System

Abstract

An object of a system according to an embodiment is to effectively utilize food ingredients scheduled to be discarded and to suppress food discarding.SOLUTION: A system includes a photographing unit, an analysis unit, a menu generation unit, a matching unit, and a selection unit. The photographing unit photographs a food ingredient scheduled to be discarded. The analysis unit analyzes the photograph taken by the photographing unit and verbalizes the food ingredient. The menu generation unit generates a menu based on the food ingredient information verbalized by the analysis unit. The matching unit lists restaurants and individuals who want food ingredients. The selection unit selects the other party listed by the matching unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide means for effectively utilizing food waste generated at home or in business, and there is room for improvement.

[0005] The system according to the embodiment aims to effectively utilize ingredients that would otherwise be discarded and reduce food waste. [Means for solving the problem]

[0006] The system according to the embodiment includes a photographing unit, an analysis unit, a menu generation unit, a matching unit, and a selection unit. The photographing unit photographs ingredients to be discarded. The analysis unit analyzes the photos taken by the photographing unit and verbalizes the ingredients. The menu generation unit generates a menu based on the ingredient information verbalized by the analysis unit. The matching unit lists restaurants and individuals who want ingredients. The selection unit selects a partner listed by the matching unit. [Effects of the Invention]

[0007] The system according to the embodiment can effectively utilize ingredients that would otherwise be discarded, thereby reducing food waste. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A matching app according to an embodiment of the present invention is a system for effectively utilizing ingredients that would otherwise be discarded. In this system, a user takes a photo of ingredients that would otherwise be discarded and uploads the photo to the app. A generation AI analyzes the photo and verbalizes the ingredients. The generation AI then creates a menu from the ingredients. A list of restaurants and individuals wanting ingredients is displayed, and the user selects who they would like to give them to. Once the buyer and giver are matched, the user selects the method of delivery. While cash or points cannot be exchanged, electronic payment services can be used as tips after the transaction. This allows the matching app to effectively utilize ingredients that would otherwise be discarded and reduce food waste.

[0029] A matching app according to an embodiment includes a photography unit, an analysis unit, a menu generation unit, a matching unit, and a selection unit. The photography unit photographs ingredients to be discarded. For example, a user takes a photo of the ingredients using a smartphone camera. The photography unit may also have a function to automatically evaluate the freshness and condition of the ingredients. The analysis unit analyzes the photo taken by the photography unit and verbalizes the ingredients. For example, the generation AI identifies ingredients using image recognition technology and outputs their names as text data. The analysis unit may also have a function to provide nutritional value and allergy information for the ingredients. The menu generation unit generates a menu based on the ingredient information verbalized by the analysis unit. For example, the generation AI suggests ingredient combinations and cooking methods based on the verbalized ingredient information. The menu generation unit may also have a function to suggest personalized menus that take into account the user's past preferences and allergy information. The matching unit lists restaurants and individuals wanting ingredients. For example, the system suggests optimal partners based on the registered user's needs and preferences. The selection unit selects partners from the list by the matching unit. For example, a user can check the profiles and ratings of the listed partners and select the most suitable partner. This allows the matching app according to the embodiment to effectively utilize ingredients that would otherwise be discarded and reduce food waste. For example, a user can easily provide ingredients that would otherwise be discarded to other users, thereby reducing food waste. Furthermore, the system can promote communication between users and strengthen ties in the local community.

[0030] The photography unit can automatically evaluate the freshness and condition of ingredients. The photography unit, for example, introduces an image analysis algorithm to evaluate the freshness of ingredients. For example, it detects changes in color and shape and calculates a freshness score. The photography unit also uses the freshness evaluation function to suggest how to use the ingredients depending on their condition. For example, it suggests eating them raw if they are highly fresh, and how to cook them if they are not. The photography unit also notifies the user of the food storage method and expiration date based on the freshness evaluation results. For example, it displays a message if refrigeration is appropriate. This allows the optimal usage method to be suggested by evaluating the freshness and condition of ingredients.

[0031] The photography unit can add detailed information about ingredients through voice input. For example, when taking a photo, the photography unit provides an interface that allows the purchase date and storage method of ingredients to be added through voice input. For example, the user can input "I bought these tomatoes yesterday" through voice input. The photography unit also uses the voice input function to automatically convert the detailed information about ingredients into text and store it in a database. For example, it uses voice recognition technology to convert speech to text. The photography unit also suggests ways to use and store ingredients based on the information added through voice input. For example, it displays advice such as "It is best to store them in the refrigerator." This allows suggestions to be made about how to use and store ingredients by adding detailed information through voice input.

[0032] The photography unit automatically synthesizes photos from multiple angles, enabling more accurate ingredient recognition. For example, the photography unit introduces a function that automatically takes and synthesizes photos from multiple angles when taking photos. For example, a 360-degree camera is used to capture an overall image of the ingredient. The photography unit also develops an algorithm that more accurately recognizes the shape and characteristics of the ingredient based on the synthesized photos. For example, it generates a 3D model and analyzes the details of the ingredient. The photography unit also uses photos from multiple angles to more accurately evaluate the condition and freshness of the ingredient. For example, it analyzes changes in color and shape from different angles. This improves ingredient recognition accuracy by synthesizing photos from multiple angles.

[0033] The photography unit can receive real-time feedback from other users. For example, the photography unit introduces a function for receiving real-time feedback from other users when uploading a photo. For example, comments and ratings are displayed in real time. The photography unit also suggests ways to use and store ingredients based on feedback from other users. For example, it displays advice such as "These tomatoes are delicious in soup." The photography unit also uses the real-time feedback function to promote communication between users. For example, a chat function is added to exchange information about ingredients. This makes it possible to suggest ways to use and store ingredients based on feedback from other users.

[0034] The analysis unit can provide nutritional value and allergy information for ingredients. For example, the analysis unit will introduce a function that automatically adds nutritional value information when the generation AI verbalizes ingredients. For example, the nutritional value of "tomato" will be displayed as the content of vitamin C and lycopene. The analysis unit will also add a function that automatically provides allergy information and alerts the user. For example, it will display a warning such as "This ingredient contains allergens." The analysis unit will also develop a system that integrates nutritional value and allergy information into verbalized ingredient information and provides it to the user. For example, detailed information about ingredients will be displayed on a single screen. This will protect the user's health by providing nutritional value and allergy information for ingredients.

[0035] The analysis unit can provide an interface that allows the user to check the verbalization results and input corrections or additional information. The analysis unit, for example, provides an interface that allows the user to check the verbalization results and input corrections or additional information. For example, the analysis unit asks for confirmation by asking, "Is this ingredient a tomato?" The analysis unit also adds a function that allows the user to correct the verbalization results and provide accurate information. For example, the user could correct the result by saying, "This is not a tomato, it's a paprika." The analysis unit also provides an interface that allows the user to input additional information to the verbalization results and collect more detailed information. For example, the analysis unit adds information such as, "This tomato is organic." This allows the user to check the verbalization results and input corrections or additional information, thereby providing accurate information.

[0036] The analysis unit can share the verbalized ingredient information with other users and collaboratively devise menus. The analysis unit, for example, introduces a function for sharing the verbalized ingredient information with other users and collaboratively devising menus. For example, a chat function is used to exchange menu ideas. The analysis unit also provides an interface for collaboratively devising menus and promotes cooperation between users. For example, a collaborative editing function is added to create menus in real time. The analysis unit also develops a system for collaboratively proposing menus based on the ingredient information shared with other users. For example, menus proposed by multiple users are integrated. This allows users to collaborate on menus, thereby promoting the effective use of ingredients.

[0037] The analysis unit automatically translates the verbalized ingredient information into different languages ​​to obtain feedback from an international perspective. For example, the analysis unit automatically translates the verbalized ingredient information into different languages ​​to collect feedback from an international perspective. For example, the analysis unit translates into multiple languages ​​such as English, French, and Chinese. The analysis unit also builds a system that posts the translated ingredient information on a multilingual platform and obtains feedback from users around the world. The analysis unit also collects advice and improvement suggestions from an international perspective based on the ingredient information translated into different languages ​​to improve how ingredients are used. For example, the analysis unit reflects feedback that takes into account differences in culture and market. In this way, by translating into different languages, feedback from an international perspective can be obtained.

[0038] The menu generation unit can propose personalized menus that take into account the user's past preferences and allergy information. For example, the menu generation unit introduces a function that takes into account the user's past preferences when the generation AI creates a menu. For example, it may prioritize suggesting menus that have been well-received in the past. The menu generation unit also adds a menu suggestion function that takes allergy information into account to protect the user's health. For example, it may suggest menus that do not contain allergens. The menu generation unit will also develop a system that automatically generates personalized menus based on the user's past data. For example, it may analyze the user's dietary history and propose the optimal menu. This will allow it to propose personalized menus by taking into account the user's past preferences and allergy information.

[0039] The menu generation unit can propose the optimal cooking method by taking into account the shelf life and cooking time of ingredients. For example, the menu generation unit will introduce a function that considers the shelf life of ingredients when the generation AI creates a menu. For example, it will propose a menu that prioritizes the use of ingredients with a short shelf life. The menu generation unit will also add a menu suggestion function that takes cooking time into account, providing cooking methods that suit the user's time. For example, it will propose menus that can be cooked in a short amount of time. The menu generation unit will also develop a system that suggests the optimal cooking method based on the shelf life and cooking time of ingredients. For example, it will suggest menus that will be used at a later date for ingredients with a long shelf life. This will make it possible to propose the optimal cooking method by taking into account the shelf life and cooking time of ingredients.

[0040] The menu generation unit can refer to menus and recipes created by other users. For example, the menu generation unit will introduce a function to refer to menus and recipes created by other users, and the generation AI will use this when proposing menus. For example, popular recipes will be suggested preferentially. The menu generation unit will also store other users' menus and recipes in a database, and the generation AI will use them to create new menus. For example, recipes that have been highly rated by users will be suggested. The menu generation unit will also collect other users' menus and recipes in real time, and a system will be developed in which the generation AI will suggest menus based on this. For example, the latest trends will be reflected. This will allow a more diverse menu to be suggested by referring to menus and recipes created by other users.

[0041] The menu generation unit can convert the results of menu creation into visual notes or mind maps to make them easier to understand visually. For example, the menu generation unit introduces a function that converts the results of menu creation into visual notes to make them easier for users to understand visually. For example, it shows the main points of the menu with diagrams or icons. The menu generation unit also converts the results of menu creation into a mind map format to visually organize related keywords and concepts. This allows the overall picture of the menu to be understood at a glance. The menu generation unit also develops tools that automatically generate visual notes and mind maps to enable users to easily visually display the main points of the menu. For example, it provides a function to visualize menus using drag and drop. This visually displays the results of menu creation, making it easier for users to understand.

[0042] The matching unit can display the past transaction history and evaluation of listed counterparties, allowing users to confirm their trustworthiness. The matching unit, for example, introduces a function that displays the past transaction history of listed counterparties and allows users to confirm their trustworthiness. For example, it displays the number of past transactions and evaluation scores. The matching unit also provides an interface for confirming trustworthiness based on the counterparty's evaluation. For example, it displays comments and feedback from other users. The matching unit also develops a system that automatically evaluates trustworthiness based on transaction history and evaluation data. For example, it calculates a reliability score and presents it to the user. This allows users to confirm trustworthiness by displaying the counterparty's past transaction history and evaluation.

[0043] The matching unit can introduce a matching algorithm that takes into account the other party's needs and preferences, and suggest the most suitable partner. The matching unit, for example, introduces a matching algorithm that takes into account the other party's needs and preferences, and suggests the most suitable partner. For example, it predicts needs based on past transaction data. The matching unit also incorporates the user's preferences and ratings into the matching algorithm to develop a system that suggests the most suitable partner. For example, it prioritizes suggesting partners with high user ratings. The matching unit also provides an interface that suggests the most suitable partner based on needs and preferences. For example, it displays the other party's profile and needs, making it easier for the user to select. This makes it possible to suggest the most suitable partner by taking into account the other party's needs and preferences.

[0044] The matching unit can enable direct chat with listed parties. The matching unit, for example, introduces a function that enables direct chat with listed parties, enabling detailed information exchange. For example, a chat window is displayed and messages are sent and received in real time. The matching unit also uses the chat function to provide an interface for exchanging detailed information about transactions. For example, parties can discuss the status of ingredients and delivery methods. The matching unit also adds a function that saves chat history and allows it to be referenced later. For example, the details of past transactions can be checked and used for the next transaction. This makes it possible to exchange detailed information by chatting directly with the other party.

[0045] The matching unit can display the profiles and introduction videos of the listed partners. For example, the matching unit introduces a function that displays the profiles of the listed partners and allows the user to check detailed information. For example, it displays the partner's background and ratings. The matching unit also displays introduction videos and provides an interface that provides detailed information about the partner. For example, it uses videos to explain the partner's self-introduction and the transaction flow. The matching unit also develops a system that makes it easier for users to select partners based on the profiles and introduction videos. For example, it adds a function to allow users to rate the video after watching it. This makes it possible to provide detailed information by displaying the partner's profile and introduction video.

[0046] The selection unit can add an eco-friendly delivery option to the options for delivery methods. For example, the selection unit adds an eco-friendly delivery option to the options for delivery methods. For example, it suggests delivery using bicycle courier or electric vehicle. Furthermore, the selection unit introduces a function that displays the user's contribution to the environment when the eco-friendly delivery option is selected. For example, it displays the amount of CO2 reduction. Furthermore, the selection unit develops a system that provides points and benefits to users by selecting the eco-friendly delivery option. For example, points are accumulated each time eco-friendly delivery is selected. In this way, adding an eco-friendly delivery option can reduce the environmental burden.

[0047] The selection unit can track the delivery status in real time when selecting a delivery method. The selection unit provides a function that allows the delivery status to be tracked in real time when selecting a delivery method. For example, the location of the delivery vehicle is displayed using GPS. The selection unit also develops a system that tracks the delivery status in real time and notifies the user. For example, an alert is sent when delivery is approaching. The selection unit also provides an interface for tracking the delivery status so that the user can easily check it. For example, the delivery route is displayed on a map. This allows the user to track the delivery status in real time, allowing them to conduct transactions with peace of mind.

[0048] The selection unit can add a pickup option at a local community center to the options for delivery methods. For example, the selection unit adds a pickup option at a local community center to the options for delivery methods. For example, picking up ingredients at a specified community center. The selection unit also introduces a function that provides the user with information about local events and activities when the user selects the option for pickup at a community center. For example, local event information is displayed at the time of pickup. The selection unit also develops a system that provides points and benefits to the user when the option for pickup at a community center is selected. For example, points are accumulated each time pickup at a community center is selected. In this way, adding the option for pickup at a local community center can strengthen ties with the local community.

[0049] The selection unit can refer to reviews and ratings from other users when selecting a delivery method. For example, the selection unit introduces a function to display reviews and ratings from other users when selecting a delivery method. For example, it displays rating scores based on past transactions. The selection unit also provides an interface for selecting a delivery method based on reviews and ratings from other users. For example, it preferentially displays delivery methods with high ratings. The selection unit also develops a system that supports the selection of a delivery method based on reviews and ratings. For example, it recommends delivery methods with high user ratings. This allows the optimal delivery method to be selected by referring to reviews and ratings from other users.

[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 photography unit can automatically evaluate the freshness and condition of ingredients. For example, an image analysis algorithm is introduced to evaluate the freshness of ingredients. For example, changes in color and shape are detected and a freshness score is calculated. The photography unit also uses the freshness evaluation function to suggest how to use the ingredients depending on their condition. For example, if the freshness is high, it will suggest eating them raw, and if the freshness is low, it will suggest a cooking method. The photography unit also notifies the user of the food storage method and expiration date based on the freshness evaluation results. For example, if refrigeration is appropriate, it will display this information. This makes it possible to suggest the optimal way to use ingredients by evaluating their freshness and condition.

[0052] The photography unit can add detailed information about ingredients through voice input. For example, when taking a photo, an interface is provided that allows the purchase date and storage method of ingredients to be added through voice input. For example, "I bought these tomatoes yesterday" can be input by voice. The photography unit also uses the voice input function to automatically convert the detailed information about ingredients into text and store it in a database. For example, it uses voice recognition technology to convert speech to text. The photography unit also suggests ways to use and store ingredients based on the information added through voice input. For example, it displays advice such as "It is best to store them in the refrigerator." This allows suggestions to be made about how to use and store ingredients by adding detailed information through voice input.

[0053] The photography unit can automatically synthesize photos from multiple angles to enable more accurate ingredient recognition. For example, a function will be introduced to automatically take and synthesize photos from multiple angles when taking photos. For example, a 360-degree camera will be used to capture an overall image of the ingredient. The photography unit will then develop an algorithm to more accurately recognize the shape and characteristics of the ingredient based on the synthesized photos. For example, a 3D model will be generated and the details of the ingredient will be analyzed. The photography unit will also use photos from multiple angles to more accurately evaluate the condition and freshness of the ingredient. For example, it will analyze changes in color and shape from different angles. This will improve ingredient recognition accuracy by synthesizing photos from multiple angles.

[0054] The photography unit can receive real-time feedback from other users. For example, a function for receiving real-time feedback from other users when uploading a photo can be introduced. For example, comments and ratings can be displayed in real time. The photography unit can also suggest ways to use and store ingredients based on feedback from other users. For example, it can display advice such as "These tomatoes are delicious in soup." The photography unit can also use the real-time feedback function to promote communication between users. For example, a chat function can be added to exchange information about ingredients. This can make it possible to suggest ways to use and store ingredients based on feedback from other users.

[0055] The analysis unit can provide nutritional value and allergy information for ingredients. For example, a function will be introduced that automatically adds nutritional value information when the generation AI verbalizes ingredients. For example, the nutritional value of "tomato" will be displayed as the content of vitamin C and lycopene. The analysis unit will also add a function that automatically provides allergy information and alerts the user. For example, it will display a warning such as "This ingredient contains allergens." The analysis unit will also develop a system that integrates nutritional value and allergy information into verbalized ingredient information and provides it to the user. For example, detailed information about ingredients will be displayed on a single screen. This will protect the user's health by providing information on the nutritional value and allergy of ingredients.

[0056] The analysis unit can provide an interface that allows the user to check the verbalization results and input corrections or additional information. For example, an interface is provided that allows the user to check the verbalization results and input corrections or additional information. For example, the analysis unit may ask for confirmation by asking, "Is this ingredient a tomato?" The analysis unit may also add a function that allows the user to correct the verbalization results and provide accurate information. For example, the analysis unit may correct the verbalization results to say, "This is not a tomato, it's a paprika." The analysis unit may also provide an interface that allows additional information to be input into the verbalization results and collect more detailed information. For example, the analysis unit may add information such as, "This tomato is organic." This allows the user to check the verbalization results and input corrections or additional information, thereby providing accurate information.

[0057] The analysis unit can share the verbalized ingredient information with other users and collaboratively devise menus. For example, a function is introduced that allows users to share the verbalized ingredient information with other users and collaboratively devise menus. For example, a chat function is used to exchange menu ideas. The analysis unit also provides an interface for collaboratively devising menus and promotes cooperation between users. For example, a collaborative editing function is added to create menus in real time. The analysis unit also develops a system that collaboratively proposes menus based on the ingredient information shared with other users. For example, menus proposed by multiple users are integrated. This allows users to collaborate on menus, thereby promoting the effective use of ingredients.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The photographing unit photographs food items to be discarded. For example, a user may take a photo of the food items using a smartphone camera. The photographing unit may also have a function to automatically evaluate the freshness and condition of the food items. Step 2: The analysis unit analyzes the photos taken by the photography unit and verbalizes the ingredients. For example, the generation AI can use image recognition technology to identify ingredients and output their names as text data. The analysis unit can also provide information on the nutritional value and allergies of ingredients. Step 3: The menu generation unit generates a menu based on the ingredient information verbalized by the analysis unit. For example, the generation AI suggests ingredient combinations and cooking methods based on the verbalized ingredient information. The menu generation unit can also have a function to suggest personalized menus that take into account the user's past preferences and allergy information. Step 4: The matching unit creates a list of restaurants and individuals who want ingredients. For example, the system suggests the most suitable match based on the registered user's needs and preferences. Step 5: The selection unit selects the partner from the list by the matching unit. For example, the user checks the profiles and ratings of the partners in the list and selects the most suitable partner.

[0060] (Example 2) A matching app according to an embodiment of the present invention is a system for effectively utilizing ingredients that would otherwise be discarded. In this system, a user takes a photo of ingredients that would otherwise be discarded and uploads the photo to the app. A generation AI analyzes the photo and verbalizes the ingredients. The generation AI then creates a menu from the ingredients. A list of restaurants and individuals wanting ingredients is displayed, and the user selects who they would like to give them to. Once the buyer and giver are matched, the user selects the method of delivery. While cash or points cannot be exchanged, electronic payment services can be used as tips after the transaction. This allows the matching app to effectively utilize ingredients that would otherwise be discarded and reduce food waste.

[0061] A matching app according to an embodiment includes a photography unit, an analysis unit, a menu generation unit, a matching unit, and a selection unit. The photography unit photographs ingredients to be discarded. For example, a user takes a photo of the ingredients using a smartphone camera. The photography unit may also have a function to automatically evaluate the freshness and condition of the ingredients. The analysis unit analyzes the photo taken by the photography unit and verbalizes the ingredients. For example, the generation AI identifies ingredients using image recognition technology and outputs their names as text data. The analysis unit may also have a function to provide nutritional value and allergy information for the ingredients. The menu generation unit generates a menu based on the ingredient information verbalized by the analysis unit. For example, the generation AI suggests ingredient combinations and cooking methods based on the verbalized ingredient information. The menu generation unit may also have a function to suggest personalized menus that take into account the user's past preferences and allergy information. The matching unit lists restaurants and individuals wanting ingredients. For example, the system suggests optimal partners based on the registered user's needs and preferences. The selection unit selects partners from the list by the matching unit. For example, a user can check the profiles and ratings of the listed partners and select the most suitable partner. This allows the matching app according to the embodiment to effectively utilize ingredients that would otherwise be discarded and reduce food waste. For example, a user can easily provide ingredients that would otherwise be discarded to other users, thereby reducing food waste. Furthermore, the system can promote communication between users and strengthen ties in the local community.

[0062] The photography unit can automatically evaluate the freshness and condition of ingredients. The photography unit, for example, introduces an image analysis algorithm to evaluate the freshness of ingredients. For example, it detects changes in color and shape and calculates a freshness score. The photography unit also uses the freshness evaluation function to suggest how to use the ingredients depending on their condition. For example, it suggests eating them raw if they are highly fresh, and how to cook them if they are not. The photography unit also notifies the user of the food storage method and expiration date based on the freshness evaluation results. For example, it displays a message if refrigeration is appropriate. This allows the optimal usage method to be suggested by evaluating the freshness and condition of ingredients.

[0063] The photography unit can add detailed information about ingredients through voice input. For example, when taking a photo, the photography unit provides an interface that allows the purchase date and storage method of ingredients to be added through voice input. For example, the user can input "I bought these tomatoes yesterday" through voice input. The photography unit also uses the voice input function to automatically convert the detailed information about ingredients into text and store it in a database. For example, it uses voice recognition technology to convert speech to text. The photography unit also suggests ways to use and store ingredients based on the information added through voice input. For example, it displays advice such as "It is best to store them in the refrigerator." This allows suggestions to be made about how to use and store ingredients by adding detailed information through voice input.

[0064] The photography unit can estimate the user's emotions and display a message that elicits positive emotions. The photography unit, for example, incorporates a function that analyzes the user's facial expressions and estimates emotions when taking a photo. For example, the camera is used to analyze the user's facial expressions in real time. The photography unit also displays a message that elicits positive emotions based on the emotion estimation results. For example, an encouraging message such as "Great choice!" is displayed. The photography unit also provides an interface that corresponds to the user's emotional state based on the emotion estimation data. For example, if a negative emotion is detected, relaxing music is played. This analyzes the user's emotions and elicits positive emotions, thereby improving user satisfaction.

[0065] The photography unit automatically synthesizes photos from multiple angles, enabling more accurate ingredient recognition. For example, the photography unit introduces a function that automatically takes and synthesizes photos from multiple angles when taking photos. For example, a 360-degree camera is used to capture an overall image of the ingredient. The photography unit also develops an algorithm that more accurately recognizes the shape and characteristics of the ingredient based on the synthesized photos. For example, it generates a 3D model and analyzes the details of the ingredient. The photography unit also uses photos from multiple angles to more accurately evaluate the condition and freshness of the ingredient. For example, it analyzes changes in color and shape from different angles. This improves ingredient recognition accuracy by synthesizing photos from multiple angles.

[0066] The photography unit can receive real-time feedback from other users. For example, the photography unit introduces a function for receiving real-time feedback from other users when uploading a photo. For example, comments and ratings are displayed in real time. The photography unit also suggests ways to use and store ingredients based on feedback from other users. For example, it displays advice such as "These tomatoes are delicious in soup." The photography unit also uses the real-time feedback function to promote communication between users. For example, a chat function is added to exchange information about ingredients. This makes it possible to suggest ways to use and store ingredients based on feedback from other users.

[0067] The photography unit can estimate a user's emotions and promote emotional sharing within the community. For example, the photography unit introduces a function to estimate a user's emotions when uploading a photo, promoting emotional sharing within the community. For example, the emotion score is displayed and shared with other users. The photography unit also displays a message sharing positive emotions based on the emotion estimation result. For example, a message such as "Thank you for sharing this ingredient!" is displayed. The photography unit also uses the emotion estimation data to develop a function to promote emotional sharing within the community. For example, posts with high emotion scores are displayed preferentially. This allows users to share their emotions, promoting interaction within the community.

[0068] The analysis unit can provide nutritional value and allergy information for ingredients. For example, the analysis unit will introduce a function that automatically adds nutritional value information when the generation AI verbalizes ingredients. For example, the nutritional value of "tomato" will be displayed as the content of vitamin C and lycopene. The analysis unit will also add a function that automatically provides allergy information and alerts the user. For example, it will display a warning such as "This ingredient contains allergens." The analysis unit will also develop a system that integrates nutritional value and allergy information into verbalized ingredient information and provides it to the user. For example, detailed information about ingredients will be displayed on a single screen. This will protect the user's health by providing nutritional value and allergy information for ingredients.

[0069] The analysis unit can provide an interface that allows the user to check the verbalization results and input corrections or additional information. The analysis unit, for example, provides an interface that allows the user to check the verbalization results and input corrections or additional information. For example, the analysis unit asks for confirmation by asking, "Is this ingredient a tomato?" The analysis unit also adds a function that allows the user to correct the verbalization results and provide accurate information. For example, the user could correct the result by saying, "This is not a tomato, it's a paprika." The analysis unit also provides an interface that allows the user to input additional information to the verbalization results and collect more detailed information. For example, the analysis unit adds information such as, "This tomato is organic." This allows the user to check the verbalization results and input corrections or additional information, thereby providing accurate information.

[0070] The analysis unit can estimate the user's emotions and display a message encouraging positive feedback. For example, the analysis unit estimates the user's emotions regarding the verbalization result and displays a message encouraging positive feedback. For example, it displays a message such as "Great choice!". The analysis unit also provides feedback according to the user's emotional state based on the emotion estimation result. For example, if negative emotions are detected, it displays an encouraging message. The analysis unit also uses the emotion estimation data to provide an interface according to the user's emotions. For example, if positive emotions are strong, it displays success stories. In this way, by analyzing the user's emotions and encouraging positive feedback, user satisfaction is improved.

[0071] The analysis unit can share the verbalized ingredient information with other users and collaboratively devise menus. The analysis unit, for example, introduces a function for sharing the verbalized ingredient information with other users and collaboratively devising menus. For example, a chat function is used to exchange menu ideas. The analysis unit also provides an interface for collaboratively devising menus and promotes cooperation between users. For example, a collaborative editing function is added to create menus in real time. The analysis unit also develops a system for collaboratively proposing menus based on the ingredient information shared with other users. For example, menus proposed by multiple users are integrated. This allows users to collaborate on menus, thereby promoting the effective use of ingredients.

[0072] The analysis unit automatically translates the verbalized ingredient information into different languages ​​to obtain feedback from an international perspective. For example, the analysis unit automatically translates the verbalized ingredient information into different languages ​​to collect feedback from an international perspective. For example, the analysis unit translates into multiple languages ​​such as English, French, and Chinese. The analysis unit also builds a system that posts the translated ingredient information on a multilingual platform and obtains feedback from users around the world. The analysis unit also collects advice and improvement suggestions from an international perspective based on the ingredient information translated into different languages ​​to improve how ingredients are used. For example, the analysis unit reflects feedback that takes into account differences in culture and market. In this way, by translating into different languages, feedback from an international perspective can be obtained.

[0073] The analysis unit can collect users' emotional reactions and improve the accuracy of the verbalization. For example, the analysis unit collects users' emotional reactions to verbalized ingredient information in real time and improves the accuracy of the verbalization based on that data. For example, the analysis unit prioritizes the adoption of verbalization results with a high number of positive reactions. The analysis unit also uses an emotion estimation function to collect feedback on the verbalized ingredient information and regenerates verbalization results if there are a high number of negative reactions. The analysis unit also analyzes users' emotional reaction data and builds a system that improves the accuracy of the verbalization based on the results. For example, the analysis unit makes suggestions to correct parts with low emotional scores. In this way, the accuracy of the verbalization can be improved by collecting users' emotional reactions.

[0074] The menu generation unit can propose personalized menus that take into account the user's past preferences and allergy information. For example, the menu generation unit introduces a function that takes into account the user's past preferences when the generation AI creates a menu. For example, it may prioritize suggesting menus that have been well-received in the past. The menu generation unit also adds a menu suggestion function that takes allergy information into account to protect the user's health. For example, it may suggest menus that do not contain allergens. The menu generation unit will also develop a system that automatically generates personalized menus based on the user's past data. For example, it may analyze the user's dietary history and propose the optimal menu. This will allow it to propose personalized menus by taking into account the user's past preferences and allergy information.

[0075] The menu generation unit can propose the optimal cooking method by taking into account the shelf life and cooking time of ingredients. For example, the menu generation unit will introduce a function that considers the shelf life of ingredients when the generation AI creates a menu. For example, it will propose a menu that prioritizes the use of ingredients with a short shelf life. The menu generation unit will also add a menu suggestion function that takes cooking time into account, providing cooking methods that suit the user's time. For example, it will propose menus that can be cooked in a short amount of time. The menu generation unit will also develop a system that suggests the optimal cooking method based on the shelf life and cooking time of ingredients. For example, it will suggest menus that will be used at a later date for ingredients with a long shelf life. This will make it possible to propose the optimal cooking method by taking into account the shelf life and cooking time of ingredients.

[0076] The menu generation unit can estimate the user's emotions and prioritize suggesting menus that elicit positive emotions. For example, the menu generation unit estimates the user's emotions regarding a proposed menu and prioritizes suggesting menus that elicit positive emotions. For example, it displays a message such as "This menu is perfect for you!". The menu generation unit also suggests a menu that matches the user's emotional state based on the emotion estimation results. For example, if negative emotions are detected, it suggests a menu that will lift the user's spirits. The menu generation unit also uses emotion estimation data to develop a system that suggests menus that match the user's emotions. For example, if positive emotions are strong, it suggests a special menu. In this way, by analyzing the user's emotions and suggesting menus that elicit positive emotions, user satisfaction is improved.

[0077] The menu generation unit can refer to menus and recipes created by other users. For example, the menu generation unit will introduce a function to refer to menus and recipes created by other users, and the generation AI will use this when proposing menus. For example, popular recipes will be suggested preferentially. The menu generation unit will also store other users' menus and recipes in a database, and the generation AI will use them to create new menus. For example, recipes that have been highly rated by users will be suggested. The menu generation unit will also collect other users' menus and recipes in real time, and a system will be developed in which the generation AI will suggest menus based on this. For example, the latest trends will be reflected. This will allow a more diverse menu to be suggested by referring to menus and recipes created by other users.

[0078] The menu generation unit can convert the results of menu creation into visual notes or mind maps to make them easier to understand visually. For example, the menu generation unit introduces a function that converts the results of menu creation into visual notes to make them easier for users to understand visually. For example, it shows the main points of the menu with diagrams or icons. The menu generation unit also converts the results of menu creation into a mind map format to visually organize related keywords and concepts. This allows the overall picture of the menu to be understood at a glance. The menu generation unit also develops tools that automatically generate visual notes and mind maps to enable users to easily visually display the main points of the menu. For example, it provides a function to visualize menus using drag and drop. This visually displays the results of menu creation, making it easier for users to understand.

[0079] The menu generation unit can collect users' emotional responses and improve the accuracy of menu suggestions. The menu generation unit, for example, collects users' emotional responses to the menu creation results in real time and improves the accuracy of menu suggestions based on that data. For example, it prioritizes suggesting menus that receive a lot of positive responses. The menu generation unit also uses an emotion estimation function to collect feedback on the menu creation results and regenerates the menu if there are a lot of negative responses. The menu generation unit also analyzes users' emotional response data and builds a system that improves the accuracy of menu suggestions based on the results. For example, it makes suggestions to correct parts with low emotional scores. In this way, the accuracy of menu suggestions can be improved by collecting users' emotional responses.

[0080] The matching unit can display the past transaction history and evaluation of listed counterparties, allowing users to confirm their trustworthiness. The matching unit, for example, introduces a function that displays the past transaction history of listed counterparties and allows users to confirm their trustworthiness. For example, it displays the number of past transactions and evaluation scores. The matching unit also provides an interface for confirming trustworthiness based on the counterparty's evaluation. For example, it displays comments and feedback from other users. The matching unit also develops a system that automatically evaluates trustworthiness based on transaction history and evaluation data. For example, it calculates a reliability score and presents it to the user. This allows users to confirm trustworthiness by displaying the counterparty's past transaction history and evaluation.

[0081] The matching unit can introduce a matching algorithm that takes into account the other party's needs and preferences, and suggest the most suitable partner. The matching unit, for example, introduces a matching algorithm that takes into account the other party's needs and preferences, and suggests the most suitable partner. For example, it predicts needs based on past transaction data. The matching unit also incorporates the user's preferences and ratings into the matching algorithm to develop a system that suggests the most suitable partner. For example, it prioritizes suggesting partners with high user ratings. The matching unit also provides an interface that suggests the most suitable partner based on needs and preferences. For example, it displays the other party's profile and needs, making it easier for the user to select. This makes it possible to suggest the most suitable partner by taking into account the other party's needs and preferences.

[0082] The matching unit can estimate the user's emotions and display a message encouraging a positive choice. For example, when selecting a partner, the matching unit estimates the user's emotions and displays a message encouraging a positive choice. For example, it displays a message such as "This partner is highly trustworthy!". The matching unit also provides feedback according to the user's emotional state based on the emotion estimation result. For example, if a negative emotion is detected, it displays a message that gives a sense of security. The matching unit also uses the emotion estimation data to provide an interface according to the user's emotions. For example, if a positive emotion is strong, it displays success stories. In this way, the user's emotions are analyzed and positive choices are encouraged, thereby improving user satisfaction.

[0083] The matching unit can enable direct chat with listed parties. The matching unit, for example, introduces a function that enables direct chat with listed parties, enabling detailed information exchange. For example, a chat window is displayed and messages are sent and received in real time. The matching unit also uses the chat function to provide an interface for exchanging detailed information about transactions. For example, parties can discuss the status of ingredients and delivery methods. The matching unit also adds a function that saves chat history and allows it to be referenced later. For example, the details of past transactions can be checked and used for the next transaction. This makes it possible to exchange detailed information by chatting directly with the other party.

[0084] The matching unit can display the profiles and introduction videos of the listed partners. For example, the matching unit introduces a function that displays the profiles of the listed partners and allows the user to check detailed information. For example, it displays the partner's background and ratings. The matching unit also displays introduction videos and provides an interface that provides detailed information about the partner. For example, it uses videos to explain the partner's self-introduction and the transaction flow. The matching unit also develops a system that makes it easier for users to select partners based on the profiles and introduction videos. For example, it adds a function to allow users to rate the video after watching it. This makes it possible to provide detailed information by displaying the partner's profile and introduction video.

[0085] The matching unit can collect users' emotional responses to the other party's profile and improve matching accuracy. The matching unit, for example, collects users' emotional responses to the other party's profile in real time and improves matching accuracy based on that data. For example, it prioritizes suggesting partners with a high number of positive responses. The matching unit also uses an emotion estimation function to collect feedback on the other party's profile and regenerates suggestions if there are a high number of negative responses. The matching unit also analyzes users' emotional response data and builds a system that improves matching accuracy based on the results. For example, it makes suggestions to correct parts with low emotional scores. In this way, by collecting users' emotional responses, matching accuracy can be improved.

[0086] The selection unit can add an eco-friendly delivery option to the options for delivery methods. For example, the selection unit adds an eco-friendly delivery option to the options for delivery methods. For example, it suggests delivery using bicycle courier or electric vehicle. Furthermore, the selection unit introduces a function that displays the user's contribution to the environment when the eco-friendly delivery option is selected. For example, it displays the amount of CO2 reduction. Furthermore, the selection unit develops a system that provides points and benefits to users by selecting the eco-friendly delivery option. For example, points are accumulated each time eco-friendly delivery is selected. In this way, adding an eco-friendly delivery option can reduce the environmental burden.

[0087] The selection unit can track the delivery status in real time when selecting a delivery method. The selection unit provides a function that allows the delivery status to be tracked in real time when selecting a delivery method. For example, the location of the delivery vehicle is displayed using GPS. The selection unit also develops a system that tracks the delivery status in real time and notifies the user. For example, an alert is sent when delivery is approaching. The selection unit also provides an interface for tracking the delivery status so that the user can easily check it. For example, the delivery route is displayed on a map. This allows the user to track the delivery status in real time, allowing them to conduct transactions with peace of mind.

[0088] The selection unit can estimate the user's emotions and display a message when selecting the optimal delivery method. For example, when selecting a delivery method, the selection unit estimates the user's emotions and displays a message urging the user to make the optimal selection. For example, a message such as "This method is the most convenient!" is displayed. The selection unit also provides feedback according to the user's emotional state based on the emotion estimation result. For example, if a negative emotion is detected, a message that provides a sense of security is displayed. The selection unit also uses the emotion estimation data to provide an interface according to the user's emotions. For example, if a positive emotion is strong, success stories are displayed. In this way, analyzing the user's emotions and displaying a message when selecting the optimal delivery method improves user satisfaction.

[0089] The selection unit can add a pickup option at a local community center to the options for delivery methods. For example, the selection unit adds a pickup option at a local community center to the options for delivery methods. For example, picking up ingredients at a specified community center. The selection unit also introduces a function that provides the user with information about local events and activities when the user selects the option for pickup at a community center. For example, local event information is displayed at the time of pickup. The selection unit also develops a system that provides points and benefits to the user when the option for pickup at a community center is selected. For example, points are accumulated each time pickup at a community center is selected. In this way, adding the option for pickup at a local community center can strengthen ties with the local community.

[0090] The selection unit can refer to reviews and ratings from other users when selecting a delivery method. For example, the selection unit introduces a function to display reviews and ratings from other users when selecting a delivery method. For example, it displays rating scores based on past transactions. The selection unit also provides an interface for selecting a delivery method based on reviews and ratings from other users. For example, it preferentially displays delivery methods with high ratings. The selection unit also develops a system that supports the selection of a delivery method based on reviews and ratings. For example, it recommends delivery methods with high user ratings. This allows the optimal delivery method to be selected by referring to reviews and ratings from other users.

[0091] The selection unit can collect users' emotional reactions to delivery methods and use the collected data to improve the options. For example, the selection unit collects users' emotional reactions to delivery methods in real time and uses the data to improve the options. For example, delivery methods with a high number of positive reactions are preferentially displayed. The selection unit also uses an emotion estimation function to collect feedback on delivery methods and regenerate options if there are a high number of negative reactions. The selection unit also analyzes users' emotional reaction data and builds a system that improves the delivery method options based on the results. For example, it makes suggestions to correct parts with low emotional scores. In this way, the delivery method options can be improved by collecting users' emotional reactions.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The photography unit can automatically evaluate the freshness and condition of ingredients. For example, an image analysis algorithm is introduced to evaluate the freshness of ingredients. For example, changes in color and shape are detected and a freshness score is calculated. The photography unit also uses the freshness evaluation function to suggest how to use the ingredients depending on their condition. For example, if the freshness is high, it will suggest eating them raw, and if the freshness is low, it will suggest a cooking method. The photography unit also notifies the user of the food storage method and expiration date based on the freshness evaluation results. For example, if refrigeration is appropriate, it will display this information. This makes it possible to suggest the optimal way to use ingredients by evaluating their freshness and condition.

[0094] The photography unit can add detailed information about ingredients through voice input. For example, when taking a photo, an interface is provided that allows the purchase date and storage method of ingredients to be added through voice input. For example, "I bought these tomatoes yesterday" can be input by voice. The photography unit also uses the voice input function to automatically convert the detailed information about ingredients into text and store it in a database. For example, it uses voice recognition technology to convert speech to text. The photography unit also suggests ways to use and store ingredients based on the information added through voice input. For example, it displays advice such as "It is best to store them in the refrigerator." This allows suggestions to be made about how to use and store ingredients by adding detailed information through voice input.

[0095] The photography unit can estimate the user's emotions and display a message that elicits positive emotions. For example, a function is introduced to analyze the user's facial expressions and estimate emotions when taking a photo. For example, the camera is used to analyze the user's facial expressions in real time. The photography unit then displays a message that elicits positive emotions based on the emotion estimation results. For example, an encouraging message such as "Great choice!" is displayed. The photography unit also provides an interface that corresponds to the user's emotional state based on the emotion estimation data. For example, if a negative emotion is detected, relaxing music is played. This allows the user's emotions to be analyzed and positive emotions to be elicited, thereby improving user satisfaction.

[0096] The photography unit can automatically synthesize photos from multiple angles to enable more accurate ingredient recognition. For example, a function will be introduced to automatically take and synthesize photos from multiple angles when taking photos. For example, a 360-degree camera will be used to capture an overall image of the ingredient. The photography unit will then develop an algorithm to more accurately recognize the shape and characteristics of the ingredient based on the synthesized photos. For example, a 3D model will be generated and the details of the ingredient will be analyzed. The photography unit will also use photos from multiple angles to more accurately evaluate the condition and freshness of the ingredient. For example, it will analyze changes in color and shape from different angles. This will improve ingredient recognition accuracy by synthesizing photos from multiple angles.

[0097] The photography unit can receive real-time feedback from other users. For example, a function for receiving real-time feedback from other users when uploading a photo can be introduced. For example, comments and ratings can be displayed in real time. The photography unit can also suggest ways to use and store ingredients based on feedback from other users. For example, it can display advice such as "These tomatoes are delicious in soup." The photography unit can also use the real-time feedback function to promote communication between users. For example, a chat function can be added to exchange information about ingredients. This can make it possible to suggest ways to use and store ingredients based on feedback from other users.

[0098] The photography unit can estimate a user's emotions and promote emotional sharing within the community. For example, a function to estimate a user's emotions when uploading a photo can be introduced to promote emotional sharing within the community. For example, an emotion score can be displayed and shared with other users. The photography unit can also display a message sharing positive emotions based on the emotion estimation results. For example, a message such as "Thank you for sharing this ingredient!" can be displayed. The photography unit can also use the emotion estimation data to develop a function to promote emotional sharing within the community. For example, posts with high emotion scores can be displayed preferentially. This allows users to share their emotions and promote interaction within the community.

[0099] The analysis unit can provide nutritional value and allergy information for ingredients. For example, a function will be introduced that automatically adds nutritional value information when the generation AI verbalizes ingredients. For example, the nutritional value of "tomato" will be displayed as the content of vitamin C and lycopene. The analysis unit will also add a function that automatically provides allergy information and alerts the user. For example, it will display a warning such as "This ingredient contains allergens." The analysis unit will also develop a system that integrates nutritional value and allergy information into verbalized ingredient information and provides it to the user. For example, detailed information about ingredients will be displayed on a single screen. This will protect the user's health by providing information on the nutritional value and allergy of ingredients.

[0100] The analysis unit can provide an interface that allows the user to check the verbalization results and input corrections or additional information. For example, an interface is provided that allows the user to check the verbalization results and input corrections or additional information. For example, the analysis unit may ask for confirmation by asking, "Is this ingredient a tomato?" The analysis unit may also add a function that allows the user to correct the verbalization results and provide accurate information. For example, the analysis unit may correct the verbalization results to say, "This is not a tomato, it's a paprika." The analysis unit may also provide an interface that allows additional information to be input into the verbalization results and collect more detailed information. For example, the analysis unit may add information such as, "This tomato is organic." This allows the user to check the verbalization results and input corrections or additional information, thereby providing accurate information.

[0101] The analysis unit can estimate the user's emotions and display a message encouraging positive feedback. For example, it can estimate the user's emotions regarding the verbalization result and display a message encouraging positive feedback. For example, it can display a message such as "Great choice!". The analysis unit also provides feedback according to the user's emotional state based on the emotion estimation result. For example, if negative emotions are detected, it can display an encouraging message. The analysis unit also uses the emotion estimation data to provide an interface according to the user's emotions. For example, if positive emotions are strong, it can display success stories. In this way, by analyzing the user's emotions and encouraging positive feedback, user satisfaction can be improved.

[0102] The analysis unit can share the verbalized ingredient information with other users and collaboratively devise menus. For example, a function is introduced that allows users to share the verbalized ingredient information with other users and collaboratively devise menus. For example, a chat function is used to exchange menu ideas. The analysis unit also provides an interface for collaboratively devising menus and promotes cooperation between users. For example, a collaborative editing function is added to create menus in real time. The analysis unit also develops a system that collaboratively proposes menus based on the ingredient information shared with other users. For example, menus proposed by multiple users are integrated. This allows users to collaborate on menus, thereby promoting the effective use of ingredients.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The photographing unit photographs food items to be discarded. For example, a user may take a photo of the food items using a smartphone camera. The photographing unit may also have a function to automatically evaluate the freshness and condition of the food items. Step 2: The analysis unit analyzes the photos taken by the photography unit and verbalizes the ingredients. For example, the generation AI can use image recognition technology to identify ingredients and output their names as text data. The analysis unit can also provide information on the nutritional value and allergies of ingredients. Step 3: The menu generation unit generates a menu based on the ingredient information verbalized by the analysis unit. For example, the generation AI suggests ingredient combinations and cooking methods based on the verbalized ingredient information. The menu generation unit can also have a function to suggest personalized menus that take into account the user's past preferences and allergy information. Step 4: The matching unit creates a list of restaurants and individuals who want ingredients. For example, the system suggests the most suitable match based on the registered user's needs and preferences. Step 5: The selection unit selects the partner from the list by the matching unit. For example, the user checks the profiles and ratings of the partners in the list and selects the most suitable partner.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

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

[0139] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0141] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

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

[0145] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0146] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

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

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

[0151] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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]

[0172] 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 photography department takes photos of food items that are scheduled to be discarded, an analysis unit that analyzes the photograph taken by the photographing unit and verbalizes the ingredients; a menu generation unit that generates a menu based on the ingredient information verbalized by the analysis unit; A matching department that lists restaurants and individuals who need ingredients, a selection unit for selecting a partner listed by the matching unit; A system characterized by:

2. The photography unit is Automatically evaluate the freshness and condition of the ingredients 2. The system of claim 1.

3. The photography unit is You can add detailed information about the ingredients by voice input.

2. The system of claim 1.

4. The photography unit is Estimate user emotions and display messages that elicit positive emotions 2. The system of claim 1.

5. The photography unit is Automatically combines photos from multiple angles for more accurate ingredient recognition 2. The system of claim 1.

6. The photography unit is Receive real-time feedback from other users 2. The system of claim 1.

7. The photography unit is Estimate user emotions and encourage sharing of emotions within the community 2. The system of claim 1.

8. The analysis unit Provide nutritional value and allergy information for the ingredients 2. The system of claim 1.

Images