the ingredient into an AI and have the AI ​​execute the optimal price proposal. This allows the price proposal unit to propose an optimal price by analyzing the market price.

[0047] The price proposal unit can adjust the price based on the freshness and quality of the ingredients when proposing a price. For example, when the freshness of the ingredients is high, the price proposal unit can propose an appropriate price. Furthermore, when the quality of the ingredients is high, the price proposal unit can also propose an appropriate price. Furthermore, when the expiration date of the ingredients is near, the price proposal unit can propose a discounted price. In this way, the price proposal unit can propose an appropriate price by adjusting the price based on the freshness and quality of the ingredients. A part or all of the above-mentioned processing in the price proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the price proposal unit can input freshness and quality data of the ingredients to AI and cause the AI ​​to adjust the price. In this way, the price proposal unit can propose an appropriate price by adjusting the price based on the freshness and quality of the ingredients.

[0048] The price proposal unit can propose an optimal price by considering the geographical location information of the user when proposing a price. For example, when the user is in a specific area, the price proposal unit proposes a price by considering the market price in that area. In addition, when the user is traveling, the price proposal unit can also propose a price by considering the market price at the travel destination. In addition, when the user is at home, the price proposal unit can also propose a price by considering the market price around the home. In this way, the price proposal unit can propose an optimal price by considering the geographical location information. A part or all of the above-mentioned processing in the price proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the price proposal unit can input the geographical location information of the user to the AI ​​and cause the AI ​​to execute the optimal price proposal. In this way, the price proposal unit can propose an optimal price by considering the geographical location information.

[0049] The price proposal unit can adjust the price by analyzing the user's purchase history when proposing a price. For example, the price proposal unit can propose an appropriate price by referring to the prices of ingredients purchased by the user in the past. The price proposal unit can also predict and propose the price of a specific ingredient from the user's purchase history. The price proposal unit can also analyze the user's purchase history and propose a discount price. In this way, the price proposal unit can propose an appropriate price by analyzing the purchase history. A part or all of the above-mentioned processing in the price proposal unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the price proposal unit can input the user's purchase history data into an AI and cause the AI ​​to adjust the price. In this way, the price proposal unit can propose an appropriate price by analyzing the purchase history.

[0050] The condition setting unit can suggest optimal conditions by referring to the user's past setting history when setting conditions. For example, the condition setting unit automatically displays conditions that the user has set in the past as candidates. The condition setting unit can also preferentially suggest setting methods (detailed, simple, etc.) that the user has used in the past. The condition setting unit can also predict and suggest conditions to be used in a specific time period from the user's past setting history. As a result, the condition setting unit can provide optimal conditions to the user by referring to the past setting history. A part or all of the above-mentioned processing in the condition setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the condition setting unit can input the user's past setting history data to AI and have the AI ​​execute the suggestion of optimal conditions. As a result, the condition setting unit can provide optimal conditions to the user by referring to the past setting history.

[0051] The condition setting unit can propose optimal conditions by considering device information of the user when setting conditions. For example, when the user is using a smartphone, the condition setting unit provides condition settings that match the screen size. In addition, when the user is using a tablet, the condition setting unit can also provide condition settings that are optimized for a large screen. In addition, when the user is using a smartwatch, the condition setting unit can also provide condition settings that are concise and highly visible. As a result, the condition setting unit can provide optimal conditions by considering device information. A part or all of the above-mentioned processing in the condition setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the condition setting unit can input device information of the user to AI and cause AI to execute a proposal of optimal conditions. As a result, the condition setting unit can provide optimal conditions by considering device information.

[0052] The providing unit can select the optimal providing method by referring to the user's past usage history when providing a recipe. For example, the providing unit automatically displays the recipe providing method used by the user in the past as a candidate. The providing unit can also preferentially suggest the providing method (detailed, simple, etc.) used by the user in the past. The providing unit can also predict and suggest the providing method to be used in a specific time period from the user's past usage history. As a result, the providing unit can provide the optimal providing method to the user by referring to the past usage history. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's past usage history data to the AI ​​and cause the AI ​​to select the optimal providing method. As a result, the providing unit can provide the optimal providing method to the user by referring to the past usage history.

[0053] The providing unit can select the optimal providing method in consideration of the device information of the user when providing a recipe. For example, when the user is using a smartphone, the providing unit provides a recipe providing method that matches the screen size. In addition, when the user is using a tablet, the providing unit can also provide a recipe providing method that is optimized for a large screen. In addition, when the user is using a smartwatch, the providing unit can also provide a recipe providing method that is simple and has high visibility. As a result, the providing unit can provide the optimal providing method by considering the device information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the device information of the user to the AI ​​and cause the AI ​​to select the optimal providing method. As a result, the providing unit can provide the optimal providing method by considering the device information.

[0054] The notification unit can select the optimal notification method by referring to the user's past notification history at the time of notification. For example, the notification unit automatically displays the notification methods used by the user in the past as candidates. The notification unit can also preferentially suggest the notification methods (detailed, concise, etc.) used by the user in the past. The notification unit can also predict and suggest the notification method to be used in a specific time period from the user's past notification history. As a result, the notification unit can provide the optimal notification method to the user by referring to the past notification history. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's past notification history data into AI and cause the AI ​​to select the optimal notification method. As a result, the notification unit can provide the optimal notification method to the user by referring to the past notification history.

[0055] The notification unit can select the optimal notification method by considering the device information of the user at the time of notification. For example, when the user is using a smartphone, the notification unit provides a notification method that matches the screen size. In addition, when the user is using a tablet, the notification unit can also provide a notification method that is optimized for a large screen. In addition, when the user is using a smartwatch, the notification unit can also provide a notification method that is simple and highly visible. As a result, the notification unit can provide the optimal notification method by considering the device information. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the device information of the user to the AI ​​and cause the AI ​​to select the optimal notification method. As a result, the notification unit can provide the optimal notification method by considering the device information.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The food loss reduction system can include a nutritional analysis unit that creates recipes taking into account the nutritional value of ingredients. The nutritional analysis unit, for example, analyzes nutritional components such as vitamins, minerals, and proteins of ingredients and proposes balanced recipes. The nutritional analysis unit can also create recipes that enhance specific nutrients. For example, it can propose recipes that use ingredients rich in vitamin C or recipes that combine ingredients that are high in protein. Furthermore, the nutritional analysis unit can customize recipes based on the user's health condition and nutritional needs. In this way, the food loss reduction system can support the user's health by providing recipes that take nutritional value into account.

[0058] The food waste reduction system can include a preference learning unit that learns the user's ingredient preferences and suggests recipes based on the preferences. The preference learning unit, for example, analyzes data on recipes and ingredients previously selected by the user to estimate the user's preferences. The preference learning unit can also improve the accuracy of recipe suggestions based on feedback on recipes evaluated by the user. Furthermore, the preference learning unit can take into account the user's ingredient allergy information and suggest recipes that avoid allergies. In this way, the food waste reduction system can increase satisfaction by providing recipes that match the user's preferences.

[0059] The food loss reduction system can include a storage suggestion unit that suggests a method of storing ingredients when creating a recipe. The storage suggestion unit suggests, for example, a method of storing ingredients in the refrigerator, freezer, or dry storage. The storage suggestion unit can also provide information on the storage period and storage conditions of ingredients. Furthermore, the storage suggestion unit can monitor the storage state of ingredients and suggest an appropriate storage method. In this way, the food loss reduction system can reduce waste of ingredients and enable long-term storage by suggesting a storage method for ingredients.

[0060] The food waste reduction system can include a producer information providing unit that provides information about the producers of ingredients. The producer information providing unit provides information such as the name, location, and production method of the ingredient producer. The producer information providing unit can also provide information about the production process and quality control of the ingredients. Furthermore, the producer information providing unit can also provide traceability information for the ingredients. In this way, the food waste reduction system can reassure consumers and increase the reliability of the ingredients by providing information about the producers of the ingredients.

[0061] The food waste reduction system can include an allergy support unit that creates recipes taking into account allergy information for ingredients. For example, the allergy support unit allows a user to register ingredients to which the user has an allergy, and suggests recipes that avoid those ingredients. The allergy support unit can also suggest alternative ingredients to ingredients to which the user has an allergy. Furthermore, the allergy support unit can create nutritionally balanced recipes that take into account allergy information. In this way, the food waste reduction system can support the health of users by providing recipes that take into account allergy information.

[0062] The food loss reduction system can include a storage monitoring unit that monitors the storage conditions of ingredients and suggests appropriate storage methods. The storage monitoring unit, for example, monitors the temperature, humidity, freshness, etc. of ingredients and suggests optimal storage methods. The storage monitoring unit can also predict the storage period of ingredients and notify when the expiration date is approaching. Furthermore, the storage monitoring unit can also suggest recipes based on the storage conditions of ingredients. In this way, the food loss reduction system can reduce waste of ingredients and enable long-term storage by monitoring the storage conditions of ingredients.

[0063] The process flow of the first embodiment will be briefly described below.

[0064] Step 1: The registration unit registers information about ingredients that are likely to be left over at supermarkets and food stores. Information about ingredients includes, for example, the type of ingredient, the amount, freshness, place of origin, and expiration date. The registration unit can register information about ingredients by manual input, automatic scanning, or by storing the information in a database. Step 2: The recipe creation unit uses AI to analyze the information on the ingredients registered by the registration unit and create a recipe. The recipe may include, for example, a list of ingredients, cooking steps, cooking time, nutritional information, etc. The recipe creation unit analyzes the information on the ingredients using methods such as data mining, statistical analysis, and machine learning algorithms, and creates an optimal recipe. Step 3: The price proposal unit proposes a set selling price of the ingredients based on the recipe created by the recipe creation unit. The set selling price includes, for example, cost calculation, profit margin, discount rate, etc. The price proposal unit proposes the set selling price of the ingredients based on cost calculation. It can also propose a price taking into account the profit margin. It can also propose a price by applying a discount rate. Some or all of the processing in the price proposal unit may be performed using AI, or may be performed without using AI.

[0065] (Example 2) The food waste reduction system according to the embodiment of the present invention is a system in which information on ingredients likely to be left over at supermarkets and food stores is registered, AI automatically creates recipes, and suggests a set selling price for the ingredients. This food waste reduction system reduces local food waste and promotes the effective use of ingredients by registering information on ingredients, analyzing it, creating recipes with AI, and proposing a set selling price. For example, if there are ingredients such as tomatoes, cabbage, and chicken that are likely to be left over at supermarkets and food stores, the information on them is entered into the AI. At this time, detailed information such as the type and amount of ingredients and the expiration date is entered. Next, the AI ​​analyzes the information on the registered ingredients and automatically creates the optimal recipe. For example, when creating a recipe using tomatoes, cabbage, and chicken, the AI ​​combines these ingredients and suggests recipes such as tomato and cabbage soup and chicken stewed in tomato. In addition, more specific recipes can be created by setting conditions such as the amount of the recipe, the genre of the dish, and the cooking time. In addition, the AI ​​also suggests a set selling price for the ingredients based on the created recipe. For example, when a recipe using tomatoes, cabbage, and chicken is proposed, the AI ​​calculates and suggests the price at which these ingredients are sold as a set. This allows consumers to easily purchase ingredients needed for a recipe, and allows supermarkets and grocery stores to efficiently sell surplus ingredients. This system reduces local food waste and leads to effective use of ingredients. For example, food waste can be reduced by providing surplus ingredients at supermarkets and grocery stores to consumers rather than wasting them. Also, consumers can easily purchase ingredients needed for a recipe and prepare delicious dishes at home. In this way, the food waste reduction system can reduce local food waste and promote effective use of ingredients.

[0066] The food waste reduction system according to the embodiment includes a registration unit, a recipe creation unit, and a price proposal unit. The registration unit registers information on ingredients that are likely to be surplus at supermarkets and food stores. The information on ingredients includes, for example, the type, amount, freshness, place of origin, and expiration date of the ingredients, but is not limited to such examples. The registration unit can register the information on ingredients, for example, by manual input, automatic scanning, or a method of saving in a database. The recipe creation unit uses AI to analyze the information on ingredients registered by the registration unit and create a recipe. The recipe includes, for example, an ingredient list, cooking steps, cooking time, and nutritional information, but is not limited to such examples. The recipe creation unit analyzes the information on ingredients using methods such as data mining, statistical analysis, and machine learning algorithms, and creates an optimal recipe. The price proposal unit proposes a set sales price for ingredients based on the recipe created by the recipe creation unit. The set sales price includes, for example, cost calculation, profit margin, discount rate, and the like, but is not limited to such examples. The price proposal unit proposes a set sales price for ingredients based on, for example, cost calculation. The price proposal unit can also propose a price taking into account a profit margin. The price proposal unit can also propose a price by applying a discount rate. As a result, the food waste reduction system according to the embodiment can register information on ingredients that are likely to be left over at supermarkets and grocery stores, have AI automatically create recipes, and propose a set selling price for the ingredients. Some or all of the above-mentioned processing in the price proposal unit can be performed, for example, using AI, or can be performed without using AI. For example, the price proposal unit can propose a price using an AI model that inputs a recipe created by the recipe creation unit and outputs a set selling price. As a result, the food waste reduction system can reduce local food waste and promote the effective use of ingredients.

[0067] The registration unit registers information on ingredients that are likely to be left over at supermarkets and food stores. The information on ingredients includes, for example, the type, amount, freshness, place of origin, and expiration date of the ingredient, but is not limited to such examples. The registration unit can register the information on ingredients by, for example, manual input, automatic scanning, or a method of saving to a database. Specifically, in the case of manual input, a store clerk uses a dedicated terminal or application to input the information on ingredients one by one. In the case of automatic scanning, a barcode reader or a two-dimensional code (e.g., QR code) reader is used to read the code printed on the packaging of the ingredients and automatically obtain the information. Furthermore, it is also possible to read the information on ingredients without contact using an RFID tag. As a method of saving to a database, a cloud-based database can be used to centrally manage information from multiple stores and update it in real time. This allows the registration unit to efficiently and accurately collect information on ingredients and build the foundation of a food loss reduction system. The registration unit can also periodically update the information on ingredients and respond to changes in freshness and expiration date. For example, an alert can be issued for ingredients that are approaching their expiration date, allowing measures to be taken early. This enables the registration unit to minimize waste of ingredients and achieve efficient inventory management.

[0068] The recipe creation unit uses AI to analyze the information of the ingredients registered by the registration unit and create a recipe. The recipe includes, for example, an ingredient list, cooking steps, cooking time, nutritional information, etc., but is not limited to such examples. The recipe creation unit analyzes the information of the ingredients using methods such as data mining, statistical analysis, and machine learning algorithms, and creates an optimal recipe. Specifically, the AI ​​finds the optimal combination based on information such as the type and amount of the registered ingredients, freshness, and expiration date, and generates a balanced recipe. For example, the AI ​​can use a machine learning algorithm to learn past recipe data and user preference data and propose new recipes. Furthermore, the AI ​​can also create recipes for providing healthy meals by taking into account nutritional information. For example, a nutritionally balanced recipe is provided by preferentially using ingredients that contain a large amount of a specific nutrient. In addition, the recipe creation unit can propose recipes that meet the needs of the user by taking into account cooking time and difficulty. For example, a recipe that can be cooked in a short time is provided to a busy user, and a recipe that can be made with simple steps is provided to a beginner cook. In this way, the recipe creation unit can respond to the diverse needs of users and promote the effective use of ingredients.

[0069] The price proposal unit proposes a set selling price of ingredients based on the recipe created by the recipe creation unit. The set selling price includes, but is not limited to, cost calculation, profit margin, discount rate, etc., for example. The price proposal unit proposes a set selling price of ingredients based on cost calculation, for example. Specifically, the total cost is calculated taking into account the purchase price of each ingredient, transportation cost, storage cost, etc., and an appropriate selling price is set based on the total cost. The price proposal unit can also propose a price taking into account the profit margin. For example, in order to ensure a certain profit margin, an appropriate margin is added to the cost and the selling price is set. Furthermore, the price proposal unit can also propose a price by applying a discount rate. For example, for ingredients whose expiration date is approaching, a high discount rate can be set and the ingredients can be sold early to prevent food loss. A part or all of the above-mentioned processing in the price proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the price proposal unit can propose a price using an AI model that inputs the recipe created by the recipe creation unit and outputs a set selling price. The AI ​​model can learn from past sales data and market trends to predict optimal prices, allowing the price proposal department to set prices efficiently and effectively and promote sales of ingredients.

[0070] The food loss reduction system includes a condition setting unit that sets the recipe quantity, food genre, and cooking time conditions. The condition setting unit sets conditions such as the recipe quantity, food genre, and cooking time. The quantity includes, for example, units such as grams, cups, and number, but is not limited to such examples. The food genre includes, for example, classifications such as Japanese food, Western food, and Chinese food, but is not limited to such examples. The cooking time includes, for example, preparation time, cooking time, and total time, but is not limited to such examples. The condition setting unit sets the recipe quantity, for example, by inputting a desired quantity by the user. The condition setting unit can also set the recipe genre by selecting a desired food genre by the user. The condition setting unit can also set the cooking time of the recipe by inputting a desired cooking time by the user. In this way, the condition setting unit can provide a recipe that meets the needs of the user. A part or all of the above-mentioned processing in the condition setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the condition setting unit can input the user's input as a prompt to the AI, and the AI ​​can create a recipe based on the conditions. This allows the condition setting unit to provide recipes that meet the needs of the user.

[0071] The food loss reduction system includes a providing unit that provides the created recipe to the user. The providing unit provides the created recipe to the user. The providing method includes, for example, email, app notification, printed matter, and the like, but is not limited to such examples. For example, the providing unit transmits the created recipe to the user by email. The providing unit can also provide the created recipe to the user by app notification. The providing unit can also provide the created recipe as a printed matter. As a result, the providing unit can promote effective use of ingredients by providing the recipe to the user. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs the created recipe to the AI, and the AI ​​can select the optimal providing method for the user. As a result, the providing unit can promote effective use of ingredients by providing the recipe to the user.

[0072] The food waste reduction system includes a notification unit that notifies the user of the set sales price of the ingredients. The notification unit notifies the user of the set sales price of the ingredients. Notification methods include, for example, push notification, SMS, email, and the like, but are not limited to such examples. For example, the notification unit notifies the user of the set sales price of the ingredients by push notification. The notification unit can also notify the user of the set sales price of the ingredients by SMS. The notification unit can also notify the user of the set sales price of the ingredients by email. In this way, the notification unit can promote the purchase of ingredients by notifying the user of the set sales price. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit inputs the set sales price of the ingredients into AI, and the AI ​​can select the optimal notification method for the user. In this way, the notification unit can promote the purchase of ingredients by notifying the user of the set sales price.

[0073] The recipe creation unit can create recipes using tomatoes, cabbage, or chicken. The recipe creation unit creates recipes using tomatoes, cabbage, or chicken, for example. Tomatoes include, for example, information such as variety, place of origin, and freshness, but are not limited to such examples. Cabbage includes, for example, information such as variety, place of origin, and freshness, but are not limited to such examples. Chicken includes, for example, information such as variety, place of origin, and freshness, but are not limited to such examples. The recipe creation unit creates, for example, tomato soup or tomato salad as recipes using tomatoes. The recipe creation unit can also create cabbage soup or stir-fried cabbage as recipes using cabbage. The recipe creation unit can also create chicken stewed in tomato sauce or chicken wrapped in cabbage as recipes using chicken. In this way, the recipe creation unit can promote effective use of ingredients by creating recipes using specific ingredients. A part or all of the above-mentioned processing in the recipe creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the recipe creation unit can input information about tomatoes, cabbage, or chicken into the AI, which can then create the optimal recipe. This allows the recipe creation unit to promote the effective use of ingredients by creating recipes that use specific ingredients.

[0074] The price proposal unit can propose a set selling price of ingredients based on the created recipe. The price proposal unit, for example, proposes a set selling price of ingredients based on the created recipe. The set selling price includes, for example, cost calculation, profit margin, discount rate, etc., but is not limited to such examples. The price proposal unit, for example, proposes a set selling price of ingredients based on cost calculation. The price proposal unit can also propose a price taking into account a profit margin. The price proposal unit can also propose a price by applying a discount rate. In this way, the price proposal unit can promote sales of ingredients by proposing a price based on the recipe. A part or all of the above-mentioned processing in the price proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the price proposal unit can input the created recipe into AI, and the AI ​​can propose an optimal set selling price. In this way, the price proposal unit can promote sales of ingredients by proposing a price based on the recipe.

[0075] The registration unit can estimate the user's emotion and adjust the timing of registering the ingredient information based on the estimated emotion of the user. For example, when the user is stressed, the registration unit can simplify the registration procedure and quickly register the ingredient information. In addition, when the user is relaxed, the registration unit can provide detailed registration options to allow the user to carefully input the ingredient information. In addition, when the user is in a hurry, the registration unit can prioritize voice input and quickly register the ingredient information. In this way, the registration unit can adjust the registration timing according to the user's emotion and register the ingredient information at a more appropriate timing. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the registration unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the registration unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the timing of registering the ingredient information. This allows the registration unit to adjust the registration timing according to the user's emotions, thereby enabling the ingredient information to be registered at a more appropriate timing.

[0076] The registration unit can analyze the user's past registration history and select the optimal registration method when registering ingredients. For example, the registration unit automatically displays ingredients that the user has frequently registered in the past as candidates. The registration unit can also preferentially suggest registration methods (such as voice and text) that the user has used in the past. The registration unit can also predict and suggest ingredients to be registered in a specific time period from the user's past registration history. In this way, the registration unit can provide the user with the optimal registration method by analyzing the past registration history. A part or all of the above-mentioned processing in the registration unit may be performed, for example, using AI, or may be performed without using AI. For example, the registration unit can input the user's past registration history into AI and cause AI to select the optimal registration method. In this way, the registration unit can provide the user with the optimal registration method by analyzing the past registration history.

[0077] The registration unit can filter the registration information based on the freshness and quality of the ingredients when registering the ingredients. For example, if the freshness of the ingredients is low, the registration unit suggests refraining from registering the ingredients. In addition, the registration unit can also suggest that the ingredients be registered preferentially if the quality of the ingredients is high. In addition, the registration unit can also suggest that the ingredients be registered as soon as possible if the expiration date of the ingredients is approaching. In this way, the registration unit can register appropriate ingredient information by filtering the registration information based on the freshness and quality of the ingredients. A part or all of the above-mentioned processing in the registration unit may be performed, for example, using AI, or may be performed without using AI. For example, the registration unit can input data on the freshness and quality of the ingredients to the AI ​​and cause the AI ​​to filter the registration information. In this way, the registration unit can register appropriate ingredient information by filtering the registration information based on the freshness and quality of the ingredients.

[0078] The registration unit can estimate the user's emotion and determine the priority of the ingredient information to be registered based on the estimated emotion of the user. For example, when the user is stressed, the registration unit prioritizes ingredient information that can be easily registered. In addition, when the user is relaxed, the registration unit can also prioritize registering detailed ingredient information. In addition, when the user is in a hurry, the registration unit can also prioritize registering important ingredient information. In this way, the registration unit can prioritize registering more appropriate information by determining the priority of ingredient information according to the emotion of the user. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the registration unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the registration unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of the ingredient information. In this way, the registration unit can prioritize registering more appropriate information by determining the priority of ingredient information according to the emotion of the user.

[0079] The registration unit can preferentially register highly relevant ingredient information in consideration of the geographical location information of the user when registering ingredients. For example, when the user is in a specific area, the registration unit preferentially registers ingredient information that is often used in that area. In addition, when the user is traveling, the registration unit can also preferentially register ingredient information available in the travel destination area. In addition, when the user is at home, the registration unit can also preferentially register ingredient information available around the home. In this way, the registration unit can preferentially register highly relevant ingredient information by considering the geographical location information. A part or all of the above-mentioned processing in the registration unit may be performed, for example, using AI, or may be performed without using AI. For example, the registration unit can input the geographical location information of the user to the AI ​​and cause the AI ​​to select highly relevant ingredient information. In this way, the registration unit can preferentially register highly relevant ingredient information by considering the geographical location information.

[0080] The registration unit can analyze the user's social media activity and register related ingredient information when registering ingredients. For example, the registration unit automatically registers ingredient information shared by the user on social media. The registration unit can also preferentially register ingredient information of cooking accounts that the user follows on social media. The registration unit can also automatically register ingredient information that the user has "liked" on social media. This allows the registration unit to automatically register related ingredient information by analyzing social media activity. A part or all of the above-mentioned processing in the registration unit may be performed, for example, using AI, or may be performed without using AI. For example, the registration unit can input the user's social media activity data into AI and cause AI to register related ingredient information. This allows the registration unit to automatically register related ingredient information by analyzing social media activity.

[0081] The recipe creation unit can estimate the user's emotion and adjust the recipe expression method based on the estimated user's emotion. For example, the recipe creation unit provides a detailed recipe description when the user is relaxed. Also, the recipe creation unit can provide a concise recipe description when the user is in a hurry. Also, the recipe creation unit can provide a visually attractive recipe description when the user is excited. As a result, the recipe creation unit can provide a more appropriate recipe by adjusting the recipe expression method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the recipe creation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recipe creation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the recipe expression method. As a result, the recipe creation unit can provide a more appropriate recipe by adjusting the recipe expression method according to the user's emotion.

[0082] The recipe creation unit can adjust the level of detail of the recipe based on the importance of the ingredients when creating the recipe. The recipe creation unit, for example, provides detailed cooking instructions for main ingredients. The recipe creation unit can also provide concise cooking instructions for supplementary ingredients. The recipe creation unit can also adjust the number of steps of the recipe according to the importance of the ingredients. As a result, the recipe creation unit can provide an appropriate recipe by adjusting the level of detail of the recipe based on the importance of the ingredients. Some or all of the above-mentioned processing in the recipe creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the recipe creation unit can input the importance data of ingredients to the AI ​​and cause the AI ​​to adjust the level of detail of the recipe. As a result, the recipe creation unit can provide an appropriate recipe by adjusting the level of detail of the recipe based on the importance of the ingredients.

[0083] The recipe creation unit can apply different recipe algorithms depending on the category of ingredients when creating a recipe. For example, the recipe creation unit applies a recipe algorithm dedicated to vegetables to ingredients in the vegetable category. The recipe creation unit can also apply a recipe algorithm dedicated to meat to ingredients in the meat category. The recipe creation unit can also apply a recipe algorithm dedicated to seafood to ingredients in the seafood category. In this way, the recipe creation unit can provide an appropriate recipe by applying different recipe algorithms depending on the category of ingredients. Some or all of the above-mentioned processes in the recipe creation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recipe creation unit can input ingredient category data to the AI ​​and cause the AI ​​to apply different recipe algorithms. In this way, the recipe creation unit can provide an appropriate recipe by applying different recipe algorithms depending on the category of ingredients.

[0084] The recipe creation unit can estimate the user's emotion and adjust the length of the recipe based on the estimated user's emotion. For example, when the user is in a hurry, the recipe creation unit provides a short recipe that is to the point. Also, when the user is relaxed, the recipe creation unit can provide a longer recipe that includes detailed explanations. Also, when the user is excited, the recipe creation unit can provide a recipe with a visually stimulating effect. This allows the recipe creation unit to provide a more appropriate recipe by adjusting the length of the recipe according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the recipe creation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recipe creation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the length of the recipe. This allows the recipe creation unit to provide a more appropriate recipe by adjusting the length of the recipe according to the user's emotion.

[0085] The recipe creation unit can determine the priority of recipes based on the time when ingredients are obtained when creating a recipe. The recipe creation unit, for example, provides a recipe that prioritizes the use of seasonal ingredients. The recipe creation unit can also provide a recipe that prioritizes the use of ingredients that are close to their expiration date. The recipe creation unit can also provide a recipe that prioritizes the use of seasonal ingredients. In this way, the recipe creation unit can provide an appropriate recipe by determining the priority of recipes based on the time when ingredients are obtained. A part or all of the above-mentioned processing in the recipe creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the recipe creation unit can input data on the time when ingredients are obtained to AI and cause AI to determine the priority of recipes. In this way, the recipe creation unit can provide an appropriate recipe by determining the priority of recipes based on the time when ingredients are obtained.

[0086] The recipe creation unit can adjust the order of the recipe based on the relevance of ingredients when creating a recipe. For example, the recipe creation unit provides a recipe that uses a main ingredient first. The recipe creation unit can also provide a recipe that uses a supplementary ingredient later. The recipe creation unit can also adjust the order of steps of the recipe according to the relevance of the ingredients. As a result, the recipe creation unit can provide an appropriate recipe by adjusting the order of the recipe based on the relevance of the ingredients. Some or all of the above-mentioned processing in the recipe creation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recipe creation unit can input the relevance data of ingredients to the AI ​​and cause the AI ​​to adjust the order of the recipe. As a result, the recipe creation unit can provide an appropriate recipe by adjusting the order of the recipe based on the relevance of the ingredients.

[0087] The price proposal unit can estimate the user's emotion and adjust the price proposal method based on the estimated user's emotion. For example, the price proposal unit can provide a detailed price proposal when the user is relaxed. Also, the price proposal unit can provide a concise price proposal when the user is in a hurry. Also, the price proposal unit can provide a visually attractive price proposal when the user is excited. As a result, the price proposal unit can provide a more appropriate price proposal by adjusting the price proposal method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the price proposal unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the price proposal unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the price proposal method. As a result, the price proposal unit can provide a more appropriate price proposal by adjusting the price proposal method according to the user's emotion.

[0088] The price proposal unit can analyze the market price of the ingredient and propose an appropriate price when proposing a price. For example, the price proposal unit obtains the market price of the ingredient in real time and proposes an optimal price. The price proposal unit can also analyze fluctuations in the market price of the ingredient and propose an appropriate price. The price proposal unit can also propose an optimal price taking into account the market price and inventory status of the ingredient. This allows the price proposal unit to propose an optimal price by analyzing the market price. A part or all of the above-mentioned processing in the price proposal unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the price proposal unit can input market price data of the ingredient into an AI and have the AI ​​execute the optimal price proposal. This allows the price proposal unit to propose an optimal price by analyzing the market price.

[0089] The price proposal unit can adjust the price based on the freshness and quality of the ingredients when proposing a price. For example, when the freshness of the ingredients is high, the price proposal unit can propose an appropriate price. Furthermore, when the quality of the ingredients is high, the price proposal unit can also propose an appropriate price. Furthermore, when the expiration date of the ingredients is near, the price proposal unit can propose a discounted price. In this way, the price proposal unit can propose an appropriate price by adjusting the price based on the freshness and quality of the ingredients. A part or all of the above-mentioned processing in the price proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the price proposal unit can input freshness and quality data of the ingredients to AI and cause the AI ​​to adjust the price. In this way, the price proposal unit can propose an appropriate price by adjusting the price based on the freshness and quality of the ingredients.

[0090] The price proposal unit can estimate the user's emotion and determine the priority of price proposals based on the estimated user's emotion. For example, the price proposal unit prioritizes detailed price proposals when the user is relaxed. Also, the price proposal unit can prioritize concise price proposals when the user is in a hurry. Also, the price proposal unit can prioritize visually attractive price proposals when the user is excited. This allows the price proposal unit to provide more appropriate price proposals by determining the priority of price proposals according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the price proposal unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the price proposal unit can input the user's emotion data into the generation AI and cause the generation AI to execute the determination of the priority of price proposals. This allows the price proposal unit to provide more appropriate price proposals by determining the priority of price proposals according to the user's emotion.

[0091] The price proposal unit can propose an optimal price by considering the geographical location information of the user when proposing a price. For example, when the user is in a specific area, the price proposal unit proposes a price by considering the market price in that area. In addition, when the user is traveling, the price proposal unit can also propose a price by considering the market price at the travel destination. In addition, when the user is at home, the price proposal unit can also propose a price by considering the market price around the home. In this way, the price proposal unit can propose an optimal price by considering the geographical location information. A part or all of the above-mentioned processing in the price proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the price proposal unit can input the geographical location information of the user to the AI ​​and cause the AI ​​to execute the optimal price proposal. In this way, the price proposal unit can propose an optimal price by considering the geographical location information.

[0092] The price proposal unit can adjust the price by analyzing the user's purchase history when proposing a price. For example, the price proposal unit can propose an appropriate price by referring to the prices of ingredients purchased by the user in the past. The price proposal unit can also predict and propose the price of a specific ingredient from the user's purchase history. The price proposal unit can also analyze the user's purchase history and propose a discount price. In this way, the price proposal unit can propose an appropriate price by analyzing the purchase history. A part or all of the above-mentioned processing in the price proposal unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the price proposal unit can input the user's purchase history data into an AI and cause the AI ​​to adjust the price. In this way, the price proposal unit can propose an appropriate price by analyzing the purchase history.

[0093] The condition setting unit can estimate the user's emotion and adjust the condition setting method based on the estimated user's emotion. For example, the condition setting unit can provide detailed condition setting options when the user is relaxed. The condition setting unit can also provide concise condition setting options when the user is in a hurry. The condition setting unit can also provide visually attractive condition setting options when the user is excited. As a result, the condition setting unit can provide more appropriate condition setting by adjusting the condition setting method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the condition setting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the condition setting unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the condition setting method. As a result, the condition setting unit can provide more appropriate condition setting by adjusting the condition setting method according to the user's emotion.

[0094] The condition setting unit can suggest optimal conditions by referring to the user's past setting history when setting conditions. For example, the condition setting unit automatically displays conditions that the user has set in the past as candidates. The condition setting unit can also preferentially suggest setting methods (detailed, simple, etc.) that the user has used in the past. The condition setting unit can also predict and suggest conditions to be used in a specific time period from the user's past setting history. As a result, the condition setting unit can provide optimal conditions to the user by referring to the past setting history. A part or all of the above-mentioned processing in the condition setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the condition setting unit can input the user's past setting history data to AI and have the AI ​​execute the suggestion of optimal conditions. As a result, the condition setting unit can provide optimal conditions to the user by referring to the past setting history.

[0095] The condition setting unit can estimate the user's emotion and determine the priority of the condition settings based on the estimated user's emotion. For example, when the user is relaxed, the condition setting unit prioritizes detailed condition settings. Also, when the user is in a hurry, the condition setting unit can also prioritize concise condition settings. Also, when the user is excited, the condition setting unit can also prioritize visually attractive condition settings. As a result, the condition setting unit can provide more appropriate condition settings by determining the priority of the condition settings according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the condition setting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the condition setting unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of the condition settings. As a result, the condition setting unit can provide more appropriate condition settings by determining the priority of the condition settings according to the user's emotion.

[0096] The condition setting unit can propose optimal conditions by considering device information of the user when setting conditions. For example, when the user is using a smartphone, the condition setting unit provides condition settings that match the screen size. In addition, when the user is using a tablet, the condition setting unit can also provide condition settings that are optimized for a large screen. In addition, when the user is using a smartwatch, the condition setting unit can also provide condition settings that are concise and highly visible. As a result, the condition setting unit can provide optimal conditions by considering device information. A part or all of the above-mentioned processing in the condition setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the condition setting unit can input device information of the user to AI and cause AI to execute a proposal of optimal conditions. As a result, the condition setting unit can provide optimal conditions by considering device information.

[0097] The providing unit can estimate the user's emotion and adjust the recipe providing method based on the estimated user's emotion. For example, the providing unit provides a detailed recipe description when the user is relaxed. Also, the providing unit can provide a concise recipe description when the user is in a hurry. Also, the providing unit can provide a visually attractive recipe description when the user is excited. As a result, the providing unit can provide a more appropriate recipe by adjusting the recipe providing method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the recipe providing method. As a result, the providing unit can provide a more appropriate recipe by adjusting the recipe providing method according to the user's emotion.

[0098] The providing unit can select the optimal providing method by referring to the user's past usage history when providing a recipe. For example, the providing unit automatically displays the recipe providing method used by the user in the past as a candidate. The providing unit can also preferentially suggest the providing method (detailed, simple, etc.) used by the user in the past. The providing unit can also predict and suggest the providing method to be used in a specific time period from the user's past usage history. As a result, the providing unit can provide the optimal providing method to the user by referring to the past usage history. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's past usage history data to the AI ​​and cause the AI ​​to select the optimal providing method. As a result, the providing unit can provide the optimal providing method to the user by referring to the past usage history.

[0099] The providing unit can estimate the user's emotion and adjust the recipe serving order based on the estimated user's emotion. For example, when the user is relaxed, the providing unit can provide a detailed recipe description with priority. Also, when the user is in a hurry, the providing unit can provide a concise recipe description with priority. Also, when the user is excited, the providing unit can provide a visually attractive recipe description with priority. As a result, the providing unit can provide a more appropriate recipe by adjusting the recipe serving order according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the recipe serving order. As a result, the providing unit can provide a more appropriate recipe by adjusting the recipe serving order according to the user's emotion.

[0100] The providing unit can select the optimal providing method in consideration of the device information of the user when providing a recipe. For example, when the user is using a smartphone, the providing unit provides a recipe providing method that matches the screen size. In addition, when the user is using a tablet, the providing unit can also provide a recipe providing method that is optimized for a large screen. In addition, when the user is using a smartwatch, the providing unit can also provide a recipe providing method that is simple and has high visibility. As a result, the providing unit can provide the optimal providing method by considering the device information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the device information of the user to the AI ​​and cause the AI ​​to select the optimal providing method. As a result, the providing unit can provide the optimal providing method by considering the device information.

[0101] The notification unit can estimate the user's emotion and adjust the notification method based on the estimated user's emotion. For example, the notification unit provides a detailed notification when the user is relaxed. The notification unit can also provide a concise notification when the user is in a hurry. The notification unit can also provide a visually attractive notification when the user is excited. This allows the notification unit to provide a more appropriate notification by adjusting the notification method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the notification method. This allows the notification unit to provide a more appropriate notification by adjusting the notification method according to the user's emotion.

[0102] The notification unit can select the optimal notification method by referring to the user's past notification history at the time of notification. For example, the notification unit automatically displays the notification methods used by the user in the past as candidates. The notification unit can also preferentially suggest the notification methods (detailed, concise, etc.) used by the user in the past. The notification unit can also predict and suggest the notification method to be used in a specific time period from the user's past notification history. As a result, the notification unit can provide the optimal notification method to the user by referring to the past notification history. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's past notification history data into AI and cause the AI ​​to select the optimal notification method. As a result, the notification unit can provide the optimal notification method to the user by referring to the past notification history.

[0103] The notification unit can estimate the user's emotion and determine the priority of notifications based on the estimated user's emotion. For example, when the user is relaxed, the notification unit prioritizes detailed notifications. In addition, when the user is in a hurry, the notification unit can also prioritize brief notifications. In addition, when the user is excited, the notification unit can also prioritize visually attractive notifications. As a result, the notification unit can provide more appropriate notifications by determining the priority of notifications according to the user's emotion. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of notifications. As a result, the notification unit can provide more appropriate notifications by determining the priority of notifications according to the user's emotion.

[0104] The notification unit can select the optimal notification method by considering the device information of the user at the time of notification. For example, when the user is using a smartphone, the notification unit provides a notification method that matches the screen size. In addition, when the user is using a tablet, the notification unit can also provide a notification method that is optimized for a large screen. In addition, when the user is using a smartwatch, the notification unit can also provide a notification method that is simple and highly visible. As a result, the notification unit can provide the optimal notification method by considering the device information. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the device information of the user to the AI ​​and cause the AI ​​to select the optimal notification method. As a result, the notification unit can provide the optimal notification method by considering the device information.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The food loss reduction system can include a nutritional analysis unit that creates recipes taking into account the nutritional value of ingredients. The nutritional analysis unit, for example, analyzes nutritional components such as vitamins, minerals, and proteins of ingredients and proposes balanced recipes. The nutritional analysis unit can also create recipes that enhance specific nutrients. For example, it can propose recipes that use ingredients rich in vitamin C or recipes that combine ingredients that are high in protein. Furthermore, the nutritional analysis unit can customize recipes based on the user's health condition and nutritional needs. In this way, the food loss reduction system can support the user's health by providing recipes that take nutritional value into account.

[0107] The food waste reduction system can include a preference learning unit that learns the user's ingredient preferences and suggests recipes based on the preferences. The preference learning unit, for example, analyzes data on recipes and ingredients previously selected by the user to estimate the user's preferences. The preference learning unit can also improve the accuracy of recipe suggestions based on feedback on recipes evaluated by the user. Furthermore, the preference learning unit can take into account the user's ingredient allergy information and suggest recipes that avoid allergies. In this way, the food waste reduction system can increase satisfaction by providing recipes that match the user's preferences.

[0108] The food loss reduction system can include a storage suggestion unit that suggests a method of storing ingredients when creating a recipe. The storage suggestion unit suggests, for example, a method of storing ingredients in the refrigerator, freezer, or dry storage. The storage suggestion unit can also provide information on the storage period and storage conditions of ingredients. Furthermore, the storage suggestion unit can monitor the storage state of ingredients and suggest an appropriate storage method. In this way, the food loss reduction system can reduce waste of ingredients and enable long-term storage by suggesting a storage method for ingredients.

[0109] The food waste reduction system can estimate the user's emotions and adjust the difficulty of the recipe based on the estimated user's emotions. For example, if the user is feeling stressed, a simple and easy recipe can be suggested. Also, if the user is relaxed, a recipe that can be made over time can be suggested. Furthermore, if the user is excited, a challenging recipe can be suggested. In this way, the food waste reduction system can provide more appropriate recipes by adjusting the difficulty of the recipe according to the user's emotions.

[0110] The food waste reduction system can estimate the user's emotions and adjust the timing of providing a recipe based on the estimated user's emotions. For example, if the user is busy, a recipe that can be made in a short time can be provided. Also, if the user is relaxed, a recipe that can be made over time can be provided. Furthermore, if the user is in a hurry, a recipe that can be made instantly can be provided. In this way, the food waste reduction system can provide more appropriate recipes by adjusting the timing of providing a recipe according to the user's emotions.

[0111] The food waste reduction system can include a producer information providing unit that provides information about the producers of ingredients. The producer information providing unit provides information such as the name, location, and production method of the ingredient producer. The producer information providing unit can also provide information about the production process and quality control of the ingredients. Furthermore, the producer information providing unit can also provide traceability information for the ingredients. In this way, the food waste reduction system can reassure consumers and increase the reliability of the ingredients by providing information about the producers of the ingredients.

[0112] The food waste reduction system can estimate the user's emotions and provide recipe customization options based on the estimated user's emotions. For example, if the user is relaxed, detailed customization options can be provided. Also, if the user is in a hurry, brief customization options can be provided. Furthermore, if the user is excited, visually appealing customization options can be provided. In this way, the food waste reduction system can provide more appropriate recipes by providing recipe customization options according to the user's emotions.

[0113] The food waste reduction system can include an allergy support unit that creates recipes taking into account allergy information for ingredients. For example, the allergy support unit allows a user to register ingredients to which the user has an allergy, and suggests recipes that avoid those ingredients. The allergy support unit can also suggest alternative ingredients to ingredients to which the user has an allergy. Furthermore, the allergy support unit can create nutritionally balanced recipes that take into account allergy information. In this way, the food waste reduction system can support the health of users by providing recipes that take into account allergy information.

[0114] The food waste reduction system can estimate the user's emotions and adjust the presentation method of the recipe based on the estimated user's emotions. For example, if the user is relaxed, a detailed recipe description can be provided. If the user is in a hurry, a concise recipe description can be provided. Furthermore, if the user is excited, a visually appealing recipe description can be provided. In this way, the food waste reduction system can provide a more appropriate recipe by adjusting the presentation method of the recipe according to the user's emotions.

[0115] The food loss reduction system can include a storage monitoring unit that monitors the storage conditions of ingredients and suggests appropriate storage methods. The storage monitoring unit, for example, monitors the temperature, humidity, freshness, etc. of ingredients and suggests optimal storage methods. The storage monitoring unit can also predict the storage period of ingredients and notify when the expiration date is approaching. Furthermore, the storage monitoring unit can also suggest recipes based on the storage conditions of ingredients. In this way, the food loss reduction system can reduce waste of ingredients and enable long-term storage by monitoring the storage conditions of ingredients.

[0116] The process flow of the second embodiment will be briefly described below.

[0117] Step 1: The registration unit registers information about ingredients that are likely to be left over at supermarkets and food stores. Information about ingredients includes, for example, the type of ingredient, the amount, freshness, place of origin, and expiration date. The registration unit can register information about ingredients by manual input, automatic scanning, or by storing the information in a database. Step 2: The recipe creation unit uses AI to analyze the information on the ingredients registered by the registration unit and create a recipe. The recipe may include, for example, a list of ingredients, cooking steps, cooking time, nutritional information, etc. The recipe creation unit analyzes the information on the ingredients using methods such as data mining, statistical analysis, and machine learning algorithms, and creates an optimal recipe. Step 3: The price proposal unit proposes a set selling price of the ingredients based on the recipe created by the recipe creation unit. The set selling price includes, for example, cost calculation, profit margin, discount rate, etc. The price proposal unit proposes the set selling price of the ingredients based on cost calculation. It can also propose a price taking into account the profit margin. It can also propose a price by applying a discount rate. Some or all of the processing in the price proposal unit may be performed using AI, or may be performed without using AI.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0120] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0121] Each of the multiple elements including the above-mentioned registration unit, recipe creation unit, price proposal unit, condition setting unit, provision unit, and notification unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the smart device 14 and registers information on ingredients. The recipe creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a recipe by analyzing the registered ingredient information. The price proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes a set sales price of ingredients based on the created recipe. The condition setting unit is realized by the control unit 46A of the smart device 14 and sets recipe conditions according to the needs of the user. The provision unit is realized by the control unit 46A of the smart device 14 and provides the created recipe to the user. The notification unit is realized by the control unit 46A of the smart device 14 and notifies the user of the set sales price of ingredients. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 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.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] 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.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0129] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0132] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0136] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0137] Each of the multiple elements including the above-mentioned registration unit, recipe creation unit, price proposal unit, condition setting unit, provision unit, and notification unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the smart glasses 214 and registers information on ingredients. The recipe creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a recipe by analyzing the registered ingredient information. The price proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes a set sales price of ingredients based on the created recipe. The condition setting unit is realized by the control unit 46A of the smart glasses 214 and sets recipe conditions according to the needs of the user. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides the created recipe to the user. The notification unit is realized by the control unit 46A of the smart glasses 214 and notifies the user of the set sales price of ingredients. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 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.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] 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.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0145] 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.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0148] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0152] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0153] Each of the multiple elements including the above-mentioned registration unit, recipe creation unit, price proposal unit, condition setting unit, provision unit, and notification unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the headset type terminal 314 and registers information on ingredients. The recipe creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a recipe by analyzing the registered ingredient information. The price proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes a set sales price of ingredients based on the created recipe. The condition setting unit is realized by the control unit 46A of the headset type terminal 314 and sets recipe conditions according to the needs of the user. The provision unit is realized by the control unit 46A of the headset type terminal 314 and provides the created recipe to the user. The notification unit is realized by the control unit 46A of the headset type terminal 314 and notifies the user of the set sales price of ingredients. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0156] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] 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.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0161] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0162] 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.

[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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0165] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0167] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0169] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0170] Each of the multiple elements including the above-mentioned registration unit, recipe creation unit, price proposal unit, condition setting unit, supply unit, and notification unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the robot 414 and registers information on ingredients. The recipe creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a recipe by analyzing the registered ingredient information. The price proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes a set sales price of ingredients based on the created recipe. The condition setting unit is realized by the control unit 46A of the robot 414 and sets recipe conditions according to the needs of the user. The supply unit is realized by the control unit 46A of the robot 414 and provides the created recipe to the user. The notification unit is realized by the control unit 46A of the robot 414 and notifies the user of the set sales price of ingredients. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0171] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0172] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0173] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0174] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0175] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0176] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0177] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0178] 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.

[0179] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0180] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0181] 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.

[0182] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0183] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0184] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0185] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0186] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0187] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0188] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0189] (Appendix 1) A registration unit for registering information on ingredients; a recipe creation unit that analyzes the information registered by the registration unit and creates a recipe; a price proposal unit that proposes a set sales price based on the recipe created by the recipe creation unit. A system characterized by: (Appendix 2) Equipped with a condition setting section for setting recipe quantities, food genre, and cooking time conditions 2. The system of claim 1, (Appendix 3) A providing unit is provided to provide the created recipe to the user. 2. The system of claim 1, (Appendix 4) Equipped with a notification unit that notifies the user of the set sales price of ingredients 2. The system of claim 1, (Appendix 5) The recipe creation unit includes: Create recipes with tomatoes, cabbage or chicken 2. The system of claim 1, (Appendix 6) The price proposal unit, Propose a set price for ingredients based on the recipe you created 2. The system of claim 1, (Appendix 7) The registration unit is To estimate a user's emotion and adjust the timing of registering ingredient information based on the estimated user's emotion. 2. The system of claim 1, (Appendix 8) The registration unit is When registering ingredients, analyze the user's past registration history and select the appropriate registration method. 2. The system of claim 1, (Appendix 9) The registration unit is When registering ingredients, filter entries based on freshness or quality of ingredients 2. The system of claim 1, (Appendix 10) The registration unit is The user's emotions are estimated, and the priority of the food information to be registered is determined based on the estimated user's emotions. 2. The system of claim 1, (Appendix 11) The registration unit is When registering ingredients, the system takes into account the user's geographic location information and prioritizes registering ingredients information that is more relevant to the user. 2. The system of claim 1, (Appendix 12) The registration unit is When registering ingredients, analyze the user's social media activity and register related ingredient information 2. The system of claim 1, (Appendix 13) The recipe creation unit includes: Infer user emotions and adjust recipe presentation based on the inferred user emotions 2. The system of claim 1, (Appendix 14) The recipe creation unit includes: When creating a recipe, adjust the level of detail in the recipe based on the importance of the ingredients 2. The system of claim 1, (Appendix 15) The recipe creation unit includes: When creating a recipe, apply different recipe algorithms depending on the ingredient category 2. The system of claim 1, (Appendix 16) The recipe creation unit includes: Infer user sentiment and adjust recipe length based on the inferred sentiment 2. The system of claim 1, (Appendix 17) The recipe creation unit includes: When creating recipes, prioritize recipes based on when ingredients are available 2. The system of claim 1, (Appendix 18) The recipe creation unit includes: When creating a recipe, adjust the order of your recipes based on ingredient relationships 2. The system of claim 1, (Appendix 19) The price proposal unit, Infer user sentiment and adjust price suggestions based on the inferred user sentiment 2. The system of claim 1, (Appendix 20) The price proposal unit, When proposing prices, we analyze the market prices of ingredients and propose appropriate prices. 2. The system of claim 1, (Appendix 21) The price proposal unit, When proposing prices, adjust prices based on the freshness and quality of ingredients. 2. The system of claim 1, (Appendix 22) The price proposal unit, Infer user sentiment and prioritize price offers based on the inferred user sentiment 2. The system of claim 1, (Appendix 23) The price proposal unit, When making price suggestions, the app takes into account the user's geographic location to provide the best price. 2. The system of claim 1, (Appendix 24) The price proposal unit, When proposing prices, adjust prices based on the user's purchase history 2. The system of claim 1, (Appendix 25) The condition setting unit is Inferring user emotions and adjusting the method of setting conditions based on the estimated user emotions 3. The system of claim 2, (Appendix 26) The condition setting unit is When setting conditions, the system refers to the user's past setting history to suggest optimal conditions 3. The system of claim 2, (Appendix 27) The condition setting unit is Inferring user emotions and determining priority of condition settings based on the estimated user emotions 3. The system of claim 2, (Appendix 28) The condition setting unit is When setting conditions, the system considers the user's device information to suggest optimal conditions 3. The system of claim 2, (Appendix 29) The providing unit is Infer user emotions and adjust recipe serving methods based on the inferred user emotions 4. The system of claim 3. (Appendix 30) The providing unit is When providing a recipe, the optimal method of providing the recipe is selected by referring to the user's past usage history. 4. The system of claim 3. (Appendix 31) The providing unit is Infer user emotions and adjust the order of recipes based on the inferred emotions 4. The system of claim 3. (Appendix 32) The providing unit is When providing recipes, the optimal method of providing them is selected taking into account the user's device information. 4. The system of claim 3. (Appendix 33) The notification unit is Infer user emotions and adjust notification methods based on the estimated user emotions 5. The system of claim 4. (Appendix 34) The notification unit is When notifying users, the system selects the optimal notification method by referring to the user's past notification history. 5. The system of claim 4. (Appendix 35) The notification unit is Infer user emotions and prioritize notifications based on the inferred user emotions 5. The system of claim 4. (Appendix 36) The notification unit is When notifying users, the optimal notification method is selected taking into account the user's device information. 5. The system of claim 4. [Explanation of symbols]

[0190] 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 registration unit for registering information on ingredients; a recipe creation unit that analyzes the information registered by the registration unit and creates a recipe; a price proposal unit that proposes a set sales price based on the recipe created by the recipe creation unit. A system characterized by:

2. Equipped with a condition setting section for setting recipe quantities, food genre, and cooking time conditions 2. The system of claim 1 .

3. A providing unit that provides the recipe created by the recipe creating unit to a user.

2. The system of claim 1 .

4. Equipped with a notification unit that notifies the user of the set sales price of ingredients 2. The system of claim 1 .

5. The recipe creation unit includes: Create recipes with tomatoes, cabbage or chicken 2. The system of claim 1 .

6. The price proposal unit, Proposing a set selling price of ingredients based on the recipe created by the recipe creation unit 2. The system of claim 1 .

7. The registration unit is To estimate a user's emotion and adjust the timing of registering ingredient information based on the estimated user's emotion.

2. The system of claim 1 .

8. The registration unit is When registering ingredients, analyze the user's past registration history and select the appropriate registration method.

2. The system of claim 1 .

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