system

Abstract

The system according to the embodiment aims to generate recipes based on ingredient information and recommend inexpensive products by utilizing information from nearby supermarkets. [Solution] A system according to an embodiment includes an acquisition unit, a generation unit, a supermarket information acquisition unit, and a recommendation unit. The acquisition unit acquires ingredient information. The generation unit generates a recipe based on the ingredient information acquired by the acquisition unit. The supermarket information acquisition unit acquires information on nearby supermarkets. The recommendation unit recommends inexpensive products based on the information acquired by the supermarket information acquisition unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately generate recipes based on ingredient information, nor does it utilize information from nearby supermarkets to recommend inexpensive products, so there is room for improvement.

[0005] The system according to the embodiment aims to generate recipes based on ingredient information and recommend inexpensive products by utilizing information from nearby supermarkets. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, a generation unit, a supermarket information acquisition unit, and a recommendation unit. The acquisition unit acquires ingredient information. The generation unit generates a recipe based on the ingredient information acquired by the acquisition unit. The supermarket information acquisition unit acquires information on nearby supermarkets. The recommendation unit recommends inexpensive products based on the information acquired by the supermarket information acquisition unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate recipes based on ingredient information and recommend inexpensive products by utilizing information on nearby supermarkets. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A cooking assistance system according to an embodiment of the present invention is a powerful system for cooking. This cooking assistance system acquires ingredient information, generates recipes using a generation AI, and recommends the cheapest items by obtaining information on nearby supermarkets. For example, the system acquires information about ingredients a user owns or has purchased. The generation AI then generates an optimal recipe based on the acquired ingredient information. Furthermore, the generation AI obtains information about nearby supermarkets, analyzes that information, and recommends the cheapest items. This system potentially leads to the creation of new dishes and also provides economic benefits by using the cheapest items. Users can easily generate recipes without complex operations and efficiently cook by receiving recommendations for the cheapest items. This allows the cooking assistance system to acquire information about ingredients a user owns or has purchased, generate optimal recipes using a generation AI based on that information, and further obtain information about nearby supermarkets to recommend the cheapest items.

[0029] A cooking assistance system according to an embodiment includes an acquisition unit, a generation unit, a supermarket information acquisition unit, and a recommendation unit. The acquisition unit acquires information about ingredients owned or purchased by a user. For example, the acquisition unit can scan ingredient information stored in a refrigerator and convert it into digital data. The acquisition unit can also read receipt information about ingredients purchased by a user. The acquisition unit can also acquire ingredient information manually entered by a user. The generation unit generates a recipe based on the ingredient information acquired by the acquisition unit. The generation unit, for example, uses a generation AI to analyze the input ingredient information and generate an optimal recipe based on it. The generation AI generates a recipe based on the ingredient information using a text generation AI (e.g., LLM) or a multimodal generation AI. The supermarket information acquisition unit collects information about nearby supermarkets. For example, the supermarket information acquisition unit collects price and inventory information about nearby supermarkets using web scraping technology. The supermarket information acquisition unit can also acquire supermarket information through an API. The recommendation unit analyzes the information collected by the supermarket information acquisition unit and recommends the lowest-priced items. The recommendation unit, for example, uses a generation AI to analyze the collected supermarket information and recommend the cheapest products. The generation AI uses data mining technology and machine learning algorithms to identify the cheapest products and recommend them to the user. As a result, the cooking assistance system according to the embodiment obtains information on ingredients the user owns or has purchased, and the generation AI generates optimal recipes based on that information, and further obtains information on nearby supermarkets to recommend the cheapest products.

[0030] The acquisition unit can acquire information about ingredients that the user has or has purchased. The acquisition unit acquires, for example, information about ingredients that the user has. For example, the acquisition unit can scan ingredient information in the refrigerator and convert it into digital data. The acquisition unit can also read receipt information about ingredients purchased by the user. Furthermore, the acquisition unit can acquire ingredient information manually entered by the user. This enables more accurate recipe generation by acquiring information about ingredients that the user has or has purchased. Some or all of the above-described processing in the acquisition unit may be performed, for example, using AI or without AI. For example, the acquisition unit can input ingredient information manually entered by the user to the generation AI and have the generation AI analyze the ingredient information.

[0031] The generation unit can analyze the acquired ingredient information and generate an appropriate recipe based on it. The generation unit, for example, analyzes the acquired ingredient information and generates an optimal recipe based on it. For example, the generation unit uses a generation AI to analyze the input ingredient information and generate an optimal recipe based on it. The generation AI generates a recipe based on the ingredient information using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI generates a recipe for a dish that can be easily made using ingredients in the refrigerator. The generation AI can also generate a recipe taking into account the user's preferences and nutritional balance. For example, the generation AI learns the user's preferred ingredients and cooking methods and generates a recipe based on that. In this way, the acquired ingredient information can be analyzed to generate an optimal recipe. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI. For example, the generation unit can input the acquired ingredient information into the generation AI and cause the generation AI to generate a recipe.

[0032] The supermarket information acquisition unit can collect information about nearby supermarkets. The supermarket information acquisition unit, for example, collects information about nearby supermarkets. For example, the supermarket information acquisition unit uses web scraping technology to collect price information and inventory information about nearby supermarkets. The supermarket information acquisition unit can also acquire supermarket information through an API. For example, the supermarket information acquisition unit collects sale information and discount information about nearby supermarkets. Furthermore, the supermarket information acquisition unit can prioritize collecting information about the nearest supermarket by taking into account the user's geographical location information. In this way, by collecting information about nearby supermarkets, it is possible to recommend products with the lowest prices. Some or all of the above-mentioned processing in the supermarket information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the supermarket information acquisition unit can input supermarket information collected using web scraping technology into a generation AI and have the generation AI analyze the information.

[0033] The recommendation unit can analyze the collected supermarket information and recommend inexpensive products. The recommendation unit, for example, analyzes the collected supermarket information and recommends the cheapest products. For example, the recommendation unit uses a generation AI to analyze the collected supermarket information and recommend the cheapest products. The generation AI uses data mining technology or a machine learning algorithm to identify the cheapest products and recommend them to the user. For example, the recommendation unit prioritizes recommending sale items and discounted items. The recommendation unit can also recommend highly relevant products based on the user's purchasing history and preferences. For example, the recommendation unit generates recommendations based on products the user has purchased in the past. In this way, the cheapest products can be recommended by analyzing the collected supermarket information. Some or all of the above-described processing in the recommendation unit may be performed, for example, using AI or without using AI. For example, the recommendation unit can input collected supermarket information into the generation AI and have the generation AI make product recommendations.

[0034] The acquisition unit can analyze the user's past ingredient purchase history and select an appropriate acquisition method. The acquisition unit, for example, analyzes the user's past ingredient purchase history and selects the optimal acquisition method. For example, the acquisition unit prioritizes acquisition of ingredients that the user has frequently purchased in the past. The acquisition unit can also predict and acquire ingredients that will be purchased in a specific season based on the user's past purchase history. Furthermore, the acquisition unit can analyze the user's past purchasing patterns and suggest the most efficient acquisition method. In this way, the optimal acquisition method can be selected by analyzing the user's past ingredient purchase history. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past ingredient purchase history into the generation AI and cause the generation AI to select an acquisition method.

[0035] The acquisition unit can filter the ingredient information based on the user's current meal plan and health condition when acquiring the ingredient information. For example, the acquisition unit can filter the ingredient information based on the user's current meal plan and health condition when acquiring the ingredient information. For example, if the user is on a diet, the acquisition unit can prioritize acquiring low-calorie ingredient information. Furthermore, if the user needs a specific nutrient, the acquisition unit can also acquire ingredient information containing that nutrient. Furthermore, the acquisition unit can filter and acquire necessary ingredient information based on the user's meal plan. This makes it possible to acquire more appropriate ingredient information by filtering based on the user's current meal plan and health condition. Some or all of the above-described processing by the acquisition unit can be performed using, for example, AI, or without AI. For example, the acquisition unit can input data on the user's meal plan and health condition to the generation AI and have the generation AI perform filtering.

[0036] When acquiring ingredient information, the acquisition unit can prioritize acquiring highly relevant ingredient information by taking into account the user's geographical location information. For example, when acquiring ingredient information, the acquisition unit prioritizes acquiring highly relevant ingredient information by taking into account the user's geographical location information. For example, when the user is in a specific area, the acquisition unit prioritizes acquiring ingredient information available in that area. Furthermore, when the user is traveling, the acquisition unit can also acquire ingredient information available at the user's travel destination. Furthermore, the acquisition unit can prioritize acquiring ingredient information available at supermarkets near the user's home. In this way, highly relevant ingredient information can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's geographical location information to the generation AI and cause the generation AI to acquire highly relevant ingredient information.

[0037] The acquisition unit can analyze the user's social media activities when acquiring ingredient information and acquire related ingredient information. For example, the acquisition unit can analyze the user's social media activities when acquiring ingredient information and acquire related ingredient information. For example, the acquisition unit can acquire ingredient information based on recipes shared by the user on social media. The acquisition unit can also analyze posts from cooking accounts the user follows and acquire related ingredient information. Furthermore, the acquisition unit can acquire ingredient information based on cooking posts that the user has "liked." In this way, related ingredient information can be acquired by analyzing the user's social media activities. Some or all of the above-described processing by the acquisition unit can be performed using, for example, AI, or without AI. For example, the acquisition unit can input the user's social media activity data into the generation AI and cause the generation AI to acquire related ingredient information.

[0038] The generation unit can adjust the level of detail of the recipe based on the freshness and nutritional value of the ingredients when generating a recipe. For example, the generation unit adjusts the level of detail of the recipe based on the freshness and nutritional value of the ingredients when generating a recipe. For example, when using highly fresh ingredients, the generation unit generates a recipe including detailed cooking steps. Furthermore, when using highly nutritious ingredients, the generation unit can also generate a recipe including cooking methods that make the most of the nutritional value. Furthermore, when using less fresh ingredients, the generation unit can also generate a recipe including simple cooking steps. In this way, by adjusting the level of detail of the recipe based on the freshness and nutritional value of the ingredients, a more appropriate recipe can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the freshness and nutritional value of ingredients into the generation AI and cause the generation AI to adjust the level of detail of the recipe.

[0039] The generation unit can apply different recipe algorithms depending on the user's dietary restrictions and allergy information when generating a recipe. For example, the generation unit can apply different recipe algorithms depending on the user's dietary restrictions and allergy information when generating a recipe. For example, if the user has a gluten-free diet, the generation unit can generate a gluten-free recipe. Furthermore, if the user has a nut allergy, the generation unit can generate a nut-free recipe. Furthermore, if the user has a low-carbohydrate diet, the generation unit can generate a low-carbohydrate recipe. This allows for more appropriate recipes to be provided by applying different recipe algorithms depending on the user's dietary restrictions and allergy information. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the user's dietary restrictions and allergy information into the generation AI and cause the generation AI to apply the recipe algorithm.

[0040] The generation unit can determine the priority of recipes based on the seasonality of ingredients when generating recipes. The generation unit, for example, determines the priority of recipes based on the seasonality of ingredients when generating recipes. For example, the generation unit prioritizes recipes that use seasonal ingredients. The generation unit can also postpone recipes that use out-of-season ingredients. Furthermore, the generation unit can also prioritize recipes that use seasonal specialties. In this way, by determining the priority of recipes based on the seasonality of ingredients, more appropriate recipes can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input seasonality data of ingredients into the generation AI and cause the generation AI to determine the priority of recipes.

[0041] The generation unit can adjust the order of recipes based on the relevance of ingredients when generating a recipe. The generation unit, for example, adjusts the order of recipes based on the relevance of ingredients when generating a recipe. For example, the generation unit prioritizes generating recipes with good ingredient combinations. The generation unit can also postpone recipes with ingredients that do not go well together. Furthermore, the generation unit can generate recipes in an optimal order taking into account the relevance of ingredients. In this way, by adjusting the order of recipes based on the relevance of ingredients, a more appropriate recipe can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input ingredient relevance data into the generation AI and cause the generation AI to adjust the order of the recipes.

[0042] When acquiring supermarket information, the supermarket information acquisition unit can select an appropriate acquisition method by referring to past price fluctuation data. For example, when acquiring supermarket information, the supermarket information acquisition unit selects the optimal acquisition method by referring to past price fluctuation data. For example, the supermarket information acquisition unit acquires supermarket information at the cheapest time based on past price fluctuation data. The supermarket information acquisition unit can also acquire product information on products that are cheaper on specific days of the week or during specific time periods from past price fluctuation data. Furthermore, the supermarket information acquisition unit can analyze past price fluctuation data and suggest the most efficient acquisition method. In this way, the optimal acquisition method can be selected by referring to past price fluctuation data. Some or all of the above-described processing in the supermarket information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the supermarket information acquisition unit can input past price fluctuation data into the generation AI and have the generation AI select an acquisition method.

[0043] When acquiring supermarket information, the supermarket information acquisition unit can prioritize acquiring highly relevant information based on the user's purchase history. For example, when acquiring supermarket information, the supermarket information acquisition unit prioritizes acquiring highly relevant information based on the user's purchase history. For example, the supermarket information acquisition unit prioritizes acquiring information about products purchased by the user in the past. The supermarket information acquisition unit can also acquire information about products purchased in a specific season from the user's purchase history. Furthermore, the supermarket information acquisition unit can analyze the user's purchasing patterns and acquire the most relevant information. This allows for the provision of more appropriate information by preferentially acquiring highly relevant information based on the user's purchase history. Some or all of the above-described processing in the supermarket information acquisition unit may be performed using, or without, AI. For example, the supermarket information acquisition unit can input the user's purchase history data into the generation AI and cause the generation AI to acquire highly relevant information.

[0044] When acquiring supermarket information, the supermarket information acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. For example, when acquiring supermarket information, the supermarket information acquisition unit prioritizes acquiring highly relevant information by taking into account the user's geographical location information. For example, when acquiring supermarket information, the supermarket information acquisition unit prioritizes acquiring product information available in a specific area. Also, when the user is traveling, the supermarket information acquisition unit can acquire product information available at the user's travel destination. Furthermore, the supermarket information acquisition unit can prioritize acquiring product information available at supermarkets near the user's home. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the supermarket information acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the supermarket information acquisition unit can input the user's geographical location information to the generation AI and cause the generation AI to acquire highly relevant information.

[0045] The super information acquisition unit can analyze the user's social media activity and acquire related information when acquiring super information. For example, the super information acquisition unit can analyze the user's social media activity and acquire related information when acquiring super information. For example, the super information acquisition unit can acquire information based on products shared by the user on social media. The super information acquisition unit can also analyze the content of posts from supermarkets the user follows and acquire related information. Furthermore, the super information acquisition unit can acquire information based on product posts that the user has "liked." In this way, related information can be acquired by analyzing the user's social media activity. Some or all of the above-described processing in the super information acquisition unit can be performed using AI, for example, or without AI. For example, the super information acquisition unit can input the user's social media activity data into the generation AI and cause the generation AI to acquire related information.

[0046] The recommendation unit can adjust the level of detail of the recommendation based on the freshness and nutritional value of the product when making a recommendation. For example, the recommendation unit adjusts the level of detail of the recommendation based on the freshness and nutritional value of the product when making a recommendation. For example, when recommending a highly fresh product, the recommendation unit generates a recommendation including a detailed description. Furthermore, when recommending a highly nutritious product, the recommendation unit can also generate a recommendation including a cooking method that makes the most of the nutritional value. Furthermore, when recommending a less fresh product, the recommendation unit can also generate a recommendation including simple cooking steps. In this way, by adjusting the level of detail of the recommendation based on the freshness and nutritional value of the product, more appropriate recommendations can be provided. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input data on the freshness and nutritional value of the product to the generation AI and cause the generation AI to adjust the level of detail of the recommendation.

[0047] The recommendation unit can apply different recommendation algorithms depending on the user's purchasing history and preferences when making recommendations. For example, the recommendation unit can generate recommendations based on products previously purchased by the user. The recommendation unit can also recommend highly relevant products based on the user's preferences. Furthermore, the recommendation unit can analyze the user's purchasing history and generate the most relevant recommendations. This allows for more appropriate recommendations to be provided by applying different recommendation algorithms depending on the user's purchasing history and preferences. Some or all of the above-described processing in the recommendation unit can be performed using, or without, AI. For example, the recommendation unit can input data on the user's purchasing history and preferences into a generation AI and cause the generation AI to apply a recommendation algorithm.

[0048] The recommendation unit can determine the priority of recommendations based on the seasonality of the products when making recommendations. The recommendation unit, for example, determines the priority of recommendations based on the seasonality of the products when making recommendations. For example, the recommendation unit prioritizes recommendations that use seasonal ingredients. The recommendation unit can also postpone recommendations that use out-of-season ingredients. Furthermore, the recommendation unit can also prioritize recommendations that use seasonal specialties. In this way, by determining the priority of recommendations based on the seasonality of the products, more appropriate recommendations can be provided. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input product seasonality data into the generation AI and cause the generation AI to determine the priority of recommendations.

[0049] The recommendation unit can adjust the order of recommendations based on the relevance of products when making recommendations. The recommendation unit, for example, adjusts the order of recommendations based on the relevance of products when making recommendations. For example, the recommendation unit prioritizes generating recommendations that combine ingredients well. The recommendation unit can also postpone recommendations that combine ingredients poorly. Furthermore, the recommendation unit can generate recommendations in an optimal order taking into account the relevance of ingredients. In this way, by adjusting the order of recommendations based on the relevance of products, more appropriate recommendations can be provided. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input product relevance data into a generation AI and cause the generation AI to adjust the order of recommendations.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The cooking assistance system can also obtain information about the user's food allergies, and the generation AI can generate recipes taking that information into account. For example, if the user has a nut allergy, the generation AI can generate recipes that do not contain nuts. Also, if the user has a dairy allergy, it can generate recipes that do not contain dairy products. Furthermore, if the user is allergic to a specific ingredient, it can generate recipes that avoid that ingredient. This allows the system to provide recipes that take the user's allergy information into account, supporting safer and more appropriate cooking.

[0052] The acquisition unit can also acquire expiration date information for ingredients from the user, and the generation AI can generate recipes based on that information. For example, the acquisition unit can scan expiration dates on ingredients in the refrigerator and convert them into digital data. The acquisition unit can also read expiration dates from receipt information for ingredients purchased by the user. Furthermore, the acquisition unit can acquire expiration date information manually entered by the user. This can reduce food waste and support efficient cooking by generating recipes that prioritize the use of ingredients with upcoming expiration dates.

[0053] The supermarket information acquisition unit can analyze the user's purchasing history and preferentially acquire discount information for specific supermarkets. For example, the supermarket information acquisition unit preferentially collects discount information for supermarkets that the user has frequently visited in the past. Also, if the user prefers products from a specific brand, the supermarket information acquisition unit can preferentially acquire information on discounts for products from that brand. Furthermore, the supermarket information acquisition unit can analyze the user's purchasing patterns and acquire the most relevant discount information. This can support better shopping deals based on the user's purchasing history.

[0054] The acquisition unit can acquire information on how the user stores ingredients, and the generation AI can generate recipes based on that information. For example, the acquisition unit can scan the storage methods for ingredients in the refrigerator and convert them into digital data. The acquisition unit can also read storage methods from receipt information for ingredients purchased by the user. Furthermore, the acquisition unit can acquire storage method information manually entered by the user. This can support cooking while maintaining the quality of ingredients by generating recipes that include optimal cooking methods based on storage methods.

[0055] The supermarket information acquisition unit can prioritize acquiring information about local specialties of a specific region, taking into account the user's geographical location information. For example, if the user is in a specific region, the supermarket information acquisition unit prioritizes collecting information about local specialties of that region. Also, if the user is traveling, the supermarket information acquisition unit can acquire information about local specialties of the user's travel destination. Furthermore, the supermarket information acquisition unit can prioritize acquiring information about local specialties available at supermarkets near the user's home. This makes it possible to support cooking that utilizes local specialties by taking into account the user's geographical location information.

[0056] The recommendation unit can analyze the user's purchase history and prioritize recommending products related to a specific season. For example, the recommendation unit can recommend seasonal specialties based on product information of products purchased in the past by the user. The recommendation unit can also predict and recommend products that the user will purchase in a specific season based on the user's purchase history. Furthermore, the recommendation unit can analyze the user's purchasing patterns and recommend the most relevant seasonal products. This makes it possible to recommend appropriate products according to the season based on the user's purchase history.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The acquisition unit acquires information about ingredients that the user owns or has purchased. For example, the acquisition unit can scan the information about ingredients in the refrigerator and convert it into digital data. The acquisition unit can also read receipt information about ingredients purchased by the user. Furthermore, the acquisition unit can also acquire ingredient information manually entered by the user. Step 2: The generation unit generates a recipe based on the ingredient information acquired by the acquisition unit. The generation unit analyzes the input ingredient information using, for example, a generation AI and generates an optimal recipe based on the information. The generation AI generates a recipe based on the ingredient information using a text generation AI (for example, LLM) or a multimodal generation AI. Step 3: The supermarket information acquisition unit collects information about nearby supermarkets. For example, the supermarket information acquisition unit may use web scraping technology to collect price and inventory information about nearby supermarkets. The supermarket information acquisition unit may also obtain supermarket information through an API. Step 4: The recommendation unit analyzes the information collected by the supermarket information acquisition unit and recommends the cheapest products. The recommendation unit uses, for example, a generation AI to analyze the collected supermarket information and recommend the cheapest products. The generation AI uses data mining technology and machine learning algorithms to identify the cheapest products and recommend them to the user.

[0059] (Example 2) A cooking assistance system according to an embodiment of the present invention is a powerful system for cooking. This cooking assistance system acquires ingredient information, generates recipes using a generation AI, and recommends the cheapest items by obtaining information on nearby supermarkets. For example, the system acquires information about ingredients a user owns or has purchased. The generation AI then generates an optimal recipe based on the acquired ingredient information. Furthermore, the generation AI obtains information about nearby supermarkets, analyzes that information, and recommends the cheapest items. This system potentially leads to the creation of new dishes and also provides economic benefits by using the cheapest items. Users can easily generate recipes without complex operations and efficiently cook by receiving recommendations for the cheapest items. This allows the cooking assistance system to acquire information about ingredients a user owns or has purchased, generate optimal recipes using a generation AI based on that information, and further obtain information about nearby supermarkets to recommend the cheapest items.

[0060] A cooking assistance system according to an embodiment includes an acquisition unit, a generation unit, a supermarket information acquisition unit, and a recommendation unit. The acquisition unit acquires information about ingredients owned or purchased by a user. For example, the acquisition unit can scan ingredient information stored in a refrigerator and convert it into digital data. The acquisition unit can also read receipt information about ingredients purchased by a user. The acquisition unit can also acquire ingredient information manually entered by a user. The generation unit generates a recipe based on the ingredient information acquired by the acquisition unit. The generation unit, for example, uses a generation AI to analyze the input ingredient information and generate an optimal recipe based on it. The generation AI generates a recipe based on the ingredient information using a text generation AI (e.g., LLM) or a multimodal generation AI. The supermarket information acquisition unit collects information about nearby supermarkets. For example, the supermarket information acquisition unit collects price and inventory information about nearby supermarkets using web scraping technology. The supermarket information acquisition unit can also acquire supermarket information through an API. The recommendation unit analyzes the information collected by the supermarket information acquisition unit and recommends the lowest-priced items. The recommendation unit, for example, uses a generation AI to analyze the collected supermarket information and recommend the cheapest products. The generation AI uses data mining technology and machine learning algorithms to identify the cheapest products and recommend them to the user. As a result, the cooking assistance system according to the embodiment obtains information on ingredients the user owns or has purchased, and the generation AI generates optimal recipes based on that information, and further obtains information on nearby supermarkets to recommend the cheapest products.

[0061] The acquisition unit can acquire information about ingredients that the user has or has purchased. The acquisition unit acquires, for example, information about ingredients that the user has. For example, the acquisition unit can scan ingredient information in the refrigerator and convert it into digital data. The acquisition unit can also read receipt information about ingredients purchased by the user. Furthermore, the acquisition unit can acquire ingredient information manually entered by the user. This enables more accurate recipe generation by acquiring information about ingredients that the user has or has purchased. Some or all of the above-described processing in the acquisition unit may be performed, for example, using AI or without AI. For example, the acquisition unit can input ingredient information manually entered by the user to the generation AI and have the generation AI analyze the ingredient information.

[0062] The generation unit can analyze the acquired ingredient information and generate an appropriate recipe based on it. The generation unit, for example, analyzes the acquired ingredient information and generates an optimal recipe based on it. For example, the generation unit uses a generation AI to analyze the input ingredient information and generate an optimal recipe based on it. The generation AI generates a recipe based on the ingredient information using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI generates a recipe for a dish that can be easily made using ingredients in the refrigerator. The generation AI can also generate a recipe taking into account the user's preferences and nutritional balance. For example, the generation AI learns the user's preferred ingredients and cooking methods and generates a recipe based on that. In this way, the acquired ingredient information can be analyzed to generate an optimal recipe. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI. For example, the generation unit can input the acquired ingredient information into the generation AI and cause the generation AI to generate a recipe.

[0063] The supermarket information acquisition unit can collect information about nearby supermarkets. The supermarket information acquisition unit, for example, collects information about nearby supermarkets. For example, the supermarket information acquisition unit uses web scraping technology to collect price information and inventory information about nearby supermarkets. The supermarket information acquisition unit can also acquire supermarket information through an API. For example, the supermarket information acquisition unit collects sale information and discount information about nearby supermarkets. Furthermore, the supermarket information acquisition unit can prioritize collecting information about the nearest supermarket by taking into account the user's geographical location information. In this way, by collecting information about nearby supermarkets, it is possible to recommend products with the lowest prices. Some or all of the above-mentioned processing in the supermarket information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the supermarket information acquisition unit can input supermarket information collected using web scraping technology into a generation AI and have the generation AI analyze the information.

[0064] The recommendation unit can analyze the collected supermarket information and recommend inexpensive products. The recommendation unit, for example, analyzes the collected supermarket information and recommends the cheapest products. For example, the recommendation unit uses a generation AI to analyze the collected supermarket information and recommend the cheapest products. The generation AI uses data mining technology or a machine learning algorithm to identify the cheapest products and recommend them to the user. For example, the recommendation unit prioritizes recommending sale items and discounted items. The recommendation unit can also recommend highly relevant products based on the user's purchasing history and preferences. For example, the recommendation unit generates recommendations based on products the user has purchased in the past. In this way, the cheapest products can be recommended by analyzing the collected supermarket information. Some or all of the above-described processing in the recommendation unit may be performed, for example, using AI or without using AI. For example, the recommendation unit can input collected supermarket information into the generation AI and have the generation AI make product recommendations.

[0065] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring ingredient information based on the estimated user emotions. For example, the acquisition unit estimates the user's emotions and adjusts the timing of acquiring ingredient information based on the estimated user emotions. For example, if the user is feeling stressed, the acquisition unit delays the acquisition of ingredient information and acquires it when the user is relaxed. Furthermore, if the user is relaxed, the acquisition unit can immediately acquire ingredient information to support smooth cooking preparation. Furthermore, if the user is in a hurry, the acquisition unit can quickly acquire ingredient information to improve cooking efficiency. This allows the ingredient information to be acquired at a more appropriate time by adjusting the timing of acquiring ingredient information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, an AI. For example, the acquisition unit may input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0066] The acquisition unit can analyze the user's past ingredient purchase history and select an appropriate acquisition method. The acquisition unit, for example, analyzes the user's past ingredient purchase history and selects the optimal acquisition method. For example, the acquisition unit prioritizes acquisition of ingredients that the user has frequently purchased in the past. The acquisition unit can also predict and acquire ingredients that will be purchased in a specific season based on the user's past purchase history. Furthermore, the acquisition unit can analyze the user's past purchasing patterns and suggest the most efficient acquisition method. In this way, the optimal acquisition method can be selected by analyzing the user's past ingredient purchase history. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past ingredient purchase history into the generation AI and cause the generation AI to select an acquisition method.

[0067] The acquisition unit can filter the ingredient information based on the user's current meal plan and health condition when acquiring the ingredient information. For example, the acquisition unit can filter the ingredient information based on the user's current meal plan and health condition when acquiring the ingredient information. For example, if the user is on a diet, the acquisition unit can prioritize acquiring low-calorie ingredient information. Furthermore, if the user needs a specific nutrient, the acquisition unit can also acquire ingredient information containing that nutrient. Furthermore, the acquisition unit can filter and acquire necessary ingredient information based on the user's meal plan. This makes it possible to acquire more appropriate ingredient information by filtering based on the user's current meal plan and health condition. Some or all of the above-described processing by the acquisition unit can be performed using, for example, AI, or without AI. For example, the acquisition unit can input data on the user's meal plan and health condition to the generation AI and have the generation AI perform filtering.

[0068] The acquisition unit can estimate the user's emotions and determine the priority of ingredient information to be acquired based on the estimated user emotions. The acquisition unit, for example, estimates the user's emotions and determines the priority of ingredient information to be acquired based on the estimated user emotions. For example, if the user is feeling stressed, the acquisition unit may prioritize acquiring ingredient information with a relaxing effect. Furthermore, if the user is relaxed, the acquisition unit may prioritize acquiring ingredient information for everyday use. Furthermore, if the user is in a hurry, the acquisition unit may prioritize acquiring ingredient information with a short cooking time. This allows more appropriate ingredient information to be acquired by determining the priority of ingredient information to be acquired based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, an AI. For example, the acquisition unit may input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0069] When acquiring ingredient information, the acquisition unit can prioritize acquiring highly relevant ingredient information by taking into account the user's geographical location information. For example, when acquiring ingredient information, the acquisition unit prioritizes acquiring highly relevant ingredient information by taking into account the user's geographical location information. For example, when the user is in a specific area, the acquisition unit prioritizes acquiring ingredient information available in that area. Furthermore, when the user is traveling, the acquisition unit can also acquire ingredient information available at the user's travel destination. Furthermore, the acquisition unit can prioritize acquiring ingredient information available at supermarkets near the user's home. In this way, highly relevant ingredient information can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's geographical location information to the generation AI and cause the generation AI to acquire highly relevant ingredient information.

[0070] The acquisition unit can analyze the user's social media activities when acquiring ingredient information and acquire related ingredient information. For example, the acquisition unit can analyze the user's social media activities when acquiring ingredient information and acquire related ingredient information. For example, the acquisition unit can acquire ingredient information based on recipes shared by the user on social media. The acquisition unit can also analyze posts from cooking accounts the user follows and acquire related ingredient information. Furthermore, the acquisition unit can acquire ingredient information based on cooking posts that the user has "liked." In this way, related ingredient information can be acquired by analyzing the user's social media activities. Some or all of the above-described processing by the acquisition unit can be performed using, for example, AI, or without AI. For example, the acquisition unit can input the user's social media activity data into the generation AI and cause the generation AI to acquire related ingredient information.

[0071] The generation unit can estimate the user's emotions and adjust the recipe presentation method based on the estimated user emotions. For example, the generation unit can estimate the user's emotions and adjust the recipe presentation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a recipe with detailed steps. If the user is in a hurry, the generation unit can also generate a concise and to-the-point recipe. Furthermore, if the user is excited, the generation unit can also generate a visually appealing recipe. This allows for providing a more appropriate recipe by adjusting the recipe presentation method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or without an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the recipe presentation method.

[0072] The generation unit can adjust the level of detail of the recipe based on the freshness and nutritional value of the ingredients when generating a recipe. For example, the generation unit adjusts the level of detail of the recipe based on the freshness and nutritional value of the ingredients when generating a recipe. For example, when using highly fresh ingredients, the generation unit generates a recipe including detailed cooking steps. Furthermore, when using highly nutritious ingredients, the generation unit can also generate a recipe including cooking methods that make the most of the nutritional value. Furthermore, when using less fresh ingredients, the generation unit can also generate a recipe including simple cooking steps. In this way, by adjusting the level of detail of the recipe based on the freshness and nutritional value of the ingredients, a more appropriate recipe can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the freshness and nutritional value of ingredients into the generation AI and cause the generation AI to adjust the level of detail of the recipe.

[0073] The generation unit can apply different recipe algorithms depending on the user's dietary restrictions and allergy information when generating a recipe. For example, the generation unit can apply different recipe algorithms depending on the user's dietary restrictions and allergy information when generating a recipe. For example, if the user has a gluten-free diet, the generation unit can generate a gluten-free recipe. Furthermore, if the user has a nut allergy, the generation unit can generate a nut-free recipe. Furthermore, if the user has a low-carbohydrate diet, the generation unit can generate a low-carbohydrate recipe. This allows for more appropriate recipes to be provided by applying different recipe algorithms depending on the user's dietary restrictions and allergy information. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the user's dietary restrictions and allergy information into the generation AI and cause the generation AI to apply the recipe algorithm.

[0074] The generation unit can estimate the user's emotions and adjust the length of the recipe based on the estimated user emotions. For example, the generation unit can estimate the user's emotions and adjust the length of the recipe based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise recipe. If the user is relaxed, the generation unit can generate a longer recipe with detailed instructions. Furthermore, if the user is excited, the generation unit can generate a recipe with visually stimulating effects. This allows for adjusting the length of the recipe according to the user's emotions, thereby providing a more appropriate recipe. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the recipe.

[0075] The generation unit can determine the priority of recipes based on the seasonality of ingredients when generating recipes. The generation unit, for example, determines the priority of recipes based on the seasonality of ingredients when generating recipes. For example, the generation unit prioritizes recipes that use seasonal ingredients. The generation unit can also postpone recipes that use out-of-season ingredients. Furthermore, the generation unit can also prioritize recipes that use seasonal specialties. In this way, by determining the priority of recipes based on the seasonality of ingredients, more appropriate recipes can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input seasonality data of ingredients into the generation AI and cause the generation AI to determine the priority of recipes.

[0076] The generation unit can adjust the order of recipes based on the relevance of ingredients when generating a recipe. The generation unit, for example, adjusts the order of recipes based on the relevance of ingredients when generating a recipe. For example, the generation unit prioritizes generating recipes with good ingredient combinations. The generation unit can also postpone recipes with ingredients that do not go well together. Furthermore, the generation unit can generate recipes in an optimal order taking into account the relevance of ingredients. In this way, by adjusting the order of recipes based on the relevance of ingredients, a more appropriate recipe can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input ingredient relevance data into the generation AI and cause the generation AI to adjust the order of the recipes.

[0077] The super information acquisition unit can estimate the user's emotions and adjust the timing of acquiring the super information based on the estimated user emotions. The super information acquisition unit, for example, estimates the user's emotions and adjusts the timing of acquiring the super information based on the estimated user emotions. For example, if the user is feeling stressed, the super information acquisition unit delays acquiring the super information and acquires it when the user is relaxed. Furthermore, if the user is relaxed, the super information acquisition unit can immediately acquire the super information to support smooth shopping. Furthermore, if the user is in a hurry, the super information acquisition unit can quickly acquire the super information to improve shopping efficiency. In this way, by adjusting the timing of acquiring the super information according to the user's emotions, the super information can be acquired at a more appropriate time. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the super information acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the super information acquisition unit can input the user's emotional data into the generation AI and have the generation AI perform emotion estimation.

[0078] When acquiring supermarket information, the supermarket information acquisition unit can select an appropriate acquisition method by referring to past price fluctuation data. For example, when acquiring supermarket information, the supermarket information acquisition unit selects the optimal acquisition method by referring to past price fluctuation data. For example, the supermarket information acquisition unit acquires supermarket information at the cheapest time based on past price fluctuation data. The supermarket information acquisition unit can also acquire product information on products that are cheaper on specific days of the week or during specific time periods from past price fluctuation data. Furthermore, the supermarket information acquisition unit can analyze past price fluctuation data and suggest the most efficient acquisition method. In this way, the optimal acquisition method can be selected by referring to past price fluctuation data. Some or all of the above-described processing in the supermarket information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the supermarket information acquisition unit can input past price fluctuation data into the generation AI and have the generation AI select an acquisition method.

[0079] When acquiring supermarket information, the supermarket information acquisition unit can prioritize acquiring highly relevant information based on the user's purchase history. For example, when acquiring supermarket information, the supermarket information acquisition unit prioritizes acquiring highly relevant information based on the user's purchase history. For example, the supermarket information acquisition unit prioritizes acquiring information about products purchased by the user in the past. The supermarket information acquisition unit can also acquire information about products purchased in a specific season from the user's purchase history. Furthermore, the supermarket information acquisition unit can analyze the user's purchasing patterns and acquire the most relevant information. This allows for the provision of more appropriate information by preferentially acquiring highly relevant information based on the user's purchase history. Some or all of the above-described processing in the supermarket information acquisition unit may be performed using, or without, AI. For example, the supermarket information acquisition unit can input the user's purchase history data into the generation AI and cause the generation AI to acquire highly relevant information.

[0080] The super information acquisition unit can estimate the user's emotions and determine the priority of the super information to be acquired based on the estimated user emotions. The super information acquisition unit, for example, estimates the user's emotions and determines the priority of the super information to be acquired based on the estimated user emotions. For example, when the user is feeling stressed, the super information acquisition unit prioritizes acquiring product information that has a relaxing effect. Furthermore, when the user is relaxed, the super information acquisition unit can also prioritize acquiring information about everyday shopping. Furthermore, when the user is in a hurry, the super information acquisition unit can also prioritize acquiring product information that can be purchased quickly. This allows for determining the priority of the super information to be acquired based on the user's emotions, thereby providing more appropriate information. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the super information acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the super information acquisition unit can input the user's emotional data into the generation AI and have the generation AI perform emotion estimation.

[0081] When acquiring supermarket information, the supermarket information acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. For example, when acquiring supermarket information, the supermarket information acquisition unit prioritizes acquiring highly relevant information by taking into account the user's geographical location information. For example, when acquiring supermarket information, the supermarket information acquisition unit prioritizes acquiring product information available in a specific area. Also, when the user is traveling, the supermarket information acquisition unit can acquire product information available at the user's travel destination. Furthermore, the supermarket information acquisition unit can prioritize acquiring product information available at supermarkets near the user's home. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the supermarket information acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the supermarket information acquisition unit can input the user's geographical location information to the generation AI and cause the generation AI to acquire highly relevant information.

[0082] The super information acquisition unit can analyze the user's social media activity and acquire related information when acquiring super information. For example, the super information acquisition unit can analyze the user's social media activity and acquire related information when acquiring super information. For example, the super information acquisition unit can acquire information based on products shared by the user on social media. The super information acquisition unit can also analyze the content of posts from supermarkets the user follows and acquire related information. Furthermore, the super information acquisition unit can acquire information based on product posts that the user has "liked." In this way, related information can be acquired by analyzing the user's social media activity. Some or all of the above-described processing in the super information acquisition unit can be performed using AI, for example, or without AI. For example, the super information acquisition unit can input the user's social media activity data into the generation AI and cause the generation AI to acquire related information.

[0083] The recommendation unit can estimate the user's emotions and adjust the way recommendations are presented based on the estimated user emotions. For example, the recommendation unit can estimate the user's emotions and adjust the way recommendations are presented based on the estimated user emotions. For example, if the user is relaxed, the recommendation unit can generate recommendations that include detailed explanations. Furthermore, if the user is in a hurry, the recommendation unit can generate concise and to-the-point recommendations. Furthermore, if the user is excited, the recommendation unit can generate visually appealing recommendations. This allows for more appropriate recommendations to be provided by adjusting the way recommendations are presented based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recommendation unit can be performed using, for example, an AI, or without an AI. For example, the recommendation unit can input user emotional data into the generation AI and have the generation AI adjust the way the recommendations are expressed.

[0084] The recommendation unit can adjust the level of detail of the recommendation based on the freshness and nutritional value of the product when making a recommendation. For example, the recommendation unit adjusts the level of detail of the recommendation based on the freshness and nutritional value of the product when making a recommendation. For example, when recommending a highly fresh product, the recommendation unit generates a recommendation including a detailed description. Furthermore, when recommending a highly nutritious product, the recommendation unit can also generate a recommendation including a cooking method that makes the most of the nutritional value. Furthermore, when recommending a less fresh product, the recommendation unit can also generate a recommendation including simple cooking steps. In this way, by adjusting the level of detail of the recommendation based on the freshness and nutritional value of the product, more appropriate recommendations can be provided. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input data on the freshness and nutritional value of the product to the generation AI and cause the generation AI to adjust the level of detail of the recommendation.

[0085] The recommendation unit can apply different recommendation algorithms depending on the user's purchasing history and preferences when making recommendations. For example, the recommendation unit can generate recommendations based on products previously purchased by the user. The recommendation unit can also recommend highly relevant products based on the user's preferences. Furthermore, the recommendation unit can analyze the user's purchasing history and generate the most relevant recommendations. This allows for more appropriate recommendations to be provided by applying different recommendation algorithms depending on the user's purchasing history and preferences. Some or all of the above-described processing in the recommendation unit can be performed using, or without, AI. For example, the recommendation unit can input data on the user's purchasing history and preferences into a generation AI and cause the generation AI to apply a recommendation algorithm.

[0086] The recommendation unit can estimate the user's emotions and adjust the length of recommendations based on the estimated user emotions. For example, the recommendation unit can estimate the user's emotions and adjust the length of recommendations based on the estimated user emotions. For example, if the user is in a hurry, the recommendation unit can generate short, to-the-point recommendations. Furthermore, if the user is relaxed, the recommendation unit can generate longer recommendations with detailed explanations. Furthermore, if the user is excited, the recommendation unit can generate recommendations with visually stimulating effects. By adjusting the length of recommendations according to the user's emotions, more appropriate recommendations can be provided. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recommendation unit can be performed using, for example, an AI, or without an AI. For example, the recommendation unit can input the user's emotional data into the generation AI and have the generation AI adjust the length of the recommendation.

[0087] The recommendation unit can determine the priority of recommendations based on the seasonality of the products when making recommendations. The recommendation unit, for example, determines the priority of recommendations based on the seasonality of the products when making recommendations. For example, the recommendation unit prioritizes recommendations that use seasonal ingredients. The recommendation unit can also postpone recommendations that use out-of-season ingredients. Furthermore, the recommendation unit can also prioritize recommendations that use seasonal specialties. In this way, by determining the priority of recommendations based on the seasonality of the products, more appropriate recommendations can be provided. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input product seasonality data into the generation AI and cause the generation AI to determine the priority of recommendations.

[0088] The recommendation unit can adjust the order of recommendations based on the relevance of products when making recommendations. The recommendation unit, for example, adjusts the order of recommendations based on the relevance of products when making recommendations. For example, the recommendation unit prioritizes generating recommendations that combine ingredients well. The recommendation unit can also postpone recommendations that combine ingredients poorly. Furthermore, the recommendation unit can generate recommendations in an optimal order taking into account the relevance of ingredients. In this way, by adjusting the order of recommendations based on the relevance of products, more appropriate recommendations can be provided. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input product relevance data into a generation AI and cause the generation AI to adjust the order of recommendations. === Hard Collateral 1-1 === Each of the multiple elements, including the acquisition unit, generation unit, supermarket information acquisition unit, and recommendation unit, described above, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit uses the camera 42 of the smart device 14 to scan ingredient information in the refrigerator and convert it into digital data. The acquisition unit also acquires ingredient information manually entered by the user using the reception device 38 of the smart device 14. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and the generation AI generates an optimal recipe based on the acquired ingredient information. For example, the supermarket information acquisition unit uses web scraping technology by the specific processing unit 290 of the data processing device 12 to collect price information and inventory information of nearby supermarkets. For example, the recommendation unit analyzes the information collected by the specific processing unit 290 of the data processing device 12 and recommends the cheapest products. === Hard Collateral 1-2 === Each of the multiple elements, including the acquisition unit, generation unit, supermarket information acquisition unit, and recommendation unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit uses the camera 42 of the smart glasses 214 to scan ingredient information in the refrigerator and convert it into digital data. The acquisition unit also acquires ingredient information manually entered by the user using the microphone 238 of the smart glasses 214. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and the generation AI generates an optimal recipe based on the acquired ingredient information. For example, the supermarket information acquisition unit uses web scraping technology by the specific processing unit 290 of the data processing device 12 to collect price information and inventory information of nearby supermarkets. For example, the recommendation unit analyzes the information collected by the specific processing unit 290 of the data processing device 12 and recommends the cheapest products. === Hard Collateral 1-3 === Each of the multiple elements, including the acquisition unit, generation unit, supermarket information acquisition unit, and recommendation unit, described above, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the acquisition unit scans ingredient information in the refrigerator using the camera 42 of the headset terminal 314 and converts it into digital data. The acquisition unit also acquires ingredient information manually entered by the user using the microphone 238 of the headset terminal 314. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and the generation AI generates an optimal recipe based on the acquired ingredient information. For example, the supermarket information acquisition unit uses web scraping technology by the specific processing unit 290 of the data processing device 12 to collect price information and inventory information of nearby supermarkets. For example, the recommendation unit analyzes the information collected by the specific processing unit 290 of the data processing device 12 and recommends the cheapest products. === Hard Collateral 1-4 === Each of the multiple elements, including the acquisition unit, generation unit, supermarket information acquisition unit, and recommendation unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit uses the camera 42 of the robot 414 to scan ingredient information in the refrigerator and convert it into digital data. The acquisition unit also acquires ingredient information manually entered by the user using the microphone 238 of the robot 414. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and the generation AI generates an optimal recipe based on the acquired ingredient information. For example, the supermarket information acquisition unit uses web scraping technology by the specific processing unit 290 of the data processing device 12 to collect price information and inventory information of nearby supermarkets. For example, the recommendation unit analyzes the information collected by the specific processing unit 290 of the data processing device 12 and recommends the cheapest products.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The cooking assistance system can also obtain information about the user's food allergies, and the generation AI can generate recipes taking that information into account. For example, if the user has a nut allergy, the generation AI can generate recipes that do not contain nuts. Also, if the user has a dairy allergy, it can generate recipes that do not contain dairy products. Furthermore, if the user is allergic to a specific ingredient, it can generate recipes that avoid that ingredient. This allows the system to provide recipes that take the user's allergy information into account, supporting safer and more appropriate cooking.

[0091] The acquisition unit can also acquire expiration date information for ingredients from the user, and the generation AI can generate recipes based on that information. For example, the acquisition unit can scan expiration dates on ingredients in the refrigerator and convert them into digital data. The acquisition unit can also read expiration dates from receipt information for ingredients purchased by the user. Furthermore, the acquisition unit can acquire expiration date information manually entered by the user. This can reduce food waste and support efficient cooking by generating recipes that prioritize the use of ingredients with upcoming expiration dates.

[0092] The generation unit can estimate the user's emotions and adjust the difficulty of the recipe based on the estimated user's emotions. For example, if the user is feeling stressed, the generation unit can generate a simple and easy recipe. If the user is feeling relaxed, the generation unit can also generate a recipe that requires a little effort. Furthermore, if the user is feeling challenging, the generation unit can also generate a more difficult recipe. In this way, by adjusting the difficulty of the recipe according to the user's emotions, a more appropriate cooking experience can be provided.

[0093] The supermarket information acquisition unit can analyze the user's purchasing history and preferentially acquire discount information for specific supermarkets. For example, the supermarket information acquisition unit preferentially collects discount information for supermarkets that the user has frequently visited in the past. Also, if the user prefers products from a specific brand, the supermarket information acquisition unit can preferentially acquire information on discounts for products from that brand. Furthermore, the supermarket information acquisition unit can analyze the user's purchasing patterns and acquire the most relevant discount information. This can support better shopping deals based on the user's purchasing history.

[0094] The recommendation unit can estimate the user's emotions and adjust the timing of recommendations based on the estimated user emotions. For example, if the recommendation unit is feeling stressed, it can delay making recommendations and make recommendations at a time when the user is relaxed. Also, if the user is relaxed, the recommendation unit can make recommendations immediately to support smooth shopping. Furthermore, if the user is in a hurry, the recommendation unit can make recommendations quickly to increase shopping efficiency. In this way, by adjusting the timing of recommendations according to the user's emotions, it is possible to provide more appropriate recommendations.

[0095] The acquisition unit can acquire information on how the user stores ingredients, and the generation AI can generate recipes based on that information. For example, the acquisition unit can scan the storage methods for ingredients in the refrigerator and convert them into digital data. The acquisition unit can also read storage methods from receipt information for ingredients purchased by the user. Furthermore, the acquisition unit can acquire storage method information manually entered by the user. This can support cooking while maintaining the quality of ingredients by generating recipes that include optimal cooking methods based on storage methods.

[0096] The generation unit can estimate the user's emotions and adjust the presentation method of the recipe based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate a recipe with a visually appealing presentation. If the user is in a hurry, the generation unit can generate a recipe with a concise and to-the-point presentation. Furthermore, if the user is excited, the generation unit can generate a recipe with an entertaining presentation. In this way, by adjusting the presentation method of the recipe according to the user's emotions, a more appropriate cooking experience can be provided.

[0097] The supermarket information acquisition unit can prioritize acquiring information about local specialties of a specific region, taking into account the user's geographical location information. For example, if the user is in a specific region, the supermarket information acquisition unit prioritizes collecting information about local specialties of that region. Also, if the user is traveling, the supermarket information acquisition unit can acquire information about local specialties of the user's travel destination. Furthermore, the supermarket information acquisition unit can prioritize acquiring information about local specialties available at supermarkets near the user's home. This makes it possible to support cooking that utilizes local specialties by taking into account the user's geographical location information.

[0098] The recommendation unit can estimate the user's emotions and adjust the content of recommendations based on the estimated user emotions. For example, if the user is feeling stressed, the recommendation unit can recommend products that have a relaxing effect. Also, if the user is relaxed, the recommendation unit can recommend products that are useful for everyday shopping. Furthermore, if the user is in a hurry, the recommendation unit can recommend products that can be purchased quickly. In this way, by adjusting the content of recommendations according to the user's emotions, more appropriate recommendations can be provided.

[0099] The recommendation unit can analyze the user's purchase history and prioritize recommending products related to a specific season. For example, the recommendation unit can recommend seasonal specialties based on product information of products purchased in the past by the user. The recommendation unit can also predict and recommend products that the user will purchase in a specific season based on the user's purchase history. Furthermore, the recommendation unit can analyze the user's purchasing patterns and recommend the most relevant seasonal products. This makes it possible to recommend appropriate products according to the season based on the user's purchase history.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The acquisition unit acquires information about ingredients that the user owns or has purchased. For example, the acquisition unit can scan the information about ingredients in the refrigerator and convert it into digital data. The acquisition unit can also read receipt information about ingredients purchased by the user. Furthermore, the acquisition unit can also acquire ingredient information manually entered by the user. Step 2: The generation unit generates a recipe based on the ingredient information acquired by the acquisition unit. The generation unit analyzes the input ingredient information using, for example, a generation AI and generates an optimal recipe based on the information. The generation AI generates a recipe based on the ingredient information using a text generation AI (for example, LLM) or a multimodal generation AI. Step 3: The supermarket information acquisition unit collects information about nearby supermarkets. For example, the supermarket information acquisition unit may use web scraping technology to collect price and inventory information about nearby supermarkets. The supermarket information acquisition unit may also obtain supermarket information through an API. Step 4: The recommendation unit analyzes the information collected by the supermarket information acquisition unit and recommends the cheapest products. The recommendation unit uses, for example, a generation AI to analyze the collected supermarket information and recommend the cheapest products. The generation AI uses data mining technology and machine learning algorithms to identify the cheapest products and recommend them to the user.

[0102] 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.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0104] 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.

[0105] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] 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.

[0118] 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.

[0119] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 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.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0125] 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.

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

[0127] 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).

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

[0129] 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.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0141] 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.

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

[0143] 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).

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

[0145] 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.

[0146] 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.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0153] 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.

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

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] [Explanation of symbols]

[0174] 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 acquisition unit that acquires ingredient information, a generation unit that generates a recipe based on the ingredient information acquired by the acquisition unit; a supermarket information acquisition unit that acquires information about nearby supermarkets; a recommendation unit that recommends inexpensive products based on the information acquired by the supermarket information acquisition unit. A system characterized by:

2. The acquisition unit is, Get information about ingredients the user owns or has purchased 2. The system of claim 1.

3. The generation unit Analyze the acquired ingredient information and generate an appropriate recipe based on it 2. The system of claim 1.

4. The aforementioned super information acquisition unit is Gather information about nearby supermarkets.

2. The system of claim 1.

5. The recommendation unit is, Analyze collected supermarket information and recommend inexpensive products 2. The system of claim 1.

6. The acquisition unit is, The system estimates the user's emotions and adjusts the timing of acquiring ingredient information based on the estimated user emotions.

2. The system of claim 1.

7. The acquisition unit is, Analyze the user's past food purchase history and select the appropriate acquisition method 2. The system of claim 1.

8. The acquisition unit is, Filtering ingredient information based on the user's current diet plan and health status 2. The system of claim 1.

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