System

Abstract

An object of a system according to an embodiment is to propose an optimum menu on the basis of food materials on hand and information on bargain goods.SOLUTION: A system according to an embodiment includes a food ingredient acquisition unit, a recipe generation unit, a bargain item information acquisition unit, and a recommendation unit. The food ingredient acquisition unit acquires food ingredient information of a user. The recipe generation unit generates a recipe on the basis of the ingredient information acquired by the ingredient acquisition unit. A special sale article information acquisition part acquires special sale article information of a nearby supermarket. The recommendation unit proposes an optimal menu based on the recipe generated by the recipe generation unit and the bargain item information acquired by the bargain item information acquisition 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 an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

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

[0004] Conventional technology had the problem of making it difficult to suggest optimal menus based on ingredients on hand and information on special offers.

[0005] The system according to the embodiment aims to propose the optimal menu based on ingredients available and information on special offers. [Means for solving the problem]

[0006] The system according to the embodiment includes an ingredient acquisition unit, a recipe generation unit, a sale item information acquisition unit, and a recommendation unit. The ingredient acquisition unit acquires ingredient information from the user. The recipe generation unit generates a recipe based on the ingredient information acquired by the ingredient acquisition unit. The sale item information acquisition unit acquires sale item information from nearby supermarkets. The recommendation unit proposes an optimal menu based on the recipe generated by the recipe generation unit and the sale item information acquired by the sale item information acquisition unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose optimal menus based on ingredients on hand and information on special offers. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's 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 read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The cooking assistance system according to the embodiment of the present invention is a system that generates recipes from ingredients and proposes optimal menus by utilizing information on special sales items and nearby supermarkets. This allows users to enjoy cooking efficiently and economically.

[0029] The cooking assistance system according to the embodiment includes an ingredient acquisition unit, a recipe generation unit, a sale item information acquisition unit, and a recommendation unit. The ingredient acquisition unit acquires ingredient information from a user. For example, the ingredient information can be acquired by scanning a barcode. The ingredient acquisition unit can also acquire ingredient information by manual input or voice input. The ingredient acquisition unit can also acquire ingredient information by taking images of ingredients using a smartphone camera and using image recognition technology. The recipe generation unit generates a recipe based on the ingredient information acquired by the ingredient acquisition unit. For example, the generation AI generates a recipe based on ingredient information entered by a user. The generation AI can also generate a recipe based on conditions specified by the user (e.g., cooking time or difficulty). The generation AI can also generate a new recipe based on an existing recipe database. The sale item information acquisition unit acquires sale item information from nearby supermarkets. For example, the sale item information is acquired from the supermarket's database. The sale item information acquisition unit can also acquire sale item information using web scraping technology. The sale item information acquisition unit can also acquire sale item information manually entered by a user. The recommendation unit proposes an optimal menu based on the recipe generated by the recipe generation unit and the sale item information acquired by the sale item information acquisition unit. For example, the recommendation unit proposes an economical menu that utilizes sale items. The recommendation unit can also propose a menu that takes into account the user's preferences and nutritional balance. Furthermore, the recommendation unit can also propose a personalized menu based on the user's ingredient information and sale item information. This allows the cooking assistance system according to the embodiment to allow the user to enjoy cooking efficiently and economically. For example, the user can enjoy cooking economically by utilizing sale items and can also enjoy discovering new dishes. Furthermore, recommending the lowest priced items improves shopping efficiency.

[0030] The recipe generation unit can propose an optimal cooking method by taking into consideration the freshness and nutritional value of the ingredient information. The recipe generation unit, for example, evaluates the freshness of ingredients in real time and proposes an optimal cooking method based on the freshness. For example, if the freshness is high, it recommends eating the ingredients raw or cooking them for a short time, and if the freshness is low, it suggests stewing. The recipe generation unit also evaluates the nutritional value of ingredients and proposes an optimal cooking method based on the nutritional value. For example, it can propose healthy recipes using highly nutritious ingredients. Furthermore, the recipe generation unit can comprehensively evaluate the freshness and nutritional value of ingredients and propose an optimal cooking method. This makes it possible to propose an optimal cooking method by taking into consideration the freshness and nutritional value of ingredients.

[0031] The recipe generation unit can generate recipes that avoid allergies based on the allergy information included in the ingredient information. For example, when a user inputs allergy information, the recipe generation unit generates recipes that avoid allergies based on that information. For example, a recipe that does not contain nuts is suggested to a user who is allergic to nuts. The recipe generation unit can also suggest alternative ingredients based on the allergy information. For example, a recipe that uses soy milk instead of dairy products is suggested to a user who is allergic to dairy products. Furthermore, the recipe generation unit can customize recipes based on the allergy information. For example, a recipe that avoids allergies is suggested to a user who has a specific allergy. In this way, recipes that avoid allergies can be generated.

[0032] The recipe generation unit can suggest the optimal consumption timing by taking into account the storage method and expiration date of the ingredient information. The recipe generation unit, for example, analyzes the storage method of the ingredient and suggests the optimal consumption timing based on the storage method. For example, the recipe generation unit suggests a recipe for early consumption of refrigerated ingredients. The recipe generation unit also evaluates the expiration date of the ingredient and suggests the optimal consumption timing based on the expiration date. For example, the recipe generation unit suggests a recipe for early consumption of ingredients with an approaching expiration date. Furthermore, the recipe generation unit can also comprehensively evaluate the storage method and expiration date of the ingredient and suggest the optimal consumption timing. This makes it possible to suggest the optimal consumption timing by taking into account the storage method and expiration date of the ingredient.

[0033] The recipe generation unit generates recipes that incorporate cuisine from different cultures and regions, thereby introducing a new food culture to the user. For example, the recipe generation unit generates recipes that incorporate cuisine from different cultures, thereby introducing a new food culture to the user. For example, it proposes recipes for Italian cuisine or Indian cuisine. The recipe generation unit can also generate recipes that use ingredients from different regions. For example, it can propose recipes that use local specialties. Furthermore, the recipe generation unit can customize recipes that incorporate cuisine from different cultures and regions. For example, it arranges cuisine from different cultures to suit the user's preferences. In this way, it is possible to generate recipes that incorporate cuisine from different cultures and regions, thereby introducing a new food culture to the user.

[0034] The sale item information acquisition unit reflects the inventory status of sale items in real time and can suggest alternative items when inventory is low. For example, the sale item information acquisition unit monitors the inventory status of sale items in real time and suggests alternative items when inventory is low. For example, if the sale item pork is out of stock, chicken is suggested as an alternative. The sale item information acquisition unit can also update the list of sale items based on the inventory status. For example, sale items with low inventory can be removed from the list and alternative items can be added to the list. Furthermore, the sale item information acquisition unit can set priorities for sale items based on the inventory status. For example, sale items with high inventory can be suggested first. This allows the inventory status of sale items to be reflected in real time and alternative items to be suggested when inventory is low.

[0035] The sale item information acquisition unit can propose a healthy menu by taking into consideration the nutritional value and calorie information of the sale item information. The sale item information acquisition unit, for example, analyzes the nutritional value of the sale items and proposes a healthy menu based on the nutritional value. For example, it can propose a menu using low-calorie sale items. The sale item information acquisition unit can also evaluate the calorie information of the sale items and propose a healthy menu based on the calorie information. For example, it can propose a menu using high-nutrition sale items. Furthermore, the sale item information acquisition unit can comprehensively evaluate the nutritional value and calorie information of the sale items and propose a healthy menu. This makes it possible to propose a healthy menu by taking into consideration the nutritional value and calorie information of the sale items.

[0036] The sale item information acquisition unit can analyze the user's preferences and trends based on the purchase history of the sale item information and suggest a personalized menu. The sale item information acquisition unit, for example, analyzes the user's sale item purchase history and suggests a personalized menu based on that data. For example, it can suggest a favorite menu based on sale items purchased in the past. The sale item information acquisition unit can also analyze the user's preferences and trends based on the purchase history and customize a menu based on the results. For example, it can suggest a menu using ingredients that the user frequently purchases. Furthermore, the sale item information acquisition unit can learn the user's ingredient preferences based on the purchase history and improve the accuracy of future suggestions. This makes it possible to analyze the user's preferences and trends based on the purchase history of sale items and suggest a personalized menu.

[0037] The sale item information acquisition unit provides origin and producer information for sale item information, allowing the user to understand the background of the ingredients. For example, the sale item information acquisition unit provides origin information for sale items, allowing the user to understand the background of the ingredients. For example, it displays origin information for vegetables on sale. The sale item information acquisition unit can also provide producer information, providing the user with information about the producer of the ingredients. For example, it displays information such as the producer's name, location, and cultivation method. Furthermore, the sale item information acquisition unit can also support the selection of ingredients based on the origin and producer information. For example, it makes it easier for the user to select ingredients from a specific origin or producer. This allows the user to understand the background of the ingredients by providing origin and producer information for sale items.

[0038] The recipe generation unit collects feedback from users on new dishes proposed by the generation AI, allowing the AI ​​to learn and improve the accuracy of its suggestions. The recipe generation unit, for example, collects feedback from users on new dishes proposed by the generation AI, allowing the AI ​​to learn based on that data and improve the accuracy of its suggestions. For example, it improves recipes based on user ratings. The recipe generation unit can also improve the algorithm of the generation AI based on feedback. For example, it can learn about user preferences and tendencies and make more personalized suggestions. Furthermore, the recipe generation unit can generate new dish ideas based on feedback. This allows the AI ​​to collect feedback from users on new dishes proposed by the generation AI, allowing the AI ​​to learn and improve the accuracy of its suggestions.

[0039] The recipe generation unit can incorporate elements that take seasons and events into consideration when proposing new dishes. The recipe generation unit, for example, takes seasonal ingredients into consideration and proposes new dishes that suit the season. For example, it proposes cold dishes in the summer and hot dishes in the winter. The recipe generation unit can also propose dishes that suit specific events. For example, it can propose dishes that suit Christmas or Halloween. Furthermore, the recipe generation unit can customize recipes that incorporate elements that take seasons and events into consideration. For example, it arranges dishes that suit the season or event according to the user's preferences. This makes it possible to incorporate elements that take seasons and events into proposals for new dishes.

[0040] The recipe generation unit can add a customization function that takes into account the user's ingredient preferences and allergy information. The recipe generation unit, for example, registers the user's ingredient preferences in a database and customizes new dishes based on that information. For example, it can suggest new dishes using favorite ingredients. The recipe generation unit can also customize new dishes based on allergy information. For example, it can suggest new dishes that avoid allergies. Furthermore, the recipe generation unit can personalize the new dish suggestions based on the user's preferences and allergy information. This makes it possible to add a customization function that takes into account the user's ingredient preferences and allergy information.

[0041] The recipe generation unit can add a personalization function based on the user's past cooking history. The recipe generation unit, for example, registers the user's past cooking history in a database and personalizes new dishes based on that information. For example, it can suggest new dishes based on dishes made in the past. The recipe generation unit can also analyze the user's preferences and tendencies based on the past cooking history and suggest new dishes based on the results. For example, it can suggest new dishes based on dishes that the user frequently makes. Furthermore, the recipe generation unit can learn the user's preferences based on the past cooking history and improve the accuracy of future suggestions. This makes it possible to add a personalization function based on the user's past cooking history.

[0042] The recommendation unit can add evaluation criteria that take into account product quality and review information to recommend the cheapest products. The recommendation unit, for example, adds criteria for evaluating product quality to recommending the cheapest products. For example, it prioritizes recommending products with high quality. The recommendation unit can also evaluate products based on review information and reflect the evaluation results in the recommendation. For example, it can recommend products that have received high user reviews. Furthermore, the recommendation unit can comprehensively evaluate quality and review information to recommend the most suitable product. This makes it possible to add evaluation criteria that take into account product quality and review information to recommending the cheapest products.

[0043] The recommendation unit can add a personalized function that reflects the user's purchasing history and preferences to the recommendations of the cheapest products. For example, the recommendation unit registers the user's purchasing history in a database and personalizes and recommends the cheapest products based on that information. For example, recommendations are made based on products purchased in the past. The recommendation unit can also customize the recommendations of the cheapest products based on the user's preferences. For example, it can preferentially recommend products from brands that the user likes. Furthermore, the recommendation unit can comprehensively evaluate the purchasing history and preferences and recommend the most suitable products. This makes it possible to add a personalized function that reflects the user's purchasing history and preferences to the recommendations of the cheapest products.

[0044] The recommendation unit can add a function to prioritize eco-friendly products and locally produced products when recommending the cheapest products. The recommendation unit, for example, adds a function to prioritize eco-friendly products when recommending the cheapest products. For example, it recommends environmentally friendly products. The recommendation unit can also prioritize locally produced products. For example, it can recommend products produced within the region. Furthermore, the recommendation unit can comprehensively evaluate eco-friendly products and locally produced products and recommend the most suitable products. This makes it possible to add a function to prioritize eco-friendly products and locally produced products when recommending the cheapest products.

[0045] The recommendation unit can add a function to make suggestions that take into consideration the user's health condition and nutritional balance when recommending the cheapest products. The recommendation unit, for example, recommends the cheapest products based on the user's health condition. For example, it recommends low-calorie products. The recommendation unit can also recommend products that take into consideration the user's nutritional balance. For example, it can recommend products that are rich in vitamins and minerals. Furthermore, the recommendation unit can comprehensively evaluate the user's health condition and nutritional balance and recommend the most suitable product. This makes it possible to add a function to make suggestions that take into consideration the user's health condition and nutritional balance when recommending the cheapest products.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The cooking assistance system may further include a storage monitoring unit that monitors the storage status of the user's ingredients. The storage monitoring unit may, for example, monitor the temperature and humidity inside the refrigerator using sensors and evaluate the storage status of the ingredients in real time. The storage monitoring unit may also record the storage period of ingredients and suggest recipes that prioritize ingredients with longer storage periods. Furthermore, the storage monitoring unit may notify users of ingredients whose storage status has deteriorated and encourage them to consume them sooner. This allows users to reduce food waste and enjoy cooking efficiently.

[0048] The recipe generation unit can further include a recipe suggestion function based on the user's ingredient purchase history. For example, it can suggest new recipes based on ingredients purchased in the past. The recipe generation unit can also analyze the user's preferences and trends based on the purchase history and customize recipes based on the results. For example, it can suggest recipes using ingredients that the user frequently purchases. Furthermore, the recipe generation unit can learn the user's preferences based on the purchase history and improve the accuracy of future suggestions. This makes it possible to suggest personalized recipes based on the user's purchase history.

[0049] The recipe generation unit can further include a function for suggesting healthy recipes based on the nutritional value of the user's ingredients. For example, it can suggest healthy recipes using nutritious ingredients. The recipe generation unit can also suggest recipes that take into account the user's health condition. For example, it can suggest low-calorie recipes to a user who is on a diet. Furthermore, the recipe generation unit can also suggest recipes that take into account the user's nutritional balance. This makes it possible to suggest recipes that take into account the user's health condition and nutritional balance.

[0050] The recipe generation unit can further include a function to suggest recipes based on the user's method of storing ingredients. For example, it can suggest recipes using frozen ingredients. The recipe generation unit can also suggest cooking methods according to the method of storing ingredients. For example, it can suggest recipes that use refrigerated ingredients early. Furthermore, the recipe generation unit can comprehensively evaluate the storage method and expiration date of ingredients and suggest the optimal timing to consume them. This makes it possible to suggest recipes that take into account the storage method and expiration date of ingredients.

[0051] The bargain item information acquisition unit can further include a function for making suggestions based on the quality information of bargain items. For example, it can prioritize suggestions of bargain items with high quality. The bargain item information acquisition unit can also make suggestions based on review information of bargain items. For example, it can suggest bargain items with high user reviews. Furthermore, the bargain item information acquisition unit can comprehensively evaluate the quality information and review information to suggest the most suitable bargain items. This makes it possible to make suggestions that take into account the quality and review information of bargain items.

[0052] The bargain item information acquisition unit can further include a suggestion function based on the origin information of bargain items. For example, it can provide origin information of bargain items to help the user understand the background of the ingredients. The bargain item information acquisition unit can also provide producer information to provide the user with information about the producer of the ingredients. For example, it can display information such as the producer's name, location, and cultivation method. Furthermore, the bargain item information acquisition unit can also support the selection of ingredients based on the origin and producer information. This allows the user to understand the background of the ingredients by providing origin and producer information of bargain items.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The ingredient acquisition unit acquires ingredient information from the user, for example, by scanning a barcode, manually entering information, using voice input, or using image recognition technology with a smartphone camera. Step 2: The recipe generation unit generates a recipe based on the ingredient information acquired by the ingredient acquisition unit. For example, a recipe is generated using a generation AI based on the ingredient information entered by the user and the specified conditions (cooking time and difficulty level). It is also possible to generate a new recipe based on an existing recipe database. Step 3: The sales information acquisition unit acquires sales information from nearby supermarkets. For example, sales information can be acquired from the supermarket's database, or by web scraping technology or manually entered by the user. Step 4: The recommendation unit proposes an optimal menu based on the recipes generated by the recipe generation unit and the sale item information acquired by the sale item information acquisition unit. For example, it proposes an economical menu that utilizes sale items, a menu that takes into account the user's preferences and nutritional balance, or a personalized menu.

[0055] (Example 2) The cooking assistance system according to the embodiment of the present invention is a system that generates recipes from ingredients and proposes optimal menus by utilizing information on special sales items and nearby supermarkets. This allows users to enjoy cooking efficiently and economically.

[0056] The cooking assistance system according to the embodiment includes an ingredient acquisition unit, a recipe generation unit, a sale item information acquisition unit, and a recommendation unit. The ingredient acquisition unit acquires ingredient information from a user. For example, the ingredient information can be acquired by scanning a barcode. The ingredient acquisition unit can also acquire ingredient information by manual input or voice input. The ingredient acquisition unit can also acquire ingredient information by taking images of ingredients using a smartphone camera and using image recognition technology. The recipe generation unit generates a recipe based on the ingredient information acquired by the ingredient acquisition unit. For example, the generation AI generates a recipe based on ingredient information entered by a user. The generation AI can also generate a recipe based on conditions specified by the user (e.g., cooking time or difficulty). The generation AI can also generate a new recipe based on an existing recipe database. The sale item information acquisition unit acquires sale item information from nearby supermarkets. For example, the sale item information is acquired from the supermarket's database. The sale item information acquisition unit can also acquire sale item information using web scraping technology. The sale item information acquisition unit can also acquire sale item information manually entered by a user. The recommendation unit proposes an optimal menu based on the recipe generated by the recipe generation unit and the sale item information acquired by the sale item information acquisition unit. For example, the recommendation unit proposes an economical menu that utilizes sale items. The recommendation unit can also propose a menu that takes into account the user's preferences and nutritional balance. Furthermore, the recommendation unit can also propose a personalized menu based on the user's ingredient information and sale item information. This allows the cooking assistance system according to the embodiment to allow the user to enjoy cooking efficiently and economically. For example, the user can enjoy cooking economically by utilizing sale items and can also enjoy discovering new dishes. Furthermore, recommending the lowest priced items improves shopping efficiency.

[0057] The recipe generation unit can propose an optimal cooking method by taking into consideration the freshness and nutritional value of the ingredient information. The recipe generation unit, for example, evaluates the freshness of ingredients in real time and proposes an optimal cooking method based on the freshness. For example, if the freshness is high, it recommends eating the ingredients raw or cooking them for a short time, and if the freshness is low, it suggests stewing. The recipe generation unit also evaluates the nutritional value of ingredients and proposes an optimal cooking method based on the nutritional value. For example, it can propose healthy recipes using highly nutritious ingredients. Furthermore, the recipe generation unit can comprehensively evaluate the freshness and nutritional value of ingredients and propose an optimal cooking method. This makes it possible to propose an optimal cooking method by taking into consideration the freshness and nutritional value of ingredients.

[0058] The recipe generation unit can generate recipes that avoid allergies based on the allergy information included in the ingredient information. For example, when a user inputs allergy information, the recipe generation unit generates recipes that avoid allergies based on that information. For example, a recipe that does not contain nuts is suggested to a user who is allergic to nuts. The recipe generation unit can also suggest alternative ingredients based on the allergy information. For example, a recipe that uses soy milk instead of dairy products is suggested to a user who is allergic to dairy products. Furthermore, the recipe generation unit can customize recipes based on the allergy information. For example, a recipe that avoids allergies is suggested to a user who has a specific allergy. In this way, recipes that avoid allergies can be generated.

[0059] The recipe generation unit can use the emotion estimation function to suggest recipes that match the user's mood. The recipe generation unit, for example, analyzes the user's emotional state in real time and suggests recipes based on the results. For example, for a user who is feeling stressed, it suggests a recipe using herbs that have a relaxing effect. The recipe generation unit can also use the emotion estimation function to select ingredients that match the user's mood. For example, if the user is tired, it suggests a recipe using ingredients that replenish energy. Furthermore, the recipe generation unit can also use the emotion estimation function to suggest cooking methods that match the user's mood. For example, if the user wants to relax, it suggests an easy-to-make recipe. This makes it possible to suggest recipes that match the user's mood.

[0060] The recipe generation unit can suggest the optimal consumption timing by taking into account the storage method and expiration date of the ingredient information. The recipe generation unit, for example, analyzes the storage method of the ingredient and suggests the optimal consumption timing based on the storage method. For example, the recipe generation unit suggests a recipe for early consumption of refrigerated ingredients. The recipe generation unit also evaluates the expiration date of the ingredient and suggests the optimal consumption timing based on the expiration date. For example, the recipe generation unit suggests a recipe for early consumption of ingredients with an approaching expiration date. Furthermore, the recipe generation unit can also comprehensively evaluate the storage method and expiration date of the ingredient and suggest the optimal consumption timing. This makes it possible to suggest the optimal consumption timing by taking into account the storage method and expiration date of the ingredient.

[0061] The recipe generation unit generates recipes that incorporate cuisine from different cultures and regions, thereby introducing a new food culture to the user. For example, the recipe generation unit generates recipes that incorporate cuisine from different cultures, thereby introducing a new food culture to the user. For example, it proposes recipes for Italian cuisine or Indian cuisine. The recipe generation unit can also generate recipes that use ingredients from different regions. For example, it can propose recipes that use local specialties. Furthermore, the recipe generation unit can customize recipes that incorporate cuisine from different cultures and regions. For example, it arranges cuisine from different cultures to suit the user's preferences. In this way, it is possible to generate recipes that incorporate cuisine from different cultures and regions, thereby introducing a new food culture to the user.

[0062] The recipe generation unit can use the emotion estimation function to analyze the emotions a user has toward a specific ingredient and customize a recipe based on that emotion. The recipe generation unit, for example, analyzes the emotions a user has toward a specific ingredient and customizes a recipe based on that emotion. For example, it can suggest recipes that use a favorite ingredient. The recipe generation unit can also use the emotion estimation function to suggest recipes that avoid ingredients that the user dislikes. For example, it can suggest recipes that do not use ingredients that the user dislikes. Furthermore, the recipe generation unit can use the emotion estimation function to arrange a recipe based on the user's emotions. For example, it can customize a recipe taking into account the emotions a user has toward a specific ingredient. This allows the recipe to be analyzed and customized based on the emotions a user has toward a specific ingredient.

[0063] The sale item information acquisition unit reflects the inventory status of sale items in real time and can suggest alternative items when inventory is low. For example, the sale item information acquisition unit monitors the inventory status of sale items in real time and suggests alternative items when inventory is low. For example, if the sale item pork is out of stock, chicken is suggested as an alternative. The sale item information acquisition unit can also update the list of sale items based on the inventory status. For example, sale items with low inventory can be removed from the list and alternative items can be added to the list. Furthermore, the sale item information acquisition unit can set priorities for sale items based on the inventory status. For example, sale items with high inventory can be suggested first. This allows the inventory status of sale items to be reflected in real time and alternative items to be suggested when inventory is low.

[0064] The sale item information acquisition unit can propose a healthy menu by taking into consideration the nutritional value and calorie information of the sale item information. The sale item information acquisition unit, for example, analyzes the nutritional value of the sale items and proposes a healthy menu based on the nutritional value. For example, it can propose a menu using low-calorie sale items. The sale item information acquisition unit can also evaluate the calorie information of the sale items and propose a healthy menu based on the calorie information. For example, it can propose a menu using high-nutrition sale items. Furthermore, the sale item information acquisition unit can comprehensively evaluate the nutritional value and calorie information of the sale items and propose a healthy menu. This makes it possible to propose a healthy menu by taking into consideration the nutritional value and calorie information of the sale items.

[0065] The sale item information acquisition unit can use the emotion estimation function to suggest a menu using sale items that matches the user's mood. The sale item information acquisition unit, for example, analyzes the user's emotional state in real time and suggests a menu using sale items based on the results. For example, it suggests an easy-to-prepare menu to a tired user. The sale item information acquisition unit can also use the emotion estimation function to select sale items that match the user's mood. For example, if the user wants to relax, it suggests a menu using sale items that have a relaxing effect. Furthermore, the sale item information acquisition unit can also use the emotion estimation function to suggest cooking methods that match the user's mood. For example, if the user is feeling stressed, it suggests an easy-to-prepare menu. This makes it possible to suggest a menu using sale items that matches the user's mood.

[0066] The sale item information acquisition unit can analyze the user's preferences and trends based on the purchase history of the sale item information and suggest a personalized menu. The sale item information acquisition unit, for example, analyzes the user's sale item purchase history and suggests a personalized menu based on that data. For example, it can suggest a favorite menu based on sale items purchased in the past. The sale item information acquisition unit can also analyze the user's preferences and trends based on the purchase history and customize a menu based on the results. For example, it can suggest a menu using ingredients that the user frequently purchases. Furthermore, the sale item information acquisition unit can learn the user's ingredient preferences based on the purchase history and improve the accuracy of future suggestions. This makes it possible to analyze the user's preferences and trends based on the purchase history of sale items and suggest a personalized menu.

[0067] The sale item information acquisition unit provides origin and producer information for sale item information, allowing the user to understand the background of the ingredients. For example, the sale item information acquisition unit provides origin information for sale items, allowing the user to understand the background of the ingredients. For example, it displays origin information for vegetables on sale. The sale item information acquisition unit can also provide producer information, providing the user with information about the producer of the ingredients. For example, it displays information such as the producer's name, location, and cultivation method. Furthermore, the sale item information acquisition unit can also support the selection of ingredients based on the origin and producer information. For example, it makes it easier for the user to select ingredients from a specific origin or producer. This allows the user to understand the background of the ingredients by providing origin and producer information for sale items.

[0068] The sale item information acquisition unit can use the emotion estimation function to analyze the emotion the user has toward sale items and customize a menu based on that emotion. The sale item information acquisition unit, for example, analyzes the emotion the user has toward sale items and customizes a menu based on that emotion. For example, it can suggest a menu using a favorite sale item. The sale item information acquisition unit can also use the emotion estimation function to suggest a menu that avoids sale items that the user dislikes. For example, it can suggest a menu that does not use the disliked sale item. Furthermore, the sale item information acquisition unit can also use the emotion estimation function to arrange a menu based on the user's emotion. For example, it can customize a menu taking into account the emotion the user has toward sale items. This allows the emotion the user has toward sale items to be analyzed and a menu to be customized based on that emotion.

[0069] The recipe generation unit collects feedback from users on new dishes proposed by the generation AI, allowing the AI ​​to learn and improve the accuracy of its suggestions. The recipe generation unit, for example, collects feedback from users on new dishes proposed by the generation AI, allowing the AI ​​to learn based on that data and improve the accuracy of its suggestions. For example, it improves recipes based on user ratings. The recipe generation unit can also improve the algorithm of the generation AI based on feedback. For example, it can learn about user preferences and tendencies and make more personalized suggestions. Furthermore, the recipe generation unit can generate new dish ideas based on feedback. This allows the AI ​​to collect feedback from users on new dishes proposed by the generation AI, allowing the AI ​​to learn and improve the accuracy of its suggestions.

[0070] The recipe generation unit can incorporate elements that take seasons and events into consideration when proposing new dishes. The recipe generation unit, for example, takes seasonal ingredients into consideration and proposes new dishes that suit the season. For example, it proposes cold dishes in the summer and hot dishes in the winter. The recipe generation unit can also propose dishes that suit specific events. For example, it can propose dishes that suit Christmas or Halloween. Furthermore, the recipe generation unit can customize recipes that incorporate elements that take seasons and events into consideration. For example, it arranges dishes that suit the season or event according to the user's preferences. This makes it possible to incorporate elements that take seasons and events into proposals for new dishes.

[0071] The recipe generation unit can use the emotion estimation function to suggest new dishes that match the user's mood. The recipe generation unit, for example, analyzes the user's emotional state in real time and suggests new dishes based on the results. For example, it suggests exotic dishes to a user who is feeling adventurous. The recipe generation unit can also use the emotion estimation function to select ingredients that match the user's mood. For example, if the user wants to relax, it suggests a new dish that uses ingredients that have a relaxing effect. Furthermore, the recipe generation unit can also use the emotion estimation function to suggest cooking methods that match the user's mood. For example, if the user is feeling stressed, it suggests a new dish that is easy to make. In this way, it is possible to suggest new dishes that match the user's mood.

[0072] The recipe generation unit can add a customization function that takes into account the user's ingredient preferences and allergy information. The recipe generation unit, for example, registers the user's ingredient preferences in a database and customizes new dishes based on that information. For example, it can suggest new dishes using favorite ingredients. The recipe generation unit can also customize new dishes based on allergy information. For example, it can suggest new dishes that avoid allergies. Furthermore, the recipe generation unit can personalize the new dish suggestions based on the user's preferences and allergy information. This makes it possible to add a customization function that takes into account the user's ingredient preferences and allergy information.

[0073] The recipe generation unit can add a personalization function based on the user's past cooking history. The recipe generation unit, for example, registers the user's past cooking history in a database and personalizes new dishes based on that information. For example, it can suggest new dishes based on dishes made in the past. The recipe generation unit can also analyze the user's preferences and tendencies based on the past cooking history and suggest new dishes based on the results. For example, it can suggest new dishes based on dishes that the user frequently makes. Furthermore, the recipe generation unit can learn the user's preferences based on the past cooking history and improve the accuracy of future suggestions. This makes it possible to add a personalization function based on the user's past cooking history.

[0074] The recipe generation unit can use the emotion estimation function to analyze the emotions the user has toward a new dish and customize the proposal based on those emotions. The recipe generation unit, for example, analyzes the emotions the user has toward a new dish and customizes the proposal based on those emotions. For example, it can suggest dishes that are highly popular. The recipe generation unit can also use the emotion estimation function to make suggestions that avoid dishes that the user dislikes. For example, it can suggest new dishes that do not use ingredients that the user dislikes. Furthermore, the recipe generation unit can also use the emotion estimation function to arrange new dishes based on the user's emotions. For example, it customizes the proposal taking into account the emotions the user has toward the new dish. This allows the recipe generation unit to analyze the emotions the user has toward a new dish and customize the proposal based on those emotions.

[0075] The recommendation unit can add evaluation criteria that take into account product quality and review information to recommend the cheapest products. The recommendation unit, for example, adds criteria for evaluating product quality to recommending the cheapest products. For example, it prioritizes recommending products with high quality. The recommendation unit can also evaluate products based on review information and reflect the evaluation results in the recommendation. For example, it can recommend products that have received high user reviews. Furthermore, the recommendation unit can comprehensively evaluate quality and review information to recommend the most suitable product. This makes it possible to add evaluation criteria that take into account product quality and review information to recommending the cheapest products.

[0076] The recommendation unit can add a personalized function that reflects the user's purchasing history and preferences to the recommendations of the cheapest products. For example, the recommendation unit registers the user's purchasing history in a database and personalizes and recommends the cheapest products based on that information. For example, recommendations are made based on products purchased in the past. The recommendation unit can also customize the recommendations of the cheapest products based on the user's preferences. For example, it can preferentially recommend products from brands that the user likes. Furthermore, the recommendation unit can comprehensively evaluate the purchasing history and preferences and recommend the most suitable products. This makes it possible to add a personalized function that reflects the user's purchasing history and preferences to the recommendations of the cheapest products.

[0077] The recommendation unit can use the emotion estimation function to make recommendations to increase the user's desire to purchase. The recommendation unit, for example, analyzes the user's emotional state in real time and makes recommendations to increase the user's desire to purchase based on the results. For example, the recommendation unit can recommend products that are particularly good value to a user who is conscious of saving money. The recommendation unit can also use the emotion estimation function to select products to increase the user's desire to purchase. For example, it can recommend products that the user is likely to be interested in. Furthermore, the recommendation unit can also use the emotion estimation function to suggest promotions to increase the user's desire to purchase. For example, it can provide discount information for specific products. In this way, the emotion estimation function can be used to make recommendations to increase the user's desire to purchase.

[0078] The recommendation unit can add a function to prioritize eco-friendly products and locally produced products when recommending the cheapest products. The recommendation unit, for example, adds a function to prioritize eco-friendly products when recommending the cheapest products. For example, it recommends environmentally friendly products. The recommendation unit can also prioritize locally produced products. For example, it can recommend products produced within the region. Furthermore, the recommendation unit can comprehensively evaluate eco-friendly products and locally produced products and recommend the most suitable products. This makes it possible to add a function to prioritize eco-friendly products and locally produced products when recommending the cheapest products.

[0079] The recommendation unit can add a function to make suggestions that take into consideration the user's health condition and nutritional balance when recommending the cheapest products. The recommendation unit, for example, recommends the cheapest products based on the user's health condition. For example, it recommends low-calorie products. The recommendation unit can also recommend products that take into consideration the user's nutritional balance. For example, it can recommend products that are rich in vitamins and minerals. Furthermore, the recommendation unit can comprehensively evaluate the user's health condition and nutritional balance and recommend the most suitable product. This makes it possible to add a function to make suggestions that take into consideration the user's health condition and nutritional balance when recommending the cheapest products.

[0080] The recommendation unit can use the emotion estimation function to analyze the emotion a user has toward the lowest priced item and customize recommendations based on that emotion. The recommendation unit, for example, analyzes the emotion a user has toward the lowest priced item and customizes recommendations based on that emotion. For example, it recommends items that are highly popular. The recommendation unit can also use the emotion estimation function to make recommendations that avoid items that the user dislikes. For example, it can recommend items that do not use disliked items. Furthermore, the recommendation unit can use the emotion estimation function to arrange recommendations based on the user's emotion. For example, it customizes recommendations taking into account the emotion a user has toward the lowest priced item. In this way, the emotion estimation function can be used to analyze the emotion a user has toward the lowest priced item and customize recommendations based on that emotion.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The cooking assistance system may further include a storage monitoring unit that monitors the storage status of the user's ingredients. The storage monitoring unit may, for example, monitor the temperature and humidity inside the refrigerator using sensors and evaluate the storage status of the ingredients in real time. The storage monitoring unit may also record the storage period of ingredients and suggest recipes that prioritize ingredients with longer storage periods. Furthermore, the storage monitoring unit may notify users of ingredients whose storage status has deteriorated and encourage them to consume them sooner. This allows users to reduce food waste and enjoy cooking efficiently.

[0083] The recipe generation unit can further include a recipe suggestion function based on the user's ingredient purchase history. For example, it can suggest new recipes based on ingredients purchased in the past. The recipe generation unit can also analyze the user's preferences and trends based on the purchase history and customize recipes based on the results. For example, it can suggest recipes using ingredients that the user frequently purchases. Furthermore, the recipe generation unit can learn the user's preferences based on the purchase history and improve the accuracy of future suggestions. This makes it possible to suggest personalized recipes based on the user's purchase history.

[0084] The recipe generation unit can use the emotion estimation function to evaluate the user's stress level and suggest recipes that match that stress level. For example, for a user with high stress, it can suggest recipes that use ingredients that have a relaxing effect. The recipe generation unit can also use the emotion estimation function to suggest cooking methods that match the user's stress level. For example, if the user is highly stressed, it can suggest recipes that are easy to make. Furthermore, the recipe generation unit can also use the emotion estimation function to select ingredients that match the user's stress level. This makes it possible to suggest recipes that match the user's stress level.

[0085] The recipe generation unit can further include a function for suggesting healthy recipes based on the nutritional value of the user's ingredients. For example, it can suggest healthy recipes using nutritious ingredients. The recipe generation unit can also suggest recipes that take into account the user's health condition. For example, it can suggest low-calorie recipes to a user who is on a diet. Furthermore, the recipe generation unit can also suggest recipes that take into account the user's nutritional balance. This makes it possible to suggest recipes that take into account the user's health condition and nutritional balance.

[0086] The recipe generation unit can use the emotion estimation function to evaluate the user's emotions toward ingredients and suggest recipes based on those emotions. For example, it can suggest recipes using favorite ingredients. The recipe generation unit can also use the emotion estimation function to suggest recipes that avoid ingredients that the user dislikes. For example, it can suggest recipes that do not use disliked ingredients. Furthermore, the recipe generation unit can also use the emotion estimation function to arrange recipes based on the user's emotions. This makes it possible to suggest recipes that take into account the user's emotions toward ingredients.

[0087] The recipe generation unit can further include a function to suggest recipes based on the user's method of storing ingredients. For example, it can suggest recipes using frozen ingredients. The recipe generation unit can also suggest cooking methods according to the method of storing ingredients. For example, it can suggest recipes that use refrigerated ingredients early. Furthermore, the recipe generation unit can comprehensively evaluate the storage method and expiration date of ingredients and suggest the optimal timing to consume them. This makes it possible to suggest recipes that take into account the storage method and expiration date of ingredients.

[0088] The recipe generation unit can use the emotion estimation function to suggest new dishes that match the user's mood. For example, an exotic dish can be suggested to a user who is feeling adventurous. The recipe generation unit can also use the emotion estimation function to select ingredients that match the user's mood. For example, if the user wants to relax, a new dish using ingredients that have a relaxing effect can be suggested. Furthermore, the recipe generation unit can also use the emotion estimation function to suggest cooking methods that match the user's mood. This makes it possible to suggest new dishes that match the user's mood.

[0089] The bargain item information acquisition unit can further include a function for making suggestions based on the quality information of bargain items. For example, it can prioritize suggestions of bargain items with high quality. The bargain item information acquisition unit can also make suggestions based on review information of bargain items. For example, it can suggest bargain items with high user reviews. Furthermore, the bargain item information acquisition unit can comprehensively evaluate the quality information and review information to suggest the most suitable bargain items. This makes it possible to make suggestions that take into account the quality and review information of bargain items.

[0090] The sale item information acquisition unit can use the emotion estimation function to suggest sale items to increase the user's purchasing motivation. For example, the unit analyzes the user's emotional state in real time and suggests sale items that will increase the user's purchasing motivation based on the results. For example, the unit can suggest sale items that are particularly advantageous to users who are conscious of saving money. The sale item information acquisition unit can also use the emotion estimation function to select sale items to increase the user's purchasing motivation. Furthermore, the sale item information acquisition unit can also use the emotion estimation function to suggest promotions to increase the user's purchasing motivation. This makes it possible to suggest sale items to increase the user's purchasing motivation using the emotion estimation function.

[0091] The bargain item information acquisition unit can further include a suggestion function based on the origin information of bargain items. For example, it can provide origin information of bargain items to help the user understand the background of the ingredients. The bargain item information acquisition unit can also provide producer information to provide the user with information about the producer of the ingredients. For example, it can display information such as the producer's name, location, and cultivation method. Furthermore, the bargain item information acquisition unit can also support the selection of ingredients based on the origin and producer information. This allows the user to understand the background of the ingredients by providing origin and producer information of bargain items.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The ingredient acquisition unit acquires ingredient information from the user, for example, by scanning a barcode, manually entering information, using voice input, or using image recognition technology with a smartphone camera. Step 2: The recipe generation unit generates a recipe based on the ingredient information acquired by the ingredient acquisition unit. For example, a recipe is generated using a generation AI based on the ingredient information entered by the user and the specified conditions (cooking time and difficulty level). It is also possible to generate a new recipe based on an existing recipe database. Step 3: The sales information acquisition unit acquires sales information from nearby supermarkets. For example, sales information can be acquired from the supermarket's database, or by web scraping technology or manually entered by the user. Step 4: The recommendation unit proposes an optimal menu based on the recipes generated by the recipe generation unit and the sale item information acquired by the sale item information acquisition unit. For example, it proposes an economical menu that utilizes sale items, a menu that takes into account the user's preferences and nutritional balance, or a personalized menu.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] 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.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0104] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] 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.

[0106] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 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.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] 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.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0119] 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.

[0120] 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.

[0121] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0123] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0124] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 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.

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

[0130] 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.

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

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0135] 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.

[0136] 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.

[0137] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0138] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0144] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0145] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0146] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0147] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0148] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0149] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0150] 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.

[0151] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0152] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0153] 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.

[0154] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0155] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0156] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0157] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0158] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0159] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0160] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0161] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an ingredient acquisition unit that acquires ingredient information of a user; a recipe creation unit that creates a recipe based on the ingredient information acquired by the ingredient acquisition unit; a sales information acquisition unit that acquires sales information of nearby supermarkets; a recommendation unit that proposes an optimal menu based on the recipe generated by the recipe generation unit and the special sale information acquired by the special sale information acquisition unit. A system characterized by:

2. The recipe generation unit The system proposes the best cooking method based on the freshness and nutritional value of the ingredients.

2. The system of claim 1.

3. The sale item information acquisition unit The inventory status of the sale items is reflected in real time, and alternative items are suggested if there is low inventory.

2. The system of claim 1.

4. The recipe generation unit The AI ​​will collect feedback from users about new dishes it proposes, allowing it to learn and improve the accuracy of its suggestions.

2. The system of claim 1.

5. The recommendation unit Adding rating criteria that take into account product quality and reviews to recommend the cheapest products 2. The system of claim 1.

6. The recipe generation unit Suggest recipes that match the user's mood 2. The system of claim 1.

7. The sale item information acquisition unit Suggesting menus using special sale items that match the user's mood 2. The system of claim 1.

8. The recipe generation unit Analyze users' feelings about new dishes and customize suggestions based on those feelings 2. The system of claim 1.

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