System

Abstract

An object of a system according to an embodiment is to accurately evaluate the quality and proper price of a food ingredient.SOLUTION: A system includes a quality evaluation part and a price confirmation part. The quality evaluation unit evaluates the qualities of the food materials using the generated AI. A price confirmation part analyzes the market price data of the food material evaluated by the quality evaluation part and confirms a proper price.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem of making it difficult to accurately determine the quality and fair price of ingredients.

[0005] The system according to the embodiment aims to accurately evaluate the quality and fair price of ingredients. [Means for solving the problem]

[0006] The system according to the embodiment includes a quality evaluation unit and a price confirmation unit. The quality evaluation unit evaluates the quality of ingredients using a generation AI. The price confirmation unit analyzes market price data for the ingredients evaluated by the quality evaluation unit and confirms the appropriate price. [Effects of the Invention]

[0007] The system according to the embodiment can accurately evaluate the quality and fair price of ingredients. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A generative AI service according to an embodiment of the present invention is a system for checking the quality and fair price of ingredients. This system is provided to restaurants, food retailers, and consumers, and helps them determine whether ingredients are inexpensive or expensive, as well as their freshness and fair price. This allows the generative AI service to efficiently check the quality and fair price of ingredients.

[0029] The generation AI service according to the embodiment includes a quality evaluation unit and a price confirmation unit. The quality evaluation unit uses the generation AI to evaluate the quality of ingredients. For example, the generation AI analyzes images and data of ingredients to evaluate their quality. The quality evaluation unit can also evaluate freshness and quality by having the generation AI analyze the color, shape, surface condition, etc. of the ingredients. For example, the generation AI receives images of vegetables as input, analyzes their color, shape, and surface condition, and evaluates their freshness and quality. The quality evaluation unit can also analyze images and data of high-quality ingredients to evaluate their quality and value. For example, the generation AI receives images of high-quality meat or fish as input, analyzes their color, fat distribution, meat quality, etc., and evaluates their quality and value. The price confirmation unit analyzes market price data of the ingredients evaluated by the quality evaluation unit to confirm the appropriate price. For example, the generation AI analyzes market price data of ingredients to confirm the appropriate price. The price confirmation unit can also receive market price data of a specific vegetable as input, analyze the data, and determine whether the price is appropriate by comparing it with the current market price. For example, the generation AI receives market price data for a specific vegetable as input, analyzes the data, and compares it with the current market price to determine whether the price is fair. This allows the generation AI service according to the embodiment to efficiently check the quality and fair price of ingredients.

[0030] The quality evaluation unit can non-destructively evaluate the internal structure using ultrasound data in addition to image analysis of food ingredients. For example, the quality evaluation unit uses generation AI to simultaneously analyze the external image and ultrasound data of fruit to evaluate the internal ripeness. For example, the external image and ultrasound data of an apple are input, and the generation AI estimates the internal ripeness. The quality evaluation unit can also use generation AI to simultaneously analyze the external image and ultrasound data of vegetables to evaluate the internal quality. For example, the external image and ultrasound data of a tomato are input, and the generation AI evaluates the internal quality. The quality evaluation unit can also use generation AI to simultaneously analyze the external image and ultrasound data of meat to evaluate the internal quality. For example, the external image and ultrasound data of beef are input, and the generation AI evaluates the internal quality. This allows the internal structure of food ingredients to be evaluated non-destructively.

[0031] The quality evaluation unit can evaluate the quality of ingredients based on information about the season or place of origin. For example, the quality evaluation unit uses generation AI to consider information about the season and place of origin and evaluate the quality of the same type of fruit under different conditions. For example, the quality of apples in summer and winter can be compared. The quality evaluation unit can also use generation AI to consider information about the season and place of origin and evaluate the quality of vegetables. For example, the quality of tomatoes in spring and autumn can be compared. The quality evaluation unit can also use generation AI to consider information about the season and place of origin and evaluate the quality of meat. For example, the quality of beef produced in different regions can be compared. This makes it possible to evaluate the quality of ingredients by considering information about the season and place of origin.

[0032] The quality evaluation unit can analyze data on the cooking process and evaluate the quality of ingredients, including their condition after cooking. For example, the quality evaluation unit uses a generation AI to analyze the condition of ingredients before and after cooking and perform a quality evaluation. For example, to evaluate the doneness of a steak, the condition of meat before cooking is compared with the condition after cooking. The quality evaluation unit can also use a generation AI to analyze the condition of vegetables before and after cooking and perform a quality evaluation. For example, the condition of tomatoes before and after cooking is compared. The quality evaluation unit can also use a generation AI to analyze the condition of fish before and after cooking and perform a quality evaluation. For example, the condition of salmon before and after cooking is compared. This makes it possible to evaluate the quality of ingredients, including their condition after cooking.

[0033] The quality evaluation unit can also be applied to the quality evaluation of pet food or animal feed. The quality evaluation unit, for example, uses the generation AI to perform quality evaluation of pet food. For example, the quality of the raw materials for dog food is evaluated to select a product that is suitable for the health of pets. The quality evaluation unit can also use the generation AI to perform quality evaluation of animal feed. For example, the quality of the raw materials for livestock feed is evaluated to select a product that is suitable for the health of livestock. The quality evaluation unit can also use the generation AI to perform quality evaluation of fish feed. For example, the quality of the raw materials for fish feed is evaluated to select a product that is suitable for the health of fish. This makes it applicable to the quality evaluation of pet food and animal feed.

[0034] The price confirmation unit can analyze past price data and current market trends to predict future price fluctuations. For example, the price confirmation unit uses generation AI to analyze past price data and current market trends to predict future vegetable price fluctuations. For example, future prices are predicted based on past tomato price data and current market trends. The price confirmation unit can also use generation AI to analyze past price data and current market trends to predict future fruit price fluctuations. For example, future prices are predicted based on past apple price data and current market trends. The price confirmation unit can also use generation AI to analyze past price data and current market trends to predict future meat price fluctuations. For example, future prices are predicted based on past beef price data and current market trends. This makes it possible to predict future price fluctuations.

[0035] The price confirmation unit can evaluate the fair price of ingredients based on the costs of the entire supply chain. For example, the price confirmation unit uses generation AI to evaluate the fair price of vegetables, taking into account the costs of the entire supply chain. For example, it calculates the fair price of tomatoes, including transportation and storage costs. The price confirmation unit can also use generation AI to evaluate the fair price of fruit, taking into account the costs of the entire supply chain. For example, it calculates the fair price of apples, including transportation and storage costs. The price confirmation unit can also use generation AI to evaluate the fair price of meat, taking into account the costs of the entire supply chain. For example, it calculates the fair price of beef, including transportation and storage costs. This makes it possible to evaluate the fair price by taking into account the costs of the entire supply chain.

[0036] The price verification unit can evaluate the fair price of ingredients from a global perspective by comparing them with market prices in different regions or countries. For example, the price verification unit can use generation AI to compare market prices in different regions or countries to evaluate the fair price of vegetables from a global perspective. For example, the market prices of tomatoes in Japan and the United States can be compared. The price verification unit can also use generation AI to compare them with market prices in different regions or countries to evaluate the fair price of fruits from a global perspective. For example, the market prices of apples in Japan and France can be compared. The price verification unit can also use generation AI to compare them with market prices in different regions or countries to evaluate the fair price of meat from a global perspective. For example, the market prices of beef in Japan and Australia can be compared. This makes it possible to evaluate fair prices from a global perspective.

[0037] The price confirmation unit can update the fair price of ingredients in real time by linking with data from online marketplaces or auction sites. The price confirmation unit, for example, uses generation AI to link with data from online marketplaces and update the fair price of vegetables in real time. For example, it calculates a fair price based on price data for tomatoes from Amazon or Rakuten. The price confirmation unit can also use generation AI to link with data from auction sites and update the fair price of fruit in real time. For example, it calculates a fair price based on price data for apples from Yahoo! Auctions. The price confirmation unit can also use generation AI to link with data from online marketplaces and auction sites and update the fair price of meat in real time. For example, it calculates a fair price based on price data for beef from Sotheby's. This allows the fair price to be updated in real time.

[0038] The quality evaluation unit can analyze the origin certificate or authentication information of the luxury food ingredient and evaluate its reliability. The quality evaluation unit can, for example, use the generation AI to analyze the origin certificate or authentication information of the luxury food ingredient and evaluate its reliability. For example, it can analyze the origin certificate of Wagyu beef and confirm its reliability. The quality evaluation unit can also use the generation AI to analyze the authentication information of the luxury food ingredient and evaluate its reliability. For example, it can analyze organic certification information and confirm its reliability. The quality evaluation unit can also use the generation AI to build a system for analyzing the origin certificate or authentication information of the luxury food ingredient and evaluate its reliability. For example, it can analyze a certificate from a third-party organization and confirm its reliability. This allows the reliability of the luxury food ingredient to be evaluated.

[0039] The quality evaluation unit can analyze data on the preservation methods or distribution processes of luxury ingredients and evaluate quality fluctuations. For example, the quality evaluation unit uses generation AI to analyze data on the preservation methods or distribution processes of luxury ingredients and evaluate quality fluctuations. For example, the preservation methods and distribution processes of Wagyu beef are analyzed and quality fluctuations are evaluated. The quality evaluation unit can also use generation AI to analyze data on the preservation methods and distribution processes of luxury ingredients and evaluate quality fluctuations. For example, the preservation methods and distribution processes of fish are analyzed and quality fluctuations are evaluated. The quality evaluation unit can also use generation AI to analyze data on the preservation methods and distribution processes of luxury ingredients and build a system for evaluating quality fluctuations. For example, the preservation methods and distribution processes of fruit are analyzed and quality fluctuations are evaluated. This makes it possible to evaluate quality fluctuations of luxury ingredients.

[0040] The quality evaluation unit can also apply the evaluation of luxury ingredients to the evaluation of their value as gifts or gift items. For example, the quality evaluation unit uses generation AI to apply the evaluation of luxury ingredients to the evaluation of their value as gifts or gift items. For example, the value of a Wagyu beef gift set can be evaluated to strengthen a marketing strategy. The quality evaluation unit can also use generation AI to apply the evaluation of luxury ingredients to the evaluation of their value as gifts or gift items. For example, the value of a fruit gift set can be evaluated to strengthen a marketing strategy. The quality evaluation unit can also use generation AI to build a system for applying the evaluation of luxury ingredients to the evaluation of their value as gifts or gift items. For example, the value of a fish gift set can be evaluated to strengthen a marketing strategy. This makes it possible to apply the evaluation of luxury ingredients to the evaluation of their value as gifts or gift items.

[0041] The quality evaluation unit can utilize the evaluations of luxury ingredients in developing menus for restaurants or hotels. For example, using generative AI, the quality evaluation unit utilizes the evaluations of luxury ingredients in developing menus for restaurants or hotels. For example, developing a new steak menu based on the evaluation of Wagyu beef. The quality evaluation unit can also utilize the evaluations of luxury ingredients in developing menus for restaurants or hotels using generative AI. For example, developing a new sushi menu based on the evaluation of fish. The quality evaluation unit can also use generative AI to build a system for utilizing the evaluations of luxury ingredients in developing menus for restaurants or hotels. For example, developing a new dessert menu based on the evaluation of fruit. In this way, the evaluations of luxury ingredients can be utilized in developing menus for restaurants and hotels.

[0042] The quality evaluation unit can analyze sales data and inventory data for each store and propose optimal purchase quantities. For example, the quality evaluation unit can use generation AI to analyze sales data and inventory data for each store and propose optimal vegetable purchase quantities. For example, it calculates the optimal purchase quantity based on sales data and inventory data for tomatoes. The quality evaluation unit can also use generation AI to analyze sales data and inventory data for each store and propose optimal fruit purchase quantities. For example, it calculates the optimal purchase quantity based on sales data and inventory data for apples. The quality evaluation unit can also use generation AI to analyze sales data and inventory data for each store and propose optimal meat purchase quantities. For example, it calculates the optimal purchase quantity based on sales data and inventory data for beef. In this way, it is possible to analyze sales data and inventory data for each store and propose optimal purchase quantities.

[0043] The quality evaluation unit can analyze a consumer's purchase history and recommend the most suitable product to each individual customer. For example, the quality evaluation unit can use generation AI to analyze a consumer's purchase history and recommend the most suitable vegetable to each individual customer. For example, it can recommend fresh tomatoes to a customer who has frequently purchased tomatoes in the past. The quality evaluation unit can also use generation AI to analyze a consumer's purchase history and recommend the most suitable fruit to each individual customer. For example, it can recommend fresh apples to a customer who has frequently purchased apples in the past. The quality evaluation unit can also use generation AI to analyze a consumer's purchase history and recommend the most suitable meat to each individual customer. For example, it can recommend fresh beef to a customer who has frequently purchased beef in the past. In this way, it is possible to analyze a consumer's purchase history and recommend the most suitable product to each individual customer.

[0044] The quality evaluation unit can work in cooperation with an online shopping platform to update the inventory status in real time. The quality evaluation unit, for example, uses a generation AI to work in cooperation with an online shopping platform to update the inventory status of vegetables in real time. For example, the inventory status of tomatoes is reflected in real time. The quality evaluation unit can also work in cooperation with an online shopping platform to update the inventory status of fruits in real time using a generation AI. For example, the inventory status of apples is reflected in real time. The quality evaluation unit can also work in cooperation with an online shopping platform to update the inventory status of meat in real time using a generation AI. For example, the inventory status of beef is reflected in real time. This allows the inventory status to be updated in real time.

[0045] The quality evaluation unit can provide a data analysis service for optimizing a store layout or display method. The quality evaluation unit can, for example, use a generative AI to provide a data analysis service for optimizing a store layout or display method. For example, optimizing the display method of vegetables to increase sales. The quality evaluation unit can also use a generative AI to provide a data analysis service for optimizing a store layout or display method. For example, optimizing the display method of fruits to increase sales. The quality evaluation unit can also use a generative AI to build a system for providing a data analysis service for optimizing a store layout or display method. For example, optimizing the display method of meat to increase sales. This makes it possible to optimize the store layout and display method.

[0046] The quality evaluation unit can analyze a consumer's food selection trends and recommend food ingredients based on individual preferences. For example, the quality evaluation unit uses generation AI to analyze a consumer's food selection trends and recommend vegetables based on individual preferences. For example, it can recommend fresh tomatoes to a consumer who has frequently purchased tomatoes in the past. The quality evaluation unit can also use generation AI to analyze a consumer's food selection trends and recommend fruits based on individual preferences. For example, it can recommend fresh apples to a consumer who has frequently purchased apples in the past. The quality evaluation unit can also use generation AI to analyze a consumer's food selection trends and recommend meat based on individual preferences. For example, it can recommend fresh beef to a consumer who has frequently purchased beef in the past. This makes it possible to recommend optimal food ingredients based on the consumer's preferences.

[0047] The quality evaluation unit can analyze the nutritional value or health benefits of ingredients and provide consumers with information useful for health management. The quality evaluation unit can, for example, use generation AI to analyze the nutritional value or health benefits of ingredients and provide consumers with information useful for health management. For example, the nutritional value of tomatoes can be analyzed and the health benefits explained. The quality evaluation unit can also use generation AI to analyze the nutritional value and health benefits of ingredients and provide consumers with information useful for health management. For example, the nutritional value of apples can be analyzed and the health benefits explained. The quality evaluation unit can also use generation AI to build a system for analyzing the nutritional value and health benefits of ingredients and providing consumers with information useful for health management. For example, the nutritional value of beef can be analyzed and the health benefits explained. This makes it possible to provide consumers with information useful for health management.

[0048] The quality evaluation unit can work in cooperation with a recipe suggestion service to suggest dishes using the purchased ingredients. The quality evaluation unit can, for example, use a generation AI to work in cooperation with the recipe suggestion service to suggest dishes using the purchased vegetables. For example, it can suggest salad or pasta recipes using tomatoes. The quality evaluation unit can also work in cooperation with a recipe suggestion service to suggest dishes using the purchased fruits. For example, it can suggest dessert recipes using apples. The quality evaluation unit can also work in cooperation with a recipe suggestion service to suggest dishes using the purchased meat. For example, it can suggest steak or stew recipes using beef. This makes it possible to suggest dishes using the purchased ingredients.

[0049] The quality evaluation unit can analyze food storage or cooking methods and suggest the optimal method to the consumer. For example, the quality evaluation unit can use a generation AI to analyze food storage or cooking methods and suggest the optimal method to the consumer. For example, it can suggest a method for storing or cooking tomatoes. The quality evaluation unit can also use a generation AI to analyze food storage or cooking methods and suggest the optimal method to the consumer. For example, it can suggest a method for storing or cooking apples. The quality evaluation unit can also use a generation AI to build a system for analyzing food storage or cooking methods and suggest the optimal method to the consumer. For example, it can suggest a method for storing or cooking beef. This makes it possible to suggest the optimal storage or cooking method to the consumer.

[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 quality evaluation unit can analyze the nutritional value of ingredients and provide health information to consumers. For example, generative AI can be used to analyze the nutritional value of vegetables and evaluate their vitamin and mineral content. The quality evaluation unit can also analyze the nutritional value of fruits and evaluate their antioxidant and dietary fiber content. Furthermore, the quality evaluation unit can analyze the nutritional value of meat and evaluate its protein and fat content. This allows consumers to make healthy choices based on the nutritional value of ingredients.

[0052] The quality evaluation unit can analyze allergen information for ingredients and notify consumers of allergy risks. For example, generative AI can be used to analyze allergen information for vegetables and identify ingredients that may cause allergies. The quality evaluation unit can also analyze allergen information for fruits and identify ingredients that may cause allergies. Furthermore, the quality evaluation unit can analyze allergen information for meat and identify ingredients that may cause allergies. This allows consumers to obtain information to avoid allergy risks.

[0053] The quality evaluation unit can analyze the environmental impact of food production processes and encourage consumers to make eco-friendly choices. For example, generative AI can be used to evaluate the amount of water used and CO2 emissions in the vegetable production process. The quality evaluation unit can also evaluate the environmental impact of fruit production processes and encourage eco-friendly choices. Furthermore, the quality evaluation unit can evaluate the environmental impact of meat production processes and encourage eco-friendly choices. This allows consumers to make environmentally conscious choices.

[0054] The quality evaluation unit can analyze food storage methods and suggest optimal storage conditions. For example, generative AI can be used to analyze vegetable storage methods and suggest optimal temperature and humidity. The quality evaluation unit can also analyze fruit storage methods and suggest optimal storage conditions. Furthermore, the quality evaluation unit can analyze meat storage methods and suggest optimal storage conditions. This allows consumers to obtain information on how to keep food fresh for a long period of time.

[0055] The quality evaluation unit can analyze cooking methods for ingredients and suggest the optimal cooking method. For example, generative AI can be used to analyze cooking methods for vegetables and suggest cooking methods that maximize nutritional value. The quality evaluation unit can also analyze cooking methods for fruits and suggest cooking methods that maximize flavor. Furthermore, the quality evaluation unit can analyze cooking methods for meat and suggest cooking methods that maximize tenderness and juiciness. This allows consumers to obtain information on how to optimally cook ingredients.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The quality evaluation unit uses the generation AI to evaluate the quality of the ingredients. The generation AI analyzes images and data of the ingredients to evaluate their quality. Specifically, the generation AI analyzes the color, shape, and surface condition of the ingredients to evaluate their freshness and quality. For example, it receives images of vegetables as input, analyzes their color, shape, and surface condition, and evaluates their freshness and quality. It can also analyze images and data of high-quality ingredients to evaluate their quality and value. For example, it receives images of high-quality meat or fish as input, analyzes their color, fat distribution, meat texture, etc., and evaluates their quality and value. Step 2: The price confirmation unit analyzes the market price data of the ingredients evaluated by the quality evaluation unit and confirms the appropriate price. The generation AI analyzes the market price data of the ingredients and confirms their appropriate price. Specifically, it receives market price data for a specific vegetable as input, analyzes the data, and compares it with the current market price to determine whether the price is appropriate.

[0058] (Example 2) A generative AI service according to an embodiment of the present invention is a system for checking the quality and fair price of ingredients. This system is provided to restaurants, food retailers, and consumers, and helps them determine whether ingredients are inexpensive or expensive, as well as their freshness and fair price. This allows the generative AI service to efficiently check the quality and fair price of ingredients.

[0059] The generation AI service according to the embodiment includes a quality evaluation unit and a price confirmation unit. The quality evaluation unit uses the generation AI to evaluate the quality of ingredients. For example, the generation AI analyzes images and data of ingredients to evaluate their quality. The quality evaluation unit can also evaluate freshness and quality by having the generation AI analyze the color, shape, surface condition, etc. of the ingredients. For example, the generation AI receives images of vegetables as input, analyzes their color, shape, and surface condition, and evaluates their freshness and quality. The quality evaluation unit can also analyze images and data of high-quality ingredients to evaluate their quality and value. For example, the generation AI receives images of high-quality meat or fish as input, analyzes their color, fat distribution, meat quality, etc., and evaluates their quality and value. The price confirmation unit analyzes market price data of the ingredients evaluated by the quality evaluation unit to confirm the appropriate price. For example, the generation AI analyzes market price data of ingredients to confirm the appropriate price. The price confirmation unit can also receive market price data of a specific vegetable as input, analyze the data, and determine whether the price is appropriate by comparing it with the current market price. For example, the generation AI receives market price data for a specific vegetable as input, analyzes the data, and compares it with the current market price to determine whether the price is fair. This allows the generation AI service according to the embodiment to efficiently check the quality and fair price of ingredients.

[0060] The quality evaluation unit can non-destructively evaluate the internal structure using ultrasound data in addition to image analysis of food ingredients. For example, the quality evaluation unit uses generation AI to simultaneously analyze the external image and ultrasound data of fruit to evaluate the internal ripeness. For example, the external image and ultrasound data of an apple are input, and the generation AI estimates the internal ripeness. The quality evaluation unit can also use generation AI to simultaneously analyze the external image and ultrasound data of vegetables to evaluate the internal quality. For example, the external image and ultrasound data of a tomato are input, and the generation AI evaluates the internal quality. The quality evaluation unit can also use generation AI to simultaneously analyze the external image and ultrasound data of meat to evaluate the internal quality. For example, the external image and ultrasound data of beef are input, and the generation AI evaluates the internal quality. This allows the internal structure of food ingredients to be evaluated non-destructively.

[0061] The quality evaluation unit can evaluate the quality of ingredients based on information about the season or place of origin. For example, the quality evaluation unit uses generation AI to consider information about the season and place of origin and evaluate the quality of the same type of fruit under different conditions. For example, the quality of apples in summer and winter can be compared. The quality evaluation unit can also use generation AI to consider information about the season and place of origin and evaluate the quality of vegetables. For example, the quality of tomatoes in spring and autumn can be compared. The quality evaluation unit can also use generation AI to consider information about the season and place of origin and evaluate the quality of meat. For example, the quality of beef produced in different regions can be compared. This makes it possible to evaluate the quality of ingredients by considering information about the season and place of origin.

[0062] The quality evaluation unit can estimate the user's emotions and adjust the quality evaluation standards based on those emotions. For example, the quality evaluation unit uses a generation AI to analyze the user's emotions toward the quality of ingredients in real time and adjust the quality evaluation standards based on those emotions. For example, if the user has positive emotions toward the quality of apples, the standard is set high. The quality evaluation unit can also use a generation AI to analyze the user's emotions toward the quality of vegetables in real time and adjust the quality evaluation standards based on those emotions. For example, if the user has negative emotions toward the quality of tomatoes, the standard is set low. The quality evaluation unit can also use a generation AI to analyze the user's emotions toward the quality of meat in real time and adjust the quality evaluation standards based on those emotions. For example, if the user has positive emotions toward the quality of beef, the standard is set high. This allows the quality evaluation standards to be adjusted based on the user's emotions.

[0063] The quality evaluation unit can analyze data on the cooking process and evaluate the quality of ingredients, including their condition after cooking. For example, the quality evaluation unit uses a generation AI to analyze the condition of ingredients before and after cooking and perform a quality evaluation. For example, to evaluate the doneness of a steak, the condition of meat before cooking is compared with the condition after cooking. The quality evaluation unit can also use a generation AI to analyze the condition of vegetables before and after cooking and perform a quality evaluation. For example, the condition of tomatoes before and after cooking is compared. The quality evaluation unit can also use a generation AI to analyze the condition of fish before and after cooking and perform a quality evaluation. For example, the condition of salmon before and after cooking is compared. This makes it possible to evaluate the quality of ingredients, including their condition after cooking.

[0064] The quality evaluation unit can also be applied to the quality evaluation of pet food or animal feed. The quality evaluation unit, for example, uses the generation AI to perform quality evaluation of pet food. For example, the quality of the raw materials for dog food is evaluated to select a product that is suitable for the health of pets. The quality evaluation unit can also use the generation AI to perform quality evaluation of animal feed. For example, the quality of the raw materials for livestock feed is evaluated to select a product that is suitable for the health of livestock. The quality evaluation unit can also use the generation AI to perform quality evaluation of fish feed. For example, the quality of the raw materials for fish feed is evaluated to select a product that is suitable for the health of fish. This makes it applicable to the quality evaluation of pet food and animal feed.

[0065] The quality evaluation unit can analyze the emotions of consumers when selecting ingredients in real time and recommend the most suitable ingredients based on those emotions. For example, the quality evaluation unit can use generation AI to analyze the emotions of consumers when selecting ingredients in real time and recommend the most suitable ingredients based on those emotions. For example, it can prioritize recommending ingredients that the consumer feels positive about. The quality evaluation unit can also use generation AI to analyze the emotions of consumers when selecting vegetables in real time and recommend the most suitable vegetables based on those emotions. For example, it can recommend tomatoes that the consumer feels positive about. The quality evaluation unit can also use generation AI to analyze the emotions of consumers when selecting meat in real time and recommend the most suitable meat based on those emotions. For example, it can recommend beef that the consumer feels positive about. This makes it possible to recommend the most suitable ingredients based on the consumer's emotions.

[0066] The price confirmation unit can analyze past price data and current market trends to predict future price fluctuations. For example, the price confirmation unit uses generation AI to analyze past price data and current market trends to predict future vegetable price fluctuations. For example, future prices are predicted based on past tomato price data and current market trends. The price confirmation unit can also use generation AI to analyze past price data and current market trends to predict future fruit price fluctuations. For example, future prices are predicted based on past apple price data and current market trends. The price confirmation unit can also use generation AI to analyze past price data and current market trends to predict future meat price fluctuations. For example, future prices are predicted based on past beef price data and current market trends. This makes it possible to predict future price fluctuations.

[0067] The price confirmation unit can evaluate the fair price of ingredients based on the costs of the entire supply chain. For example, the price confirmation unit uses generation AI to evaluate the fair price of vegetables, taking into account the costs of the entire supply chain. For example, it calculates the fair price of tomatoes, including transportation and storage costs. The price confirmation unit can also use generation AI to evaluate the fair price of fruit, taking into account the costs of the entire supply chain. For example, it calculates the fair price of apples, including transportation and storage costs. The price confirmation unit can also use generation AI to evaluate the fair price of meat, taking into account the costs of the entire supply chain. For example, it calculates the fair price of beef, including transportation and storage costs. This makes it possible to evaluate the fair price by taking into account the costs of the entire supply chain.

[0068] The price confirmation unit can analyze consumer sentiment toward prices and adjust pricing strategies based on those sentiments. For example, the price confirmation unit can use a generation AI to analyze consumer sentiment toward prices in real time and adjust pricing strategies based on those sentiments. For example, it can set a price range at which consumers have positive sentiments. The price confirmation unit can also use a generation AI to analyze consumer sentiment toward vegetable prices in real time and adjust pricing strategies based on those sentiments. For example, it can set a price range for tomatoes at which consumers have positive sentiments. The price confirmation unit can also use a generation AI to analyze consumer sentiment toward meat prices in real time and adjust pricing strategies based on those sentiments. For example, it can set a price range for beef at which consumers have positive sentiments. This makes it possible to adjust pricing strategies based on consumer sentiment.

[0069] The price verification unit can evaluate the fair price of ingredients from a global perspective by comparing them with market prices in different regions or countries. For example, the price verification unit can use generation AI to compare market prices in different regions or countries to evaluate the fair price of vegetables from a global perspective. For example, the market prices of tomatoes in Japan and the United States can be compared. The price verification unit can also use generation AI to compare them with market prices in different regions or countries to evaluate the fair price of fruits from a global perspective. For example, the market prices of apples in Japan and France can be compared. The price verification unit can also use generation AI to compare them with market prices in different regions or countries to evaluate the fair price of meat from a global perspective. For example, the market prices of beef in Japan and Australia can be compared. This makes it possible to evaluate fair prices from a global perspective.

[0070] The price confirmation unit can update the fair price of ingredients in real time by linking with data from online marketplaces or auction sites. The price confirmation unit, for example, uses generation AI to link with data from online marketplaces and update the fair price of vegetables in real time. For example, it calculates a fair price based on price data for tomatoes from Amazon or Rakuten. The price confirmation unit can also use generation AI to link with data from auction sites and update the fair price of fruit in real time. For example, it calculates a fair price based on price data for apples from Yahoo! Auctions. The price confirmation unit can also use generation AI to link with data from online marketplaces and auction sites and update the fair price of meat in real time. For example, it calculates a fair price based on price data for beef from Sotheby's. This allows the fair price to be updated in real time.

[0071] The price confirmation unit can monitor consumers' feelings about prices in real time and conduct price negotiations based on that data. The price confirmation unit can, for example, use a generation AI to monitor consumers' feelings about prices in real time and conduct price negotiations based on that data. For example, it can set a price range at which consumers feel positive. The price confirmation unit can also use a generation AI to monitor consumers' feelings about vegetable prices in real time and conduct price negotiations based on that data. For example, it can set a price range for tomatoes at which consumers feel positive. The price confirmation unit can also use a generation AI to monitor consumers' feelings about meat prices in real time and conduct price negotiations based on that data. For example, it can set a price range for beef at which consumers feel positive. This makes it possible to conduct price negotiations based on consumer feelings.

[0072] The quality evaluation unit can analyze the origin certificate or authentication information of the luxury food ingredient and evaluate its reliability. The quality evaluation unit can, for example, use the generation AI to analyze the origin certificate or authentication information of the luxury food ingredient and evaluate its reliability. For example, it can analyze the origin certificate of Wagyu beef and confirm its reliability. The quality evaluation unit can also use the generation AI to analyze the authentication information of the luxury food ingredient and evaluate its reliability. For example, it can analyze organic certification information and confirm its reliability. The quality evaluation unit can also use the generation AI to build a system for analyzing the origin certificate or authentication information of the luxury food ingredient and evaluate its reliability. For example, it can analyze a certificate from a third-party organization and confirm its reliability. This allows the reliability of the luxury food ingredient to be evaluated.

[0073] The quality evaluation unit can analyze data on the preservation methods or distribution processes of luxury ingredients and evaluate quality fluctuations. For example, the quality evaluation unit uses generation AI to analyze data on the preservation methods or distribution processes of luxury ingredients and evaluate quality fluctuations. For example, the preservation methods and distribution processes of Wagyu beef are analyzed and quality fluctuations are evaluated. The quality evaluation unit can also use generation AI to analyze data on the preservation methods and distribution processes of luxury ingredients and evaluate quality fluctuations. For example, the preservation methods and distribution processes of fish are analyzed and quality fluctuations are evaluated. The quality evaluation unit can also use generation AI to analyze data on the preservation methods and distribution processes of luxury ingredients and build a system for evaluating quality fluctuations. For example, the preservation methods and distribution processes of fruit are analyzed and quality fluctuations are evaluated. This makes it possible to evaluate quality fluctuations of luxury ingredients.

[0074] The quality evaluation unit can analyze the emotions consumers have toward luxury ingredients and adjust the evaluation criteria based on those emotions. For example, the quality evaluation unit uses a generation AI to analyze the emotions consumers have toward luxury ingredients in real time and adjust the evaluation criteria based on those emotions. For example, the evaluation criteria for Wagyu beef, for which consumers have positive feelings, can be set high. The quality evaluation unit can also use a generation AI to analyze the emotions consumers have toward luxury ingredients in real time and adjust the evaluation criteria based on those emotions. For example, the evaluation criteria for fish, for which consumers have positive feelings, can be set high. The quality evaluation unit can also use a generation AI to build a system for analyzing the emotions consumers have toward luxury ingredients in real time and adjusting the evaluation criteria based on those emotions. For example, the evaluation criteria for fruit, for which consumers have positive feelings, can be set high. This makes it possible to adjust the evaluation criteria based on consumer emotions.

[0075] The quality evaluation unit can also apply the evaluation of luxury ingredients to the evaluation of their value as gifts or gift items. For example, the quality evaluation unit uses generation AI to apply the evaluation of luxury ingredients to the evaluation of their value as gifts or gift items. For example, the value of a Wagyu beef gift set can be evaluated to strengthen a marketing strategy. The quality evaluation unit can also use generation AI to apply the evaluation of luxury ingredients to the evaluation of their value as gifts or gift items. For example, the value of a fruit gift set can be evaluated to strengthen a marketing strategy. The quality evaluation unit can also use generation AI to build a system for applying the evaluation of luxury ingredients to the evaluation of their value as gifts or gift items. For example, the value of a fish gift set can be evaluated to strengthen a marketing strategy. This makes it possible to apply the evaluation of luxury ingredients to the evaluation of their value as gifts or gift items.

[0076] The quality evaluation unit can utilize the evaluations of luxury ingredients in developing menus for restaurants or hotels. For example, using generative AI, the quality evaluation unit utilizes the evaluations of luxury ingredients in developing menus for restaurants or hotels. For example, developing a new steak menu based on the evaluation of Wagyu beef. The quality evaluation unit can also utilize the evaluations of luxury ingredients in developing menus for restaurants or hotels using generative AI. For example, developing a new sushi menu based on the evaluation of fish. The quality evaluation unit can also use generative AI to build a system for utilizing the evaluations of luxury ingredients in developing menus for restaurants or hotels. For example, developing a new dessert menu based on the evaluation of fruit. In this way, the evaluations of luxury ingredients can be utilized in developing menus for restaurants and hotels.

[0077] The quality evaluation unit can analyze the emotions of consumers when selecting luxury ingredients in real time and recommend the most suitable ingredients based on those emotions. For example, using a generation AI, the quality evaluation unit can analyze the emotions of consumers when selecting luxury ingredients in real time and recommend the most suitable ingredients based on those emotions. For example, it can recommend Wagyu beef that the consumer feels positive about. The quality evaluation unit can also use a generation AI to analyze the emotions of consumers when selecting luxury ingredients in real time and recommend the most suitable ingredients based on those emotions. For example, it can recommend fish that the consumer feels positive about. The quality evaluation unit can also use a generation AI to analyze the emotions of consumers when selecting luxury ingredients in real time and build a system to recommend the most suitable ingredients based on those emotions. For example, it can recommend fruits that the consumer feels positive about. This makes it possible to recommend the most suitable luxury ingredients based on the consumer's emotions.

[0078] The quality evaluation unit can analyze sales data and inventory data for each store and propose optimal purchase quantities. For example, the quality evaluation unit can use generation AI to analyze sales data and inventory data for each store and propose optimal vegetable purchase quantities. For example, it calculates the optimal purchase quantity based on sales data and inventory data for tomatoes. The quality evaluation unit can also use generation AI to analyze sales data and inventory data for each store and propose optimal fruit purchase quantities. For example, it calculates the optimal purchase quantity based on sales data and inventory data for apples. The quality evaluation unit can also use generation AI to analyze sales data and inventory data for each store and propose optimal meat purchase quantities. For example, it calculates the optimal purchase quantity based on sales data and inventory data for beef. In this way, it is possible to analyze sales data and inventory data for each store and propose optimal purchase quantities.

[0079] The quality evaluation unit can analyze a consumer's purchase history and recommend the most suitable product to each individual customer. For example, the quality evaluation unit can use generation AI to analyze a consumer's purchase history and recommend the most suitable vegetable to each individual customer. For example, it can recommend fresh tomatoes to a customer who has frequently purchased tomatoes in the past. The quality evaluation unit can also use generation AI to analyze a consumer's purchase history and recommend the most suitable fruit to each individual customer. For example, it can recommend fresh apples to a customer who has frequently purchased apples in the past. The quality evaluation unit can also use generation AI to analyze a consumer's purchase history and recommend the most suitable meat to each individual customer. For example, it can recommend fresh beef to a customer who has frequently purchased beef in the past. In this way, it is possible to analyze a consumer's purchase history and recommend the most suitable product to each individual customer.

[0080] The quality evaluation unit can analyze the emotions that consumers have about their in-store purchasing experience in real time and improve services based on those emotions. The quality evaluation unit can, for example, use a generative AI to analyze the emotions that consumers have about their in-store purchasing experience in real time and improve services based on those emotions. For example, it can strengthen services that evoke positive emotions from consumers. The quality evaluation unit can also use a generative AI to analyze the emotions that consumers have about their in-store purchasing experience in real time and improve services based on those emotions. For example, it can improve services that evoke negative emotions from consumers. The quality evaluation unit can also use a generative AI to build a system that analyzes the emotions that consumers have about their in-store purchasing experience in real time and improve services based on those emotions. For example, it can strengthen services that evoke positive emotions from consumers and improve services that evoke negative emotions from consumers. This makes it possible to improve services based on consumer emotions.

[0081] The quality evaluation unit can work in cooperation with an online shopping platform to update the inventory status in real time. The quality evaluation unit, for example, uses a generation AI to work in cooperation with an online shopping platform to update the inventory status of vegetables in real time. For example, the inventory status of tomatoes is reflected in real time. The quality evaluation unit can also work in cooperation with an online shopping platform to update the inventory status of fruits in real time using a generation AI. For example, the inventory status of apples is reflected in real time. The quality evaluation unit can also work in cooperation with an online shopping platform to update the inventory status of meat in real time using a generation AI. For example, the inventory status of beef is reflected in real time. This allows the inventory status to be updated in real time.

[0082] The quality evaluation unit can provide a data analysis service for optimizing a store layout or display method. The quality evaluation unit can, for example, use a generative AI to provide a data analysis service for optimizing a store layout or display method. For example, optimizing the display method of vegetables to increase sales. The quality evaluation unit can also use a generative AI to provide a data analysis service for optimizing a store layout or display method. For example, optimizing the display method of fruits to increase sales. The quality evaluation unit can also use a generative AI to build a system for providing a data analysis service for optimizing a store layout or display method. For example, optimizing the display method of meat to increase sales. This makes it possible to optimize the store layout and display method.

[0083] The quality evaluation unit can monitor the emotions that consumers have about their in-store shopping experience in real time, and provide optimal services based on those emotions. For example, the quality evaluation unit can use a generation AI to monitor the emotions that consumers have about their in-store shopping experience in real time, and provide optimal services based on those emotions. For example, the quality evaluation unit can enhance services that evoke positive emotions in consumers. The quality evaluation unit can also use a generation AI to monitor the emotions that consumers have about their in-store shopping experience in real time, and provide optimal services based on those emotions. For example, the quality evaluation unit can improve services that evoke negative emotions in consumers. The quality evaluation unit can also use a generation AI to monitor the emotions that consumers have about their in-store shopping experience in real time, and build a system for providing optimal services based on those emotions. For example, the quality evaluation unit can enhance services that evoke positive emotions in consumers and improve services that evoke negative emotions in consumers. This makes it possible to provide optimal services based on consumer emotions.

[0084] The quality evaluation unit can analyze a consumer's food selection trends and recommend food ingredients based on individual preferences. For example, the quality evaluation unit uses generation AI to analyze a consumer's food selection trends and recommend vegetables based on individual preferences. For example, it can recommend fresh tomatoes to a consumer who has frequently purchased tomatoes in the past. The quality evaluation unit can also use generation AI to analyze a consumer's food selection trends and recommend fruits based on individual preferences. For example, it can recommend fresh apples to a consumer who has frequently purchased apples in the past. The quality evaluation unit can also use generation AI to analyze a consumer's food selection trends and recommend meat based on individual preferences. For example, it can recommend fresh beef to a consumer who has frequently purchased beef in the past. This makes it possible to recommend optimal food ingredients based on the consumer's preferences.

[0085] The quality evaluation unit can analyze the nutritional value or health benefits of ingredients and provide consumers with information useful for health management. The quality evaluation unit can, for example, use generation AI to analyze the nutritional value or health benefits of ingredients and provide consumers with information useful for health management. For example, the nutritional value of tomatoes can be analyzed and the health benefits explained. The quality evaluation unit can also use generation AI to analyze the nutritional value and health benefits of ingredients and provide consumers with information useful for health management. For example, the nutritional value of apples can be analyzed and the health benefits explained. The quality evaluation unit can also use generation AI to build a system for analyzing the nutritional value and health benefits of ingredients and providing consumers with information useful for health management. For example, the nutritional value of beef can be analyzed and the health benefits explained. This makes it possible to provide consumers with information useful for health management.

[0086] The quality evaluation unit can analyze the emotions of consumers when selecting ingredients in real time and recommend the most suitable ingredients based on those emotions. For example, using a generation AI, the quality evaluation unit can analyze the emotions of consumers when selecting ingredients in real time and recommend the most suitable ingredients based on those emotions. For example, it can recommend vegetables that evoke positive feelings from consumers. The quality evaluation unit can also use a generation AI to analyze the emotions of consumers when selecting ingredients in real time and recommend the most suitable ingredients based on those emotions. For example, it can recommend fruits that evoke positive feelings from consumers. The quality evaluation unit can also use a generation AI to build a system that analyzes the emotions of consumers when selecting ingredients in real time and recommends the most suitable ingredients based on those emotions. For example, it can recommend meat that evokes positive feelings from consumers. This makes it possible to recommend the most suitable ingredients based on the consumer's emotions.

[0087] The quality evaluation unit can work in cooperation with a recipe suggestion service to suggest dishes using the purchased ingredients. The quality evaluation unit can, for example, use a generation AI to work in cooperation with the recipe suggestion service to suggest dishes using the purchased vegetables. For example, it can suggest salad or pasta recipes using tomatoes. The quality evaluation unit can also work in cooperation with a recipe suggestion service to suggest dishes using the purchased fruits. For example, it can suggest dessert recipes using apples. The quality evaluation unit can also work in cooperation with a recipe suggestion service to suggest dishes using the purchased meat. For example, it can suggest steak or stew recipes using beef. This makes it possible to suggest dishes using the purchased ingredients.

[0088] The quality evaluation unit can analyze food storage or cooking methods and suggest the optimal method to the consumer. For example, the quality evaluation unit can use a generation AI to analyze food storage or cooking methods and suggest the optimal method to the consumer. For example, it can suggest a method for storing or cooking tomatoes. The quality evaluation unit can also use a generation AI to analyze food storage or cooking methods and suggest the optimal method to the consumer. For example, it can suggest a method for storing or cooking apples. The quality evaluation unit can also use a generation AI to build a system for analyzing food storage or cooking methods and suggest the optimal method to the consumer. For example, it can suggest a method for storing or cooking beef. This makes it possible to suggest the optimal storage or cooking method to the consumer.

[0089] The quality evaluation unit can monitor the emotions of consumers when selecting ingredients in real time and recommend the most suitable ingredients based on those emotions. For example, using a generation AI, the quality evaluation unit can monitor the emotions of consumers when selecting ingredients in real time and recommend the most suitable ingredients based on those emotions. For example, it can recommend vegetables that evoke positive feelings from the consumer. The quality evaluation unit can also use a generation AI to monitor the emotions of consumers when selecting ingredients in real time and recommend the most suitable ingredients based on those emotions. For example, it can recommend fruits that evoke positive feelings from the consumer. The quality evaluation unit can also use a generation AI to build a system for monitoring the emotions of consumers when selecting ingredients in real time and recommending the most suitable ingredients based on those emotions. For example, it can recommend meat that evokes positive feelings from the consumer. This makes it possible to recommend the most suitable ingredients based on the consumer's emotions.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The quality evaluation unit can analyze the nutritional value of ingredients and provide health information to consumers. For example, generative AI can be used to analyze the nutritional value of vegetables and evaluate their vitamin and mineral content. The quality evaluation unit can also analyze the nutritional value of fruits and evaluate their antioxidant and dietary fiber content. Furthermore, the quality evaluation unit can analyze the nutritional value of meat and evaluate its protein and fat content. This allows consumers to make healthy choices based on the nutritional value of ingredients.

[0092] The quality evaluation unit can analyze allergen information for ingredients and notify consumers of allergy risks. For example, generative AI can be used to analyze allergen information for vegetables and identify ingredients that may cause allergies. The quality evaluation unit can also analyze allergen information for fruits and identify ingredients that may cause allergies. Furthermore, the quality evaluation unit can analyze allergen information for meat and identify ingredients that may cause allergies. This allows consumers to obtain information to avoid allergy risks.

[0093] The quality evaluation unit can analyze the environmental impact of food production processes and encourage consumers to make eco-friendly choices. For example, generative AI can be used to evaluate the amount of water used and CO2 emissions in the vegetable production process. The quality evaluation unit can also evaluate the environmental impact of fruit production processes and encourage eco-friendly choices. Furthermore, the quality evaluation unit can evaluate the environmental impact of meat production processes and encourage eco-friendly choices. This allows consumers to make environmentally conscious choices.

[0094] The quality evaluation unit can analyze food storage methods and suggest optimal storage conditions. For example, generative AI can be used to analyze vegetable storage methods and suggest optimal temperature and humidity. The quality evaluation unit can also analyze fruit storage methods and suggest optimal storage conditions. Furthermore, the quality evaluation unit can analyze meat storage methods and suggest optimal storage conditions. This allows consumers to obtain information on how to keep food fresh for a long period of time.

[0095] The quality evaluation unit can analyze cooking methods for ingredients and suggest the optimal cooking method. For example, generative AI can be used to analyze cooking methods for vegetables and suggest cooking methods that maximize nutritional value. The quality evaluation unit can also analyze cooking methods for fruits and suggest cooking methods that maximize flavor. Furthermore, the quality evaluation unit can analyze cooking methods for meat and suggest cooking methods that maximize tenderness and juiciness. This allows consumers to obtain information on how to optimally cook ingredients.

[0096] The quality evaluation unit can estimate the consumer's emotions and support the selection of ingredients based on those emotions. For example, using generative AI, the system can analyze the consumer's emotions in real time when selecting vegetables and recommend the most suitable vegetables based on those emotions. The quality evaluation unit can also analyze the consumer's emotions in real time when selecting fruit and recommend the most suitable fruit based on those emotions. Furthermore, the quality evaluation unit can analyze the consumer's emotions in real time when selecting meat and recommend the most suitable meat based on those emotions. This allows the system to support the selection of the most suitable ingredients based on the consumer's emotions.

[0097] The quality evaluation unit can estimate consumer sentiment and adjust the quality evaluation standards for ingredients based on that sentiment. For example, using generative AI, the consumer's sentiment toward the quality of vegetables can be analyzed in real time, and the quality evaluation standards can be adjusted based on that sentiment. The quality evaluation unit can also analyze the consumer's sentiment toward the quality of fruit in real time, and adjust the quality evaluation standards based on that sentiment. Furthermore, the quality evaluation unit can analyze the consumer's sentiment toward the quality of meat in real time, and adjust the quality evaluation standards based on that sentiment. This makes it possible to adjust the quality evaluation standards based on consumer sentiment.

[0098] The quality evaluation unit can estimate consumer emotions and suggest food storage methods based on those emotions. For example, using generative AI, the system can analyze consumers' emotions regarding vegetable storage methods in real time and suggest the optimal storage method based on those emotions. The quality evaluation unit can also analyze consumers' emotions regarding fruit storage methods in real time and suggest the optimal storage method based on those emotions. The quality evaluation unit can also analyze consumers' emotions regarding meat storage methods in real time and suggest the optimal storage method based on those emotions. This makes it possible to suggest the optimal storage method based on consumer emotions.

[0099] The quality evaluation unit can estimate consumer emotions and suggest cooking methods for ingredients based on those emotions. For example, using generative AI, the system can analyze consumers' emotions toward vegetable cooking methods in real time and suggest the optimal cooking method based on those emotions. The quality evaluation unit can also analyze consumers' emotions toward fruit cooking methods in real time and suggest the optimal cooking method based on those emotions. Furthermore, the quality evaluation unit can analyze consumers' emotions toward meat cooking methods in real time and suggest the optimal cooking method based on those emotions. This makes it possible to suggest the optimal cooking method based on the consumer's emotions.

[0100] The quality evaluation unit can estimate the consumer's emotions and support the selection of ingredients based on those emotions. For example, using generative AI, the system can analyze the consumer's emotions in real time when selecting vegetables and recommend the most suitable vegetables based on those emotions. The quality evaluation unit can also analyze the consumer's emotions in real time when selecting fruit and recommend the most suitable fruit based on those emotions. Furthermore, the quality evaluation unit can analyze the consumer's emotions in real time when selecting meat and recommend the most suitable meat based on those emotions. This allows the system to support the selection of the most suitable ingredients based on the consumer's emotions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The quality evaluation unit uses the generation AI to evaluate the quality of the ingredients. The generation AI analyzes images and data of the ingredients to evaluate their quality. Specifically, the generation AI analyzes the color, shape, and surface condition of the ingredients to evaluate their freshness and quality. For example, it receives images of vegetables as input, analyzes their color, shape, and surface condition, and evaluates their freshness and quality. It can also analyze images and data of high-quality ingredients to evaluate their quality and value. For example, it receives images of high-quality meat or fish as input, analyzes their color, fat distribution, meat texture, etc., and evaluates their quality and value. Step 2: The price confirmation unit analyzes the market price data of the ingredients evaluated by the quality evaluation unit and confirms the appropriate price. The generation AI analyzes the market price data of the ingredients and confirms their appropriate price. Specifically, it receives market price data for a specific vegetable as input, analyzes the data, and compares it with the current market price to determine whether the price is appropriate.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

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

[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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

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

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] 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.

[0133] 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.

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

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

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

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0149] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0151] The data processing system 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A quality evaluation unit that evaluates the quality of ingredients using generative AI; a price confirmation unit that analyzes the market price data of the food material evaluated by the quality evaluation unit and confirms the appropriate price. A system characterized by:

2. The quality evaluation unit Non-destructive evaluation of the internal structure of the food material using ultrasonic data in addition to image analysis. The system of claim 1 .

3. The quality evaluation unit Analyzing data from the cooking process and evaluating the quality of the ingredients, including their condition after cooking. The system of claim 1 .

4. The price confirmation unit Analyzing past price data and current market trends to predict future price fluctuations The system of claim 1 .

5. The quality evaluation unit Analyzing the origin certificate or certification information of luxury food ingredients and assessing their reliability 2. The system of claim 1.

6. The quality evaluation unit Analyzing sales and inventory data for each store and proposing optimal purchasing amounts The system of claim 1 .

7. The quality evaluation unit Inferring user sentiment and adjusting quality assessment criteria based on that sentiment The system of claim 1 .

8. The price confirmation unit Analyzing consumer sentiment towards prices and adjusting pricing strategies based on those sentiments The system of claim 1 .

Images