System

Abstract

An object of a system according to an embodiment is to propose a recipe by efficiently utilizing the contents of a refrigerator.SOLUTION: A system includes a photographing part, an ingredient specification part, a recipe generation part, and a recipe display part. The photographing unit photographs the contents of the refrigerator. The ingredient specifying unit analyzes the photograph taken by the photographing unit to specify the ingredient. The recipe generation unit generates a recipe based on the ingredient specified by the ingredient specification unit. The recipe display unit displays the recipe generated by the recipe generation 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] With conventional technology, it was difficult to efficiently utilize the contents of the refrigerator to suggest recipes.

[0005] The system according to the embodiment aims to propose recipes that make efficient use of the contents of a refrigerator. [Means for solving the problem]

[0006] The system according to the embodiment includes a photography unit, an ingredient identification unit, a recipe creation unit, and a recipe display unit. The photography unit takes a photograph of the contents of the refrigerator. The ingredient identification unit analyzes the photograph taken by the photography unit to identify ingredients. The recipe creation unit creates a recipe based on the ingredients identified by the ingredient identification unit. The recipe display unit displays the recipe created by the recipe creation unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest recipes that efficiently utilize the contents of the refrigerator. [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) The recipe matching system according to the embodiment of the present invention automatically analyzes the contents of the refrigerator and uses smart AI to suggest cooking recipes, allowing users to easily enjoy cooking based on the contents of the refrigerator.

[0029] A recipe matching system according to an embodiment includes a photography unit, an ingredient identification unit, a recipe generation unit, and a recipe display unit. The photography unit photographs the contents of a refrigerator. For example, the photography unit photographs the contents of the refrigerator using a smartphone camera. The photography unit can also photograph the contents of the refrigerator using a tablet camera. The photography unit can also photograph the contents of the refrigerator using a digital camera. The ingredient identification unit analyzes the photograph taken by the photography unit to identify ingredients. For example, the ingredient identification unit identifies ingredients in the refrigerator using image recognition technology. The ingredient identification unit can also identify ingredients using a machine learning algorithm. The ingredient identification unit can also identify ingredients using deep learning technology. The recipe generation unit generates a recipe based on the ingredients identified by the ingredient identification unit. For example, the recipe generation unit generates the recipe using a smart AI. The recipe generation unit can also generate the recipe using a text generation AI (e.g., LLM). The recipe generation unit can also generate the recipe using a multimodal generation AI. The recipe display unit displays the recipe generated by the recipe generation unit. For example, the recipe display unit displays the recipe on a smartphone screen. The recipe display unit can also display recipes on a tablet screen. The recipe display unit can also display recipes on a personal computer screen. This allows the recipe matching system according to the embodiment to allow users to easily enjoy cooking based on the contents of their refrigerator.

[0030] When a user takes a photo, the photography unit can evaluate the image quality in real time and suggest the best shooting angle and lighting conditions. For example, when a user takes a photo of the contents of a refrigerator, the photography unit's smart AI evaluates the image quality in real time and suggests the optimal shooting angle and lighting conditions. For example, AI built into a smartphone's camera app can recommend using the flash to brighten dark areas. The photography unit can also instruct the user to temporarily move items in the refrigerator if they are overlapping and difficult to see. For example, it can prompt the user to move a specific item toward the front with the refrigerator door open. The photography unit can also detect the temperature and humidity inside the refrigerator using sensors and evaluate the freshness of ingredients based on their storage conditions. For example, if the temperature is too high, it can suggest lowering the refrigerator's temperature setting. This allows users to photograph the contents of the refrigerator under optimal conditions.

[0031] The photographing unit can detect the temperature and humidity inside the refrigerator using a sensor and evaluate the freshness of ingredients based on the storage state. For example, the photographing unit detects the temperature and humidity inside the refrigerator using a sensor and evaluates the freshness of ingredients based on the storage state. For example, if the temperature is too high, the photographing unit suggests lowering the refrigerator's set temperature. Furthermore, if the humidity inside the refrigerator is too high, the photographing unit suggests to the user measures to lower the humidity. For example, the photographing unit instructs the user to open a vent in the refrigerator to lower the humidity. Furthermore, the photographing unit monitors the temperature and humidity inside the refrigerator in real time and evaluates the freshness of ingredients based on the storage state. For example, if the temperature is not appropriate, the photographing unit suggests adjusting the refrigerator's set temperature. In this way, by evaluating the freshness of ingredients, more appropriate recipes can be suggested.

[0032] Instead of taking a photo, the user can list the ingredients in the refrigerator by voice, and the ingredients can be identified using voice recognition technology. Instead of taking a photo, for example, the user can list the ingredients in the refrigerator by voice, and the smart AI can identify the ingredients using voice recognition technology. For example, if the user says "tomato, cheese, basil," the AI ​​will recognize it and add it to the list. Also, using voice recognition technology, the smart AI displays the recognition results in real time as the user lists the ingredients in the refrigerator. For example, if the user says "milk," "milk" will be displayed on the screen. Also, the user can list the ingredients in the refrigerator by voice, and the smart AI can identify the ingredients using voice recognition technology. For example, if the user says "eggs, butter, carrots," the AI ​​will recognize it and add it to the list. This improves user convenience by allowing ingredients to be identified by voice.

[0033] Instead of a photography unit, a dedicated device can be provided for users to scan the contents of the refrigerator, thereby achieving more accurate ingredient identification. Instead of a photography unit, for example, a dedicated device can be provided for users to scan the contents of the refrigerator, thereby achieving more accurate ingredient identification. For example, items in the refrigerator can be read with a barcode scanner. Also, the dedicated device can be used to scan the ingredients in the refrigerator, and smart AI can identify the ingredients based on that data. For example, ingredients can be identified using RFID tags. Also, a dedicated device can be provided for users to scan the contents of the refrigerator, thereby achieving more accurate ingredient identification. For example, items in the refrigerator can be read with a QR code. By using a dedicated device, the accuracy of ingredient identification can be improved.

[0034] When identifying ingredients, the ingredient identification unit simultaneously analyzes the nutritional value and allergen information of the ingredients, allowing it to suggest health-conscious recipes. For example, when identifying ingredients, the ingredient identification unit simultaneously analyzes the nutritional value and allergen information of the ingredients and suggests health-conscious recipes. For example, it may suggest recipes that prioritize low-calorie ingredients. In addition, at the same time as identifying ingredients, smart AI analyzes nutritional value and allergen information to suggest health-conscious recipes. For example, it may prioritize suggesting recipes that do not contain allergens. In addition, when identifying ingredients, it analyzes nutritional value and allergen information to suggest health-conscious recipes. For example, it may suggest recipes that use ingredients that are rich in vitamins and minerals. This makes it possible to suggest health-conscious recipes.

[0035] In addition to identifying ingredients, the ingredient identification unit can predict the expiration date of ingredients and prioritize suggesting ingredients that should be consumed early. For example, the ingredient identification unit can predict the expiration date of ingredients and prioritize suggesting ingredients that should be consumed early. For example, it can suggest recipes that use ingredients that are close to their expiration date. Furthermore, at the same time as identifying ingredients, smart AI can predict the expiration date and prioritize suggesting ingredients that should be consumed early. For example, it can suggest recipes that use ingredients with short expiration dates. Furthermore, it can predict the expiration date of ingredients and prioritize suggesting ingredients that should be consumed early. For example, it can suggest recipes that use ingredients with close expiration dates. This makes it possible to reduce food waste by prioritizing the use of ingredients that are close to their expiration date.

[0036] When identifying ingredients, the ingredient identification unit can also provide information on the origin and production method of the ingredients, thereby promoting ethical consumption. For example, when identifying ingredients, the ingredient identification unit can also provide information on the origin and production method of the ingredients, thereby promoting ethical consumption. For example, it can suggest recipes that use organically grown ingredients. Furthermore, when identifying ingredients, smart AI can also provide information on the origin and production method, thereby promoting ethical consumption. For example, it can suggest recipes that use fair trade ingredients. Furthermore, when identifying ingredients, smart AI can also provide information on the origin and production method, thereby promoting ethical consumption. For example, it can suggest recipes that use locally produced ingredients. This can promote ethical consumption.

[0037] The material identification unit can provide storage advice to optimize the space in the refrigerator in addition to identifying the material. The material identification unit, for example, provides storage advice to optimize the space in the refrigerator in addition to identifying the material. For example, it suggests an efficient storage method. Furthermore, at the same time as identifying the material, the smart AI provides storage advice to optimize the space in the refrigerator. For example, it suggests how to efficiently arrange items in the refrigerator. Furthermore, at the same time as identifying the material, the smart AI provides storage advice to optimize the space in the refrigerator. For example, it suggests how to efficiently arrange items in the refrigerator. This allows the space in the refrigerator to be used efficiently.

[0038] The recipe generation unit can provide individually customized recipes by reflecting the user's past cooking history and ratings. The recipe generation unit can, for example, provide individually customized recipes by reflecting the user's past cooking history and ratings. For example, it can prioritize recipes that the user has given high ratings to. Furthermore, the smart AI can propose individually customized recipes based on the user's past cooking history. For example, it can propose recipes that include ingredients that the user frequently uses. Furthermore, the smart AI can analyze the user's rating data and provide individually customized recipes. For example, it can propose recipes that reflect the user's preferred seasonings and cooking methods. This makes it possible to provide recipes that are customized based on the user's past history.

[0039] The recipe generation unit can take cooking time and difficulty into consideration when suggesting recipes, and make suggestions that are tailored to the user's schedule and skill level. For example, the recipe generation unit can take cooking time and difficulty into consideration when suggesting recipes, and make suggestions that are tailored to the user's schedule and skill level. For example, it can suggest recipes that can be made in a short amount of time to a busy user. Furthermore, the smart AI can suggest recipes that take cooking time and difficulty into consideration based on the user's skill level. For example, it can suggest simple recipes to beginner cooks. Furthermore, the smart AI can analyze the user's schedule and suggest recipes that take cooking time and difficulty into consideration. For example, it can suggest recipes that can be made over a longer period of time on weekends. This makes it possible to suggest recipes that are tailored to the user's schedule and skill level.

[0040] The recipe generation unit can reflect reviews and ratings from other users and prioritize suggesting popular recipes. The recipe generation unit, for example, reflects reviews and ratings from other users and prioritizes suggesting popular recipes. For example, highly rated recipes are displayed preferentially. Furthermore, smart AI suggests popular recipes based on reviews and ratings from other users. For example, recipes with a large number of reviews are suggested preferentially. Furthermore, smart AI analyzes rating data from other users and prioritizes suggesting popular recipes. For example, recipes with high rating scores are displayed preferentially. This allows popular recipes to be suggested preferentially.

[0041] The recipe generation unit can enrich the user's cooking experience by suggesting special recipes that match the season or an event when suggesting a recipe. The recipe generation unit can enrich the user's cooking experience by suggesting special recipes that match the season or an event when suggesting a recipe. For example, a special dinner recipe is suggested for Christmas. The smart AI can also suggest special recipes that match the season or an event to enrich the user's cooking experience. For example, a cold dessert recipe is suggested in the summer. The smart AI can also suggest special recipes that match the season or an event to enrich the user's cooking experience. For example, a special sweets recipe is suggested for Halloween. This makes it possible to suggest special recipes that match the season or an event.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The recipe matching system can also analyze a user's ingredient purchase history and suggest recipes that prioritize ingredients purchased frequently. For example, the recipe generation unit generates recipes that use ingredients that the user frequently purchases. It can also identify ingredients that the user previously purchased but did not use up based on the purchase history and suggest recipes that use them. Furthermore, it can analyze a user's purchase history to understand seasonal trends in ingredient purchases and suggest recipes that match the season. This makes it possible to suggest recipes based on the user's purchase history, thereby reducing ingredient waste.

[0044] The recipe matching system can also register the user's dietary preferences and allergy information and customize recipes based on that information. For example, if a user is allergic to a particular ingredient, it can prioritize recipes that do not contain that ingredient. Also, if a user likes a particular dish, it can suggest many recipes related to that dish. It can also suggest health-conscious recipes based on the user's dietary preferences and allergy information. This makes it possible to suggest recipes that meet the individual needs of the user.

[0045] The recipe matching system can also suggest recipes that match the user's mealtimes and meal purposes. For example, it can suggest simple, nutritious recipes for breakfast and recipes that can be made more quickly for dinner. It can also suggest low-calorie recipes for users on a diet and high-protein recipes for users who want to build muscle. It can also suggest recipes for special events or parties. This makes it possible to suggest recipes that match the user's mealtimes and meal purposes.

[0046] The recipe matching system can also analyze the user's eating history and suggest new recipes that the user has never made before. For example, it can suggest dishes that the user has never made before, providing the enjoyment of new cooking. It can also suggest different recipes using the same ingredients based on the user's eating history. Furthermore, it can suggest new recipes related to highly rated dishes based on the ratings of dishes the user has made in the past. This makes it possible to suggest new recipes based on the user's eating history.

[0047] The recipe matching system can also suggest substitutes for ingredients based on the user's dietary preferences and allergy information. For example, if the user is allergic to a particular ingredient, a substitute for that ingredient can be suggested. Also, if the user does not like a particular ingredient, a substitute for that ingredient can be suggested. Furthermore, it can also suggest healthy substitutes for ingredients based on the user's dietary preferences and allergy information. This makes it possible to suggest substitutes for ingredients that meet the individual needs of the user.

[0048] The recipe matching system can also analyze the user's meal history and suggest new ingredient combinations that the user has never made before. For example, it can suggest recipes that combine ingredients the user has never used before, providing the enjoyment of new cooking. It can also suggest recipes with different combinations using the same ingredients based on the user's meal history. Furthermore, it can suggest new ingredient combinations related to highly rated dishes based on the ratings of dishes the user has made in the past. This makes it possible to suggest new ingredient combinations based on the user's meal history.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The photography unit takes a photograph of the contents of the refrigerator. For example, the photograph is taken using a camera such as a smartphone, tablet, or digital camera. Step 2: The ingredient identification unit analyzes the photos taken by the photography unit to identify the ingredients. For example, it may use image recognition technology, machine learning algorithms, or deep learning technology to identify the ingredients in the refrigerator. Step 3: The recipe generator generates a recipe based on the ingredients identified by the ingredient identifier, for example, using smart AI, text generation AI (e.g., LLM), or multimodal generation AI. Step 4: The recipe display unit displays the recipe generated by the recipe generation unit, for example, on the screen of a smartphone, tablet, or PC.

[0051] (Example 2) The recipe matching system according to the embodiment of the present invention automatically analyzes the contents of the refrigerator and uses smart AI to suggest cooking recipes, allowing users to easily enjoy cooking based on the contents of the refrigerator.

[0052] A recipe matching system according to an embodiment includes a photography unit, an ingredient identification unit, a recipe generation unit, and a recipe display unit. The photography unit photographs the contents of a refrigerator. For example, the photography unit photographs the contents of the refrigerator using a smartphone camera. The photography unit can also photograph the contents of the refrigerator using a tablet camera. The photography unit can also photograph the contents of the refrigerator using a digital camera. The ingredient identification unit analyzes the photograph taken by the photography unit to identify ingredients. For example, the ingredient identification unit identifies ingredients in the refrigerator using image recognition technology. The ingredient identification unit can also identify ingredients using a machine learning algorithm. The ingredient identification unit can also identify ingredients using deep learning technology. The recipe generation unit generates a recipe based on the ingredients identified by the ingredient identification unit. For example, the recipe generation unit generates the recipe using a smart AI. The recipe generation unit can also generate the recipe using a text generation AI (e.g., LLM). The recipe generation unit can also generate the recipe using a multimodal generation AI. The recipe display unit displays the recipe generated by the recipe generation unit. For example, the recipe display unit displays the recipe on a smartphone screen. The recipe display unit can also display recipes on a tablet screen. The recipe display unit can also display recipes on a personal computer screen. This allows the recipe matching system according to the embodiment to allow users to easily enjoy cooking based on the contents of their refrigerator.

[0053] When a user takes a photo, the photography unit can evaluate the image quality in real time and suggest the best shooting angle and lighting conditions. For example, when a user takes a photo of the contents of a refrigerator, the photography unit's smart AI evaluates the image quality in real time and suggests the optimal shooting angle and lighting conditions. For example, AI built into a smartphone's camera app can recommend using the flash to brighten dark areas. The photography unit can also instruct the user to temporarily move items in the refrigerator if they are overlapping and difficult to see. For example, it can prompt the user to move a specific item toward the front with the refrigerator door open. The photography unit can also detect the temperature and humidity inside the refrigerator using sensors and evaluate the freshness of ingredients based on their storage conditions. For example, if the temperature is too high, it can suggest lowering the refrigerator's temperature setting. This allows users to photograph the contents of the refrigerator under optimal conditions.

[0054] The photographing unit can detect the temperature and humidity inside the refrigerator using a sensor and evaluate the freshness of ingredients based on the storage state. For example, the photographing unit detects the temperature and humidity inside the refrigerator using a sensor and evaluates the freshness of ingredients based on the storage state. For example, if the temperature is too high, the photographing unit suggests lowering the refrigerator's set temperature. Furthermore, if the humidity inside the refrigerator is too high, the photographing unit suggests to the user measures to lower the humidity. For example, the photographing unit instructs the user to open a vent in the refrigerator to lower the humidity. Furthermore, the photographing unit monitors the temperature and humidity inside the refrigerator in real time and evaluates the freshness of ingredients based on the storage state. For example, if the temperature is not appropriate, the photographing unit suggests adjusting the refrigerator's set temperature. In this way, by evaluating the freshness of ingredients, more appropriate recipes can be suggested.

[0055] The photography unit can use the emotion estimation function to provide guidance to reduce the stress and anxiety the user feels when taking a photo. For example, the photography unit uses the emotion estimation function to provide guidance to reduce the stress and anxiety the user feels when taking a photo. For example, if the user is nervous, the photography unit suggests breathing techniques to help the user relax. In addition, the photography unit uses smart AI to provide positive feedback to reduce the stress and anxiety the user feels when taking a photo. For example, if the photo is taken well, the photography unit displays words of praise. In addition, the photography unit uses the emotion estimation function to provide guidance to reduce the stress and anxiety the user feels when taking a photo. For example, if the user is in a hurry, the photography unit displays a message encouraging the user to take the photo slowly. This allows the user to relax and take the photo.

[0056] Instead of taking a photo, the user can list the ingredients in the refrigerator by voice, and the ingredients can be identified using voice recognition technology. Instead of taking a photo, for example, the user can list the ingredients in the refrigerator by voice, and the smart AI can identify the ingredients using voice recognition technology. For example, if the user says "tomato, cheese, basil," the AI ​​will recognize it and add it to the list. Also, using voice recognition technology, the smart AI displays the recognition results in real time as the user lists the ingredients in the refrigerator. For example, if the user says "milk," "milk" will be displayed on the screen. Also, the user can list the ingredients in the refrigerator by voice, and the smart AI can identify the ingredients using voice recognition technology. For example, if the user says "eggs, butter, carrots," the AI ​​will recognize it and add it to the list. This improves user convenience by allowing ingredients to be identified by voice.

[0057] Instead of a photography unit, a dedicated device can be provided for users to scan the contents of the refrigerator, thereby achieving more accurate ingredient identification. Instead of a photography unit, for example, a dedicated device can be provided for users to scan the contents of the refrigerator, thereby achieving more accurate ingredient identification. For example, items in the refrigerator can be read with a barcode scanner. Also, the dedicated device can be used to scan the ingredients in the refrigerator, and smart AI can identify the ingredients based on that data. For example, ingredients can be identified using RFID tags. Also, a dedicated device can be provided for users to scan the contents of the refrigerator, thereby achieving more accurate ingredient identification. For example, items in the refrigerator can be read with a QR code. By using a dedicated device, the accuracy of ingredient identification can be improved.

[0058] The photography unit uses a camera equipped with an emotion estimation function and can provide positive feedback in real time so that the user can enjoy taking pictures. The photography unit, for example, uses a camera equipped with an emotion estimation function and provides positive feedback in real time so that the user can enjoy taking pictures. For example, when a smile is detected, it displays "What a great smile!". The photography unit also uses the emotion estimation function to provide positive feedback so that the user can enjoy taking pictures. For example, when a photo is taken, it displays "Perfect shot!". The photography unit also uses a camera equipped with an emotion estimation function and provides positive feedback in real time so that the user can enjoy taking pictures. For example, when the user is relaxed, it displays "That's it!". This allows the user to enjoy taking pictures.

[0059] When identifying ingredients, the ingredient identification unit simultaneously analyzes the nutritional value and allergen information of the ingredients, allowing it to suggest health-conscious recipes. For example, when identifying ingredients, the ingredient identification unit simultaneously analyzes the nutritional value and allergen information of the ingredients and suggests health-conscious recipes. For example, it may suggest recipes that prioritize low-calorie ingredients. In addition, at the same time as identifying ingredients, smart AI analyzes nutritional value and allergen information to suggest health-conscious recipes. For example, it may prioritize suggesting recipes that do not contain allergens. In addition, when identifying ingredients, it analyzes nutritional value and allergen information to suggest health-conscious recipes. For example, it may suggest recipes that use ingredients that are rich in vitamins and minerals. This makes it possible to suggest health-conscious recipes.

[0060] In addition to identifying ingredients, the ingredient identification unit can predict the expiration date of ingredients and prioritize suggesting ingredients that should be consumed early. For example, the ingredient identification unit can predict the expiration date of ingredients and prioritize suggesting ingredients that should be consumed early. For example, it can suggest recipes that use ingredients that are close to their expiration date. Furthermore, at the same time as identifying ingredients, smart AI can predict the expiration date and prioritize suggesting ingredients that should be consumed early. For example, it can suggest recipes that use ingredients with short expiration dates. Furthermore, it can predict the expiration date of ingredients and prioritize suggesting ingredients that should be consumed early. For example, it can suggest recipes that use ingredients with close expiration dates. This makes it possible to reduce food waste by prioritizing the use of ingredients that are close to their expiration date.

[0061] The ingredient identification unit can use the emotion estimation function to learn which ingredients the user likes and which ingredients the user wants to avoid, and identify ingredients that suit individual preferences. The ingredient identification unit, for example, uses the emotion estimation function to learn which ingredients the user likes and which ingredients the user wants to avoid, and identify ingredients that suit individual preferences. For example, it can suggest recipes that avoid ingredients the user dislikes. It can also learn which ingredients the user likes and which ingredients the user wants to avoid based on the user's emotional response, and identify ingredients that suit individual preferences. For example, it can suggest recipes that use many of the user's favorite ingredients. It can also use the emotion estimation function to learn which ingredients the user likes and which ingredients the user wants to avoid, and identify ingredients that suit individual preferences. For example, it can suggest recipes that avoid ingredients the user is allergic to. This makes it possible to identify ingredients that suit the user's preferences.

[0062] When identifying ingredients, the ingredient identification unit can also provide information on the origin and production method of the ingredients, thereby promoting ethical consumption. For example, when identifying ingredients, the ingredient identification unit can also provide information on the origin and production method of the ingredients, thereby promoting ethical consumption. For example, it can suggest recipes that use organically grown ingredients. Furthermore, when identifying ingredients, smart AI can also provide information on the origin and production method, thereby promoting ethical consumption. For example, it can suggest recipes that use fair trade ingredients. Furthermore, when identifying ingredients, smart AI can also provide information on the origin and production method, thereby promoting ethical consumption. For example, it can suggest recipes that use locally produced ingredients. This can promote ethical consumption.

[0063] The material identification unit can provide storage advice to optimize the space in the refrigerator in addition to identifying the material. The material identification unit, for example, provides storage advice to optimize the space in the refrigerator in addition to identifying the material. For example, it suggests an efficient storage method. Furthermore, at the same time as identifying the material, the smart AI provides storage advice to optimize the space in the refrigerator. For example, it suggests how to efficiently arrange items in the refrigerator. Furthermore, at the same time as identifying the material, the smart AI provides storage advice to optimize the space in the refrigerator. For example, it suggests how to efficiently arrange items in the refrigerator. This allows the space in the refrigerator to be used efficiently.

[0064] The ingredient identification unit uses the emotion estimation function to evaluate the user's satisfaction in real time when identifying ingredients and make suggestions that will provide greater satisfaction. The ingredient identification unit, for example, uses the emotion estimation function to evaluate the user's satisfaction in real time when identifying ingredients and make suggestions that will provide greater satisfaction. For example, if the user is satisfied, the unit suggests a recipe using similar ingredients. The ingredient identification unit also evaluates the user's satisfaction in identifying ingredients in real time based on the user's emotional response and adjusts the suggestions. For example, if the user is dissatisfied, the unit suggests different ingredients. The ingredient identification unit also uses the emotion estimation function to evaluate the user's satisfaction in real time when identifying ingredients and make suggestions that will provide greater satisfaction. For example, the unit prioritizes suggestions based on the user's preferred ingredients. This makes it possible to make suggestions that will increase the user's satisfaction.

[0065] The recipe generation unit can provide individually customized recipes by reflecting the user's past cooking history and ratings. The recipe generation unit can, for example, provide individually customized recipes by reflecting the user's past cooking history and ratings. For example, it can prioritize recipes that the user has given high ratings to. Furthermore, the smart AI can propose individually customized recipes based on the user's past cooking history. For example, it can propose recipes that include ingredients that the user frequently uses. Furthermore, the smart AI can analyze the user's rating data and provide individually customized recipes. For example, it can propose recipes that reflect the user's preferred seasonings and cooking methods. This makes it possible to provide recipes that are customized based on the user's past history.

[0066] The recipe generation unit can take cooking time and difficulty into consideration when suggesting recipes, and make suggestions that are tailored to the user's schedule and skill level. For example, the recipe generation unit can take cooking time and difficulty into consideration when suggesting recipes, and make suggestions that are tailored to the user's schedule and skill level. For example, it can suggest recipes that can be made in a short amount of time to a busy user. Furthermore, the smart AI can suggest recipes that take cooking time and difficulty into consideration based on the user's skill level. For example, it can suggest simple recipes to beginner cooks. Furthermore, the smart AI can analyze the user's schedule and suggest recipes that take cooking time and difficulty into consideration. For example, it can suggest recipes that can be made over a longer period of time on weekends. This makes it possible to suggest recipes that are tailored to the user's schedule and skill level.

[0067] The recipe generation unit can use the emotion estimation function to suggest recipes that maximize the enjoyment and satisfaction that the user feels while cooking. The recipe generation unit, for example, uses the emotion estimation function to suggest recipes that maximize the enjoyment and satisfaction that the user feels while cooking. For example, if the user is enjoying cooking, a similar recipe is suggested. Furthermore, recipes that maximize the enjoyment and satisfaction that the user feels while cooking are suggested based on the user's emotional response. For example, if the user is satisfied, a recipe using similar ingredients is suggested. Furthermore, the emotion estimation function is used to suggest recipes that maximize the enjoyment and satisfaction that the user feels while cooking. For example, if the user is relaxed, a relaxing recipe is suggested. This allows the user to enjoy cooking.

[0068] The recipe generation unit can reflect reviews and ratings from other users and prioritize suggesting popular recipes. The recipe generation unit, for example, reflects reviews and ratings from other users and prioritizes suggesting popular recipes. For example, highly rated recipes are displayed preferentially. Furthermore, smart AI suggests popular recipes based on reviews and ratings from other users. For example, recipes with a large number of reviews are suggested preferentially. Furthermore, smart AI analyzes rating data from other users and prioritizes suggesting popular recipes. For example, recipes with high rating scores are displayed preferentially. This allows popular recipes to be suggested preferentially.

[0069] The recipe generation unit can enrich the user's cooking experience by suggesting special recipes that match the season or an event when suggesting a recipe. The recipe generation unit can enrich the user's cooking experience by suggesting special recipes that match the season or an event when suggesting a recipe. For example, a special dinner recipe is suggested for Christmas. The smart AI can also suggest special recipes that match the season or an event to enrich the user's cooking experience. For example, a cold dessert recipe is suggested in the summer. The smart AI can also suggest special recipes that match the season or an event to enrich the user's cooking experience. For example, a special sweets recipe is suggested for Halloween. This makes it possible to suggest special recipes that match the season or an event.

[0070] The recipe generation unit can use the emotion estimation function to evaluate the anticipation and excitement a user feels toward a specific recipe and suggest the most interesting recipe. For example, the recipe generation unit can use the emotion estimation function to evaluate the anticipation and excitement a user feels toward a specific recipe and suggest the most interesting recipe. For example, if the user is excited, a similar recipe can be suggested. Furthermore, the recipe generation unit can evaluate the anticipation and excitement a user feels toward a specific recipe based on the user's emotional response and suggest the most interesting recipe. For example, if the user is excited, a recipe using similar ingredients can be suggested. Furthermore, the recipe generation unit can use the emotion estimation function to evaluate the anticipation and excitement a user feels toward a specific recipe and suggest the most interesting recipe. For example, if the user is excited, a recipe that will elicit excitement can be suggested. In this way, recipes that will interest the user can be suggested.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The recipe matching system can also analyze a user's ingredient purchase history and suggest recipes that prioritize ingredients purchased frequently. For example, the recipe generation unit generates recipes that use ingredients that the user frequently purchases. It can also identify ingredients that the user previously purchased but did not use up based on the purchase history and suggest recipes that use them. Furthermore, it can analyze a user's purchase history to understand seasonal trends in ingredient purchases and suggest recipes that match the season. This makes it possible to suggest recipes based on the user's purchase history, thereby reducing ingredient waste.

[0073] The recipe matching system can also register the user's dietary preferences and allergy information and customize recipes based on that information. For example, if a user is allergic to a particular ingredient, it can prioritize recipes that do not contain that ingredient. Also, if a user likes a particular dish, it can suggest many recipes related to that dish. It can also suggest health-conscious recipes based on the user's dietary preferences and allergy information. This makes it possible to suggest recipes that meet the individual needs of the user.

[0074] The recipe matching system can also suggest recipes that match the user's mealtimes and meal purposes. For example, it can suggest simple, nutritious recipes for breakfast and recipes that can be made more quickly for dinner. It can also suggest low-calorie recipes for users on a diet and high-protein recipes for users who want to build muscle. It can also suggest recipes for special events or parties. This makes it possible to suggest recipes that match the user's mealtimes and meal purposes.

[0075] The recipe matching system can also estimate the user's emotions and suggest recipes based on the estimated emotions. For example, if the user is feeling stressed, it can suggest recipes using ingredients that have a relaxing effect. If the user is tired, it can suggest recipes that will replenish energy. Furthermore, if the user is in a happy mood, it can suggest recipes suitable for parties. This makes it possible to suggest recipes that match the user's emotions, thereby increasing meal satisfaction.

[0076] The recipe matching system can also analyze the user's eating history and suggest new recipes that the user has never made before. For example, it can suggest dishes that the user has never made before, providing the enjoyment of new cooking. It can also suggest different recipes using the same ingredients based on the user's eating history. Furthermore, it can suggest new recipes related to highly rated dishes based on the ratings of dishes the user has made in the past. This makes it possible to suggest new recipes based on the user's eating history.

[0077] The recipe matching system can also estimate the user's emotions and provide advice on how to select ingredients based on the estimated emotions. For example, if the user is feeling anxious, the system can advise the user to select ingredients that have a relaxing effect. If the user is tired, the system can advise the user to select ingredients that will replenish energy. Furthermore, if the user is in a happy mood, the system can advise the user to select ingredients suitable for a party. This allows the system to provide advice on selecting ingredients that matches the user's emotions, thereby increasing the satisfaction of the meal.

[0078] The recipe matching system can also suggest substitutes for ingredients based on the user's dietary preferences and allergy information. For example, if the user is allergic to a particular ingredient, a substitute for that ingredient can be suggested. Also, if the user does not like a particular ingredient, a substitute for that ingredient can be suggested. Furthermore, it can also suggest healthy substitutes for ingredients based on the user's dietary preferences and allergy information. This makes it possible to suggest substitutes for ingredients that meet the individual needs of the user.

[0079] The recipe matching system can also estimate the user's emotions and provide cooking advice based on the estimated emotions. For example, if the user is feeling stressed, the system can provide advice on cooking methods that have a relaxing effect. If the user is tired, the system can provide advice on simple and time-saving cooking methods. Furthermore, if the user is in a happy mood, the system can provide advice on cooking methods suitable for a party. This makes it possible to provide advice on cooking methods that match the user's emotions, thereby increasing the user's satisfaction with cooking.

[0080] The recipe matching system can also analyze the user's meal history and suggest new ingredient combinations that the user has never made before. For example, it can suggest recipes that combine ingredients the user has never used before, providing the enjoyment of new cooking. It can also suggest recipes with different combinations using the same ingredients based on the user's meal history. Furthermore, it can suggest new ingredient combinations related to highly rated dishes based on the ratings of dishes the user has made in the past. This makes it possible to suggest new ingredient combinations based on the user's meal history.

[0081] The recipe matching system can also estimate the user's emotions and advise meal timings based on the estimated emotions. For example, if the user is feeling stressed, the system can advise the user to eat at a time when they can relax. If the user is tired, the system can advise the user to eat at a time when they can replenish their energy. Furthermore, if the user is in a happy mood, the system can advise the user to eat at a time when they are most likely to have a party. This makes it possible to advise the user on meal timings that match their emotions, thereby increasing their satisfaction with their meal.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The photography unit takes a photograph of the contents of the refrigerator. For example, the photograph is taken using a camera such as a smartphone, tablet, or digital camera. Step 2: The ingredient identification unit analyzes the photos taken by the photography unit to identify the ingredients. For example, it may use image recognition technology, machine learning algorithms, or deep learning technology to identify the ingredients in the refrigerator. Step 3: The recipe generator generates a recipe based on the ingredients identified by the ingredient identifier, for example, using smart AI, text generation AI (e.g., LLM), or multimodal generation AI. Step 4: The recipe display unit displays the recipe generated by the recipe generation unit, for example, on the screen of a smartphone, tablet, or PC.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 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 club that takes photos of the contents of the refrigerator, an ingredient identification unit that analyzes the photograph taken by the photography unit and identifies the ingredient; a recipe generation unit that generates a recipe based on the ingredients identified by the ingredient identification unit; a recipe display unit that displays the recipe generated by the recipe generation unit. A system characterized by:

2. The photography unit is As users take photos, the app evaluates image quality in real time and suggests shooting angles and lighting conditions.

2. The system of claim 1.

3. The photography unit is The temperature and humidity inside the refrigerator are detected by a sensor, and the freshness of the ingredients is evaluated based on the storage conditions.

2. The system of claim 1.

4. The photography unit is Providing guidance to reduce stress and anxiety users may experience when taking photos 2. The system of claim 1.

5. Instead of the photography section, A user vocally lists ingredients in the refrigerator and uses voice recognition technology to identify the ingredients.

2. The system of claim 1.

Images