System

Abstract

An object of a system according to an embodiment is to propose an optimal dish to a user by integrating information such as a photograph, a receipt, and a recipe of the dish.SOLUTION: A system includes a photograph analysis unit, a receipt analysis unit, a recipe analysis unit, and a proposal unit. The photograph analysis unit analyzes the photograph of the dish using the generated AI. The receipt analysis unit analyzes the receipt of the supermarket using the generated AI. A recipe analysis part analyzes the recipe of the famous cook by using the generated AI. A proposal part proposes an optimum dish to the user on the basis of the information analyzed by the photograph analysis part, the receipt analysis part and the recipe analysis part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately integrate information such as food photos, receipts, and recipes to suggest optimal dishes, and there is room for improvement.

[0005] The system according to the embodiment aims to integrate information such as food photos, receipts, and recipes to suggest the most suitable dishes to the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a photo analysis unit, a receipt analysis unit, a recipe analysis unit, and a suggestion unit. The photo analysis unit analyzes photos of food using a generation AI. The receipt analysis unit analyzes supermarket receipts using a generation AI. The recipe analysis unit analyzes recipes from famous chefs using a generation AI. The suggestion unit suggests optimal dishes to the user based on the information analyzed by the photo analysis unit, receipt analysis unit, and recipe analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can integrate information such as food photos, receipts, and recipes to suggest the most suitable dishes to the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The recommendation system according to the embodiment of the present invention uses a generation AI to analyze food photos, supermarket receipts, and recipes from famous chefs, and then suggests meals to make today. This allows the recommendation system to suggest the most suitable dishes for the user.

[0029] The proposal system according to the embodiment includes a photo analysis unit, a receipt analysis unit, a recipe analysis unit, and a proposal unit. The photo analysis unit uses a generation AI to analyze photos of dishes. For example, the photo analysis unit inputs a photo of a dish taken by a user with a smartphone into the generation AI, which then analyzes the photo to identify the type of dish and the ingredients used. The photo analysis unit can also analyze a photo of curry and identify the type of curry and the ingredients (meat, vegetables, spices, etc.) used. The receipt analysis unit uses the generation AI to analyze supermarket receipts. For example, after a user shops at a supermarket, the receipt analysis unit inputs the receipt into the generation AI, which then analyzes the product names and quantities listed on the receipt to identify the ingredients purchased. The receipt analysis unit can also identify "chicken, onion, and carrot" if the receipt lists these ingredients. The recipe analysis unit uses the generation AI to analyze recipes by famous chefs. For example, the recipe analysis unit inputs a recipe from a famous chef that a user finds online into the generation AI, and the generation AI analyzes the recipe to identify the necessary ingredients and cooking steps. The recipe analysis unit can also allow the generation AI to analyze a recipe for "famous chef A's special pasta" and identify the necessary ingredients (pasta, tomatoes, basil, etc.) and cooking steps for the recipe. The suggestion unit suggests the most suitable dish for the user based on the information analyzed by the photo analysis unit, receipt analysis unit, and recipe analysis unit. For example, the suggestion unit combines the information analyzed by the generation AI to suggest "chicken and vegetable curry" or "special pasta" to the user. This allows the suggestion system according to the embodiment to suggest the most suitable dish for the user. For example, the user can make effective use of ingredients in the refrigerator or purchased at the supermarket to create delicious dishes based on recipes from famous chefs. Analyzing photos of dishes can also provide new dish ideas.

[0030] The photo analysis unit can analyze the freshness and quality of ingredients included in photos of dishes and suggest using fresher ingredients first. The photo analysis unit, for example, analyzes photos of dishes and detects changes in color and texture to evaluate the freshness of ingredients. For example, it checks whether the colors of vegetables are vivid or whether the surface of meat is dry. The photo analysis unit also analyzes the shape and size of ingredients from photos to identify non-standard ingredients in order to evaluate the quality of ingredients. For example, it detects vegetables that are misshapen or fruits that are unevenly sized. The photo analysis unit also suggests an order in which ingredients should be used based on the analysis results in order to prioritize the use of fresher ingredients. For example, it can suggest a recipe that uses fresher ingredients first. This allows the user to suggest more delicious dishes by prioritizing the use of fresher ingredients.

[0031] The photo analysis unit can identify the origin and producer information of ingredients from photos of dishes, and suggest optimal dishes from the perspective of local production and consumption. The photo analysis unit, for example, analyzes photos of dishes and identifies the origin based on the characteristics of the ingredients. For example, it detects the characteristics of vegetables and fruits that are only produced in specific regions. The photo analysis unit also reads the label and packaging information of ingredients from photos to identify the producer information of ingredients. For example, it analyzes labels that list the farmer's name and place of production. The photo analysis unit also suggests recipes using local ingredients based on the analysis results in order to suggest optimal dishes from the perspective of local production and consumption. For example, it suggests dishes using local specialties. This makes it possible to suggest optimal dishes from the perspective of local production and consumption.

[0032] The photo analysis unit can analyze the nutritional value of ingredients included in a photo of a dish and suggest health-conscious dishes. The photo analysis unit, for example, analyzes a photo of a dish and identifies the type and amount of ingredients. For example, it identifies ingredients such as vegetables, meat, and fish and calculates the nutritional value of each. The photo analysis unit also suggests health-conscious dishes based on the nutritional value of the ingredients. For example, it provides recipes using ingredients rich in vitamins and minerals. The photo analysis unit also suggests dishes with an optimal nutritional balance based on the user's health condition and nutritional needs. For example, it suggests low-calorie dishes to a user who is on a diet. This makes it possible to suggest health-conscious dishes.

[0033] The photo analysis unit can identify allergen information for ingredients from photos of dishes and suggest dishes suitable for users with allergies. The photo analysis unit, for example, analyzes photos of dishes and identifies allergen ingredients. For example, it detects allergens such as nuts, dairy products, and gluten. The photo analysis unit also suggests alternative ingredients based on the allergen information to suggest dishes suitable for users with allergies. For example, it provides recipes that use soy milk instead of dairy products. The photo analysis unit also registers the user's allergy information in advance and suggests dishes that do not contain allergens based on that information. For example, if the user has a nut allergy, it provides recipes that do not contain nuts. This makes it possible to suggest dishes suitable for users with allergies.

[0034] The receipt analysis unit can analyze the expiration dates of products printed on receipts and suggest dishes that prioritize ingredients that should be consumed quickly. The receipt analysis unit, for example, analyzes the expiration dates of products printed on receipts and identifies ingredients that should be consumed quickly. For example, it lists ingredients that are close to their expiration date. The receipt analysis unit also suggests dishes that prioritize ingredients that are close to their expiration date. For example, it provides recipes for soups and salads that use vegetables that are close to their expiration date. The receipt analysis unit also suggests dishes that reduce waste based on ingredients that are close to their expiration date. For example, it provides recipes that use up ingredients that are close to their expiration date at once. In this way, it is possible to reduce ingredient waste by prioritizing the use of ingredients that should be consumed quickly.

[0035] The receipt analysis unit can analyze the price information of the products listed on the receipt and suggest dishes with high cost performance. For example, the receipt analysis unit analyzes the price information of the products listed on the receipt and identifies ingredients with high cost performance. For example, it lists ingredients that are inexpensive and have high nutritional value. The receipt analysis unit also suggests dishes with high cost performance based on the price information. For example, it provides recipes that are filling and use inexpensive ingredients. The receipt analysis unit also suggests dishes with optimal cost performance based on the user's budget. For example, it provides recipes that provide the maximum nutritional value within the budget. This makes it possible to suggest dishes with high cost performance.

[0036] The receipt analysis unit can analyze the eco-label information of the products printed on the receipt and suggest environmentally friendly dishes. For example, the receipt analysis unit can analyze the eco-label information of the products printed on the receipt and identify environmentally friendly ingredients. For example, it can detect organically grown or fair trade labels. The receipt analysis unit can also suggest environmentally friendly dishes based on the eco-label information. For example, it can provide recipes using organic vegetables or dishes using fair trade ingredients. The receipt analysis unit can also suggest optimal eco-friendly dishes based on the user's environmental awareness. For example, it can provide recipes using ingredients with a low carbon footprint. This makes it possible to suggest environmentally friendly dishes.

[0037] The recipe analysis unit can analyze the cooking time written in a recipe and suggest dishes that can be made in a short time. For example, the recipe analysis unit analyzes the cooking time written in a recipe and identifies dishes that can be made in a short time. For example, it lists recipes that can be made in 30 minutes or less. The recipe analysis unit also suggests dishes that can be made in a short time, suitable for busy users, based on the cooking time. For example, it provides dishes that can be made with simple steps. The recipe analysis unit also suggests dishes with optimal cooking times according to the user's schedule. For example, it provides recipes that can be made in a short time on a weekday evening. This makes it possible to suggest dishes that can be made in a short time.

[0038] The recipe analysis unit can analyze the cooking utensils listed in a recipe and suggest dishes that are suitable for the cooking utensils the user owns. For example, the recipe analysis unit analyzes the cooking utensils listed in a recipe and identifies dishes that are suitable for the cooking utensils the user owns. For example, it lists recipes that use frying pans and ovens. The recipe analysis unit also suggests dishes that can be made with the cooking utensils the user owns based on the cooking utensils. For example, it registers the cooking utensils the user owns and provides recipes that are suitable for them. The recipe analysis unit also suggests optimal recipes based on information about the user's cooking utensils. For example, it prioritizes suggesting dishes that use specific cooking utensils. This makes it possible to suggest dishes that are suitable for the cooking utensils the user owns.

[0039] The recipe analysis unit can suggest substitutes for ingredients listed in a recipe and suggest dishes that can be made with ingredients the user has. The recipe analysis unit, for example, analyzes the ingredients listed in a recipe and suggests substitutes. For example, it provides a recipe that uses chicken instead of beef. The recipe analysis unit also suggests dishes that use substitutes based on ingredients the user has. For example, it registers ingredients in the refrigerator and provides recipes that suit them. The recipe analysis unit also suggests dishes that can be made with ingredients the user has based on substitutes for ingredients. For example, it provides a recipe that uses a substitute if a particular ingredient is unavailable. This makes it possible to suggest dishes that can be made with ingredients the user has.

[0040] The recipe analysis unit can analyze the cooking steps written in a recipe and suggest dishes with simplified steps so that even beginners can easily make them. The recipe analysis unit, for example, analyzes the cooking steps written in a recipe and simplifies the steps so that even beginners can easily make them. For example, it omits complicated steps or replaces them with simple steps. The recipe analysis unit also suggests easy dishes for beginners based on the cooking steps. For example, it provides recipes that reduce cooking time or use fewer cooking utensils. The recipe analysis unit also suggests dishes with optimal cooking steps according to the user's cooking skill. For example, it provides step-by-step recipes for beginners. This makes it possible to suggest dishes that even beginners can easily make.

[0041] The suggestion unit can increase the variety of dishes to be suggested and provide multiple options according to the user's preferences. For example, the suggestion unit provides recipes that combine different ingredients and cooking methods to increase the variety of dishes to be suggested. For example, different dishes using the same ingredients are suggested. Furthermore, the suggestion unit suggests recipes based on the user's past selection history to provide multiple options according to the user's preferences. For example, it analyzes trends in the dishes that the user prefers and provides recipes based on that. Furthermore, the suggestion unit provides recipes according to seasons or events to increase the variety of dishes to be suggested. For example, it suggests dishes using seasonal ingredients or dishes suitable for specific events. This makes it possible to provide multiple options according to the user's preferences.

[0042] The suggestion unit can analyze the difficulty of the dishes to be suggested and suggest the optimal dishes according to the user's cooking skill. For example, the suggestion unit analyzes the difficulty of the dishes to be suggested and identifies the optimal dishes according to the user's cooking skill. For example, it lists easy recipes for beginners and complex recipes for advanced cooks. The suggestion unit also provides recipes based on the user's past cooking history to suggest the optimal dishes according to the user's cooking skill. For example, it analyzes the difficulty of dishes the user has made in the past and provides recipes based on that. The suggestion unit also analyzes the difficulty of the dishes to be suggested and provides recipes to support the user's skill improvement. For example, it suggests dishes that are slightly more difficult, encouraging the user to improve their skills. This makes it possible to suggest the optimal dishes according to the user's cooking skill.

[0043] The suggestion unit can consider the seasonal feel of the proposed dish and suggest dishes that suit the season. For example, the suggestion unit provides recipes that use seasonal ingredients in order to consider the seasonal feel of the proposed dish. For example, it suggests salads using fresh vegetables in spring and hot soups in winter. The suggestion unit also provides recipes that are suitable for seasonal events and occasions in order to suggest dishes that suit the season. For example, it suggests recipes for special dinners at Christmas and barbecue recipes in summer. The suggestion unit also provides recipes based on the seasons of seasonal ingredients in order to suggest dishes that take the seasonal feel into consideration. For example, it suggests dishes that use seasonal fish and vegetables. This makes it possible to suggest dishes that suit the season.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The recommendation system can further include a history analysis unit that analyzes the user's meal history. The history analysis unit, for example, collects and analyzes data on dishes the user has made or eaten in the past. This allows the system to understand the user's preferences and eating habits and suggest more personalized dishes. For example, new recipes can be suggested based on dishes the user has frequently made or liked to eat in the past. The history analysis unit can also make suggestions to improve nutritional balance based on the user's meal history. For example, it can identify nutrients that are lacking from the user's past meal history and suggest recipes to supplement them. This can support the user's health.

[0046] The recommendation system may further include a restriction management unit that manages information about the user's dietary restrictions. The restriction management unit, for example, registers the user's dietary restrictions (allergies, religious restrictions, diets, etc.) and filters the dishes to be suggested based on them. For example, if the user requires gluten-free meals, recipes that do not contain gluten may be suggested preferentially. The restriction management unit may also suggest alternative ingredients in accordance with the user's dietary restrictions. For example, a recipe that uses soy milk instead of dairy products may be provided. This makes it possible to suggest dishes that take the user's dietary restrictions into consideration.

[0047] The recommendation system may further include a satisfaction evaluation unit that evaluates the user's satisfaction with the meal. The satisfaction evaluation unit, for example, collects and analyzes feedback on the dishes prepared by the user. For example, the user may input an evaluation of the dish's taste, appearance, and the effort required for preparation. The satisfaction evaluation unit may also make improvements to improve the quality of the dishes it recommends based on the collected feedback. For example, if a user gives a low rating to a particular dish, the reason for this can be analyzed and reflected in the next recommendation. This can increase the user's satisfaction.

[0048] The recommendation system can further include a timing management unit that manages the timing of the user's meals. The timing management unit, for example, records the time periods and frequency of the user's meals and suggests optimal dishes based on that information. For example, it can provide recipes according to the time periods for breakfast, lunch, and dinner. The timing management unit can also make suggestions to help the user regulate their eating rhythm. For example, it can send reminders to ensure regular meal times. This can support the user's healthy eating habits.

[0049] The proposal system can further include a cost management unit that manages the cost of a user's meals. The cost management unit, for example, collects and analyzes price information on ingredients purchased by the user. This makes it possible to suggest dishes that fit the user's budget. For example, it can provide recipes that can be made on a low budget or recipes that use cost-effective ingredients. The cost management unit can also generate monthly food expense reports to support the user's food expense management. This allows the user to enjoy balanced meals while being conscious of saving on food expenses.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The photo analysis unit uses the generation AI to analyze photos of food. For example, a user can input a photo of food taken with a smartphone into the generation AI, which then analyzes the photo to identify the type of food and the ingredients used. The generation AI can also analyze a photo of curry to identify the type of curry and the ingredients used (meat, vegetables, spices, etc.). Step 2: The receipt analysis unit uses the generation AI to analyze supermarket receipts. For example, after a user goes shopping at a supermarket, the generation AI inputs the receipt, which analyzes the product names and quantities listed on the receipt to identify the ingredients purchased. In addition, if the receipt lists "chicken, onions, and carrots," the generation AI can also identify these ingredients. Step 3: The recipe analysis unit uses the generation AI to analyze the famous chef's recipe. For example, a user can input a recipe from a famous chef they found online into the generation AI, and the generation AI will analyze the recipe and identify the necessary ingredients and cooking steps. The generation AI can also analyze a recipe for "famous chef A's special pasta" and identify the necessary ingredients (pasta, tomatoes, basil, etc.) and cooking steps for that recipe. Step 4: The suggestion unit suggests the best dish for the user based on the information analyzed by the photo analysis unit, receipt analysis unit, and recipe analysis unit. For example, by combining the information analyzed by the generative AI, it can suggest "chicken and vegetable curry" or "special pasta" to the user. This allows the user to make effective use of ingredients in the refrigerator or purchased at the supermarket, and create delicious dishes based on recipes from famous chefs.

[0052] (Example 2) The recommendation system according to the embodiment of the present invention uses a generation AI to analyze food photos, supermarket receipts, and recipes from famous chefs, and then suggests meals to make today. This allows the recommendation system to suggest the most suitable dishes for the user.

[0053] The proposal system according to the embodiment includes a photo analysis unit, a receipt analysis unit, a recipe analysis unit, and a proposal unit. The photo analysis unit uses a generation AI to analyze photos of dishes. For example, the photo analysis unit inputs a photo of a dish taken by a user with a smartphone into the generation AI, which then analyzes the photo to identify the type of dish and the ingredients used. The photo analysis unit can also analyze a photo of curry and identify the type of curry and the ingredients (meat, vegetables, spices, etc.) used. The receipt analysis unit uses the generation AI to analyze supermarket receipts. For example, after a user shops at a supermarket, the receipt analysis unit inputs the receipt into the generation AI, which then analyzes the product names and quantities listed on the receipt to identify the ingredients purchased. The receipt analysis unit can also identify "chicken, onion, and carrot" if the receipt lists these ingredients. The recipe analysis unit uses the generation AI to analyze recipes by famous chefs. For example, the recipe analysis unit inputs a recipe from a famous chef that a user finds online into the generation AI, and the generation AI analyzes the recipe to identify the necessary ingredients and cooking steps. The recipe analysis unit can also allow the generation AI to analyze a recipe for "famous chef A's special pasta" and identify the necessary ingredients (pasta, tomatoes, basil, etc.) and cooking steps for the recipe. The suggestion unit suggests the most suitable dish for the user based on the information analyzed by the photo analysis unit, receipt analysis unit, and recipe analysis unit. For example, the suggestion unit combines the information analyzed by the generation AI to suggest "chicken and vegetable curry" or "special pasta" to the user. This allows the suggestion system according to the embodiment to suggest the most suitable dish for the user. For example, the user can make effective use of ingredients in the refrigerator or purchased at the supermarket to create delicious dishes based on recipes from famous chefs. Analyzing photos of dishes can also provide new dish ideas.

[0054] The photo analysis unit can analyze the freshness and quality of ingredients included in photos of dishes and suggest using fresher ingredients first. The photo analysis unit, for example, analyzes photos of dishes and detects changes in color and texture to evaluate the freshness of ingredients. For example, it checks whether the colors of vegetables are vivid or whether the surface of meat is dry. The photo analysis unit also analyzes the shape and size of ingredients from photos to identify non-standard ingredients in order to evaluate the quality of ingredients. For example, it detects vegetables that are misshapen or fruits that are unevenly sized. The photo analysis unit also suggests an order in which ingredients should be used based on the analysis results in order to prioritize the use of fresher ingredients. For example, it can suggest a recipe that uses fresher ingredients first. This allows the user to suggest more delicious dishes by prioritizing the use of fresher ingredients.

[0055] The photo analysis unit can identify the origin and producer information of ingredients from photos of dishes, and suggest optimal dishes from the perspective of local production and consumption. The photo analysis unit, for example, analyzes photos of dishes and identifies the origin based on the characteristics of the ingredients. For example, it detects the characteristics of vegetables and fruits that are only produced in specific regions. The photo analysis unit also reads the label and packaging information of ingredients from photos to identify the producer information of ingredients. For example, it analyzes labels that list the farmer's name and place of production. The photo analysis unit also suggests recipes using local ingredients based on the analysis results in order to suggest optimal dishes from the perspective of local production and consumption. For example, it suggests dishes using local specialties. This makes it possible to suggest optimal dishes from the perspective of local production and consumption.

[0056] The photo analysis unit uses the emotion estimation function to analyze the emotions of a user who views a photo of food and can suggest dishes that elicit positive emotions. The photo analysis unit, for example, analyzes the facial expression of a user who views a photo of food and calculates an emotion score. For example, it detects smiling or surprised expressions and evaluates positive emotions. The photo analysis unit also uses the emotion estimation function to analyze the user's emotional response in real time and suggests dishes that elicit positive emotions. For example, it suggests recipes based on photos of food that make the user smile. The photo analysis unit also identifies dishes that evoke positive emotions in the user based on the emotion score and suggests recipes for those dishes. For example, it provides recipes that reflect the user's preferred ingredients and cooking methods. This makes it possible to suggest dishes that elicit positive emotions.

[0057] The photo analysis unit can analyze the nutritional value of ingredients included in a photo of a dish and suggest health-conscious dishes. The photo analysis unit, for example, analyzes a photo of a dish and identifies the type and amount of ingredients. For example, it identifies ingredients such as vegetables, meat, and fish and calculates the nutritional value of each. The photo analysis unit also suggests health-conscious dishes based on the nutritional value of the ingredients. For example, it provides recipes using ingredients rich in vitamins and minerals. The photo analysis unit also suggests dishes with an optimal nutritional balance based on the user's health condition and nutritional needs. For example, it suggests low-calorie dishes to a user who is on a diet. This makes it possible to suggest health-conscious dishes.

[0058] The photo analysis unit can identify allergen information for ingredients from photos of dishes and suggest dishes suitable for users with allergies. The photo analysis unit, for example, analyzes photos of dishes and identifies allergen ingredients. For example, it detects allergens such as nuts, dairy products, and gluten. The photo analysis unit also suggests alternative ingredients based on the allergen information to suggest dishes suitable for users with allergies. For example, it provides recipes that use soy milk instead of dairy products. The photo analysis unit also registers the user's allergy information in advance and suggests dishes that do not contain allergens based on that information. For example, if the user has a nut allergy, it provides recipes that do not contain nuts. This makes it possible to suggest dishes suitable for users with allergies.

[0059] The receipt analysis unit can analyze the expiration dates of products printed on receipts and suggest dishes that prioritize ingredients that should be consumed quickly. The receipt analysis unit, for example, analyzes the expiration dates of products printed on receipts and identifies ingredients that should be consumed quickly. For example, it lists ingredients that are close to their expiration date. The receipt analysis unit also suggests dishes that prioritize ingredients that are close to their expiration date. For example, it provides recipes for soups and salads that use vegetables that are close to their expiration date. The receipt analysis unit also suggests dishes that reduce waste based on ingredients that are close to their expiration date. For example, it provides recipes that use up ingredients that are close to their expiration date at once. In this way, it is possible to reduce ingredient waste by prioritizing the use of ingredients that should be consumed quickly.

[0060] The receipt analysis unit can analyze the price information of the products listed on the receipt and suggest dishes with high cost performance. For example, the receipt analysis unit analyzes the price information of the products listed on the receipt and identifies ingredients with high cost performance. For example, it lists ingredients that are inexpensive and have high nutritional value. The receipt analysis unit also suggests dishes with high cost performance based on the price information. For example, it provides recipes that are filling and use inexpensive ingredients. The receipt analysis unit also suggests dishes with optimal cost performance based on the user's budget. For example, it provides recipes that provide the maximum nutritional value within the budget. This makes it possible to suggest dishes with high cost performance.

[0061] The receipt analysis unit can use the emotion estimation function to analyze the user's emotion when purchasing the product listed on the receipt and suggest dishes that evoke positive emotions. The receipt analysis unit, for example, analyzes the user's emotion when purchasing the product listed on the receipt and calculates an emotion score. For example, it analyzes facial expressions and voice at the time of purchase to evaluate positive emotions. The receipt analysis unit also uses the emotion estimation function to suggest dishes that evoke positive emotions based on the emotion at the time of purchase. For example, it provides recipes using ingredients that the user enjoyed purchasing. The receipt analysis unit also identifies dishes that evoke positive emotions for the user based on the emotion score and suggests recipes for those dishes. For example, it provides recipes that reflect the user's preferred ingredients and cooking methods. This makes it possible to suggest dishes that evoke positive emotions.

[0062] The receipt analysis unit can analyze the eco-label information of the products printed on the receipt and suggest environmentally friendly dishes. For example, the receipt analysis unit can analyze the eco-label information of the products printed on the receipt and identify environmentally friendly ingredients. For example, it can detect organically grown or fair trade labels. The receipt analysis unit can also suggest environmentally friendly dishes based on the eco-label information. For example, it can provide recipes using organic vegetables or dishes using fair trade ingredients. The receipt analysis unit can also suggest optimal eco-friendly dishes based on the user's environmental awareness. For example, it can provide recipes using ingredients with a low carbon footprint. This makes it possible to suggest environmentally friendly dishes.

[0063] The receipt analysis unit uses the emotion estimation function to analyze the user's emotion at the time of purchasing the product listed on the receipt in real time and can suggest dishes that match the emotion. For example, the receipt analysis unit analyzes the user's emotion at the time of purchasing the product listed on the receipt in real time and calculates an emotion score. For example, it analyzes facial expressions and voice at the time of purchase and evaluates positive emotions. The receipt analysis unit also uses the emotion estimation function to suggest dishes that match the emotion based on the emotion at the time of purchase. For example, it provides recipes that use ingredients that the user was happy to purchase. The receipt analysis unit also identifies dishes that the user feels positive about based on the emotion score and suggests recipes for those dishes. For example, it provides recipes that reflect the user's preferred ingredients and cooking methods. This makes it possible to suggest dishes that match the emotion.

[0064] The recipe analysis unit can analyze the cooking time written in a recipe and suggest dishes that can be made in a short time. For example, the recipe analysis unit analyzes the cooking time written in a recipe and identifies dishes that can be made in a short time. For example, it lists recipes that can be made in 30 minutes or less. The recipe analysis unit also suggests dishes that can be made in a short time, suitable for busy users, based on the cooking time. For example, it provides dishes that can be made with simple steps. The recipe analysis unit also suggests dishes with optimal cooking times according to the user's schedule. For example, it provides recipes that can be made in a short time on a weekday evening. This makes it possible to suggest dishes that can be made in a short time.

[0065] The recipe analysis unit can analyze the cooking utensils listed in a recipe and suggest dishes that are suitable for the cooking utensils the user owns. For example, the recipe analysis unit analyzes the cooking utensils listed in a recipe and identifies dishes that are suitable for the cooking utensils the user owns. For example, it lists recipes that use frying pans and ovens. The recipe analysis unit also suggests dishes that can be made with the cooking utensils the user owns based on the cooking utensils. For example, it registers the cooking utensils the user owns and provides recipes that are suitable for them. The recipe analysis unit also suggests optimal recipes based on information about the user's cooking utensils. For example, it prioritizes suggesting dishes that use specific cooking utensils. This makes it possible to suggest dishes that are suitable for the cooking utensils the user owns.

[0066] The recipe analysis unit can use the emotion estimation function to analyze the emotions of a user who has viewed a recipe and suggest dishes that elicit positive emotions. The recipe analysis unit, for example, analyzes the facial expression of a user who has viewed a recipe and calculates an emotion score. For example, it detects smiling or surprised expressions and evaluates positive emotions. The recipe analysis unit also uses the emotion estimation function to analyze the user's emotional response in real time and suggest dishes that elicit positive emotions. For example, it suggests dishes based on recipes that make the user smile. The recipe analysis unit also identifies dishes that evoke positive emotions in the user based on the emotion score and suggests recipes for those dishes. For example, it provides recipes that reflect the user's preferred ingredients and cooking methods. This makes it possible to suggest dishes that elicit positive emotions.

[0067] The recipe analysis unit can suggest substitutes for ingredients listed in a recipe and suggest dishes that can be made with ingredients the user has. The recipe analysis unit, for example, analyzes the ingredients listed in a recipe and suggests substitutes. For example, it provides a recipe that uses chicken instead of beef. The recipe analysis unit also suggests dishes that use substitutes based on ingredients the user has. For example, it registers ingredients in the refrigerator and provides recipes that suit them. The recipe analysis unit also suggests dishes that can be made with ingredients the user has based on substitutes for ingredients. For example, it provides a recipe that uses a substitute if a particular ingredient is unavailable. This makes it possible to suggest dishes that can be made with ingredients the user has.

[0068] The recipe analysis unit can analyze the cooking steps written in a recipe and suggest dishes with simplified steps so that even beginners can easily make them. The recipe analysis unit, for example, analyzes the cooking steps written in a recipe and simplifies the steps so that even beginners can easily make them. For example, it omits complicated steps or replaces them with simple steps. The recipe analysis unit also suggests easy dishes for beginners based on the cooking steps. For example, it provides recipes that reduce cooking time or use fewer cooking utensils. The recipe analysis unit also suggests dishes with optimal cooking steps according to the user's cooking skill. For example, it provides step-by-step recipes for beginners. This makes it possible to suggest dishes that even beginners can easily make.

[0069] The recipe analysis unit uses the emotion estimation function to analyze the emotions of a user who has viewed a recipe in real time and can suggest dishes that correspond to the emotions. For example, the recipe analysis unit analyzes the facial expressions of a user who has viewed a recipe in real time and calculates an emotion score. For example, it detects smiling or surprised expressions and evaluates positive emotions. The recipe analysis unit also uses the emotion estimation function to analyze the user's emotional reactions in real time and suggests dishes that correspond to the emotions. For example, it suggests dishes based on recipes that make the user smile. The recipe analysis unit also identifies dishes that evoke positive emotions in the user based on the emotion score and suggests recipes for those dishes. For example, it provides recipes that reflect the user's preferred ingredients and cooking methods. This makes it possible to suggest dishes that correspond to the emotions.

[0070] The suggestion unit can increase the variety of dishes to be suggested and provide multiple options according to the user's preferences. For example, the suggestion unit provides recipes that combine different ingredients and cooking methods to increase the variety of dishes to be suggested. For example, different dishes using the same ingredients are suggested. Furthermore, the suggestion unit suggests recipes based on the user's past selection history to provide multiple options according to the user's preferences. For example, it analyzes trends in the dishes that the user prefers and provides recipes based on that. Furthermore, the suggestion unit provides recipes according to seasons or events to increase the variety of dishes to be suggested. For example, it suggests dishes using seasonal ingredients or dishes suitable for specific events. This makes it possible to provide multiple options according to the user's preferences.

[0071] The suggestion unit can analyze the difficulty of the dishes to be suggested and suggest the optimal dishes according to the user's cooking skill. For example, the suggestion unit analyzes the difficulty of the dishes to be suggested and identifies the optimal dishes according to the user's cooking skill. For example, it lists easy recipes for beginners and complex recipes for advanced cooks. The suggestion unit also provides recipes based on the user's past cooking history to suggest the optimal dishes according to the user's cooking skill. For example, it analyzes the difficulty of dishes the user has made in the past and provides recipes based on that. The suggestion unit also analyzes the difficulty of the dishes to be suggested and provides recipes to support the user's skill improvement. For example, it suggests dishes that are slightly more difficult, encouraging the user to improve their skills. This makes it possible to suggest the optimal dishes according to the user's cooking skill.

[0072] The suggestion unit uses the emotion estimation function to analyze the emotions of the user when they view the proposed dishes, and can suggest dishes that elicit positive emotions. For example, the suggestion unit analyzes the facial expression of the user when they view the proposed dishes and calculates an emotion score. For example, it detects expressions of smiles and surprise and evaluates positive emotions. The suggestion unit also uses the emotion estimation function to analyze the user's emotional reactions in real time and suggests dishes that elicit positive emotions. For example, it suggests recipes based on photos of dishes that make the user smile. The suggestion unit also identifies dishes that evoke positive emotions in the user based on the emotion score, and suggests recipes for those dishes. For example, it provides recipes that reflect the user's preferred ingredients and cooking methods. This makes it possible to suggest dishes that elicit positive emotions.

[0073] The suggestion unit can consider the seasonal feel of the proposed dish and suggest dishes that suit the season. For example, the suggestion unit provides recipes that use seasonal ingredients in order to consider the seasonal feel of the proposed dish. For example, it suggests salads using fresh vegetables in spring and hot soups in winter. The suggestion unit also provides recipes that are suitable for seasonal events and occasions in order to suggest dishes that suit the season. For example, it suggests recipes for special dinners at Christmas and barbecue recipes in summer. The suggestion unit also provides recipes based on the seasons of seasonal ingredients in order to suggest dishes that take the seasonal feel into consideration. For example, it suggests dishes that use seasonal fish and vegetables. This makes it possible to suggest dishes that suit the season.

[0074] The suggestion unit uses the emotion estimation function to analyze the emotions of the user when they view the proposed dishes in real time, and can suggest dishes according to their emotions. For example, the suggestion unit analyzes the facial expressions of the user when they view the proposed dishes in real time and calculates an emotion score. For example, it detects expressions of smiles and surprise and evaluates positive emotions. The suggestion unit also uses the emotion estimation function to analyze the user's emotional reactions in real time and suggests dishes according to their emotions. For example, it suggests recipes based on photos of dishes that make the user smile. The suggestion unit also identifies dishes that the user feels positive about based on the emotion score, and suggests recipes for those dishes. For example, it provides recipes that reflect the user's preferred ingredients and cooking methods. This makes it possible to suggest dishes according to emotions.

[0075] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0076] The recommendation system can further include a history analysis unit that analyzes the user's meal history. The history analysis unit, for example, collects and analyzes data on dishes the user has made or eaten in the past. This allows the system to understand the user's preferences and eating habits and suggest more personalized dishes. For example, new recipes can be suggested based on dishes the user has frequently made or liked to eat in the past. The history analysis unit can also make suggestions to improve nutritional balance based on the user's meal history. For example, it can identify nutrients that are lacking from the user's past meal history and suggest recipes to supplement them. This can support the user's health.

[0077] The recommendation system may further include a restriction management unit that manages information about the user's dietary restrictions. The restriction management unit, for example, registers the user's dietary restrictions (allergies, religious restrictions, diets, etc.) and filters the dishes to be suggested based on them. For example, if the user requires gluten-free meals, recipes that do not contain gluten may be suggested preferentially. The restriction management unit may also suggest alternative ingredients in accordance with the user's dietary restrictions. For example, a recipe that uses soy milk instead of dairy products may be provided. This makes it possible to suggest dishes that take the user's dietary restrictions into consideration.

[0078] The recommendation system may further include a satisfaction evaluation unit that evaluates the user's satisfaction with the meal. The satisfaction evaluation unit, for example, collects and analyzes feedback on the dishes prepared by the user. For example, the user may input an evaluation of the dish's taste, appearance, and the effort required for preparation. The satisfaction evaluation unit may also make improvements to improve the quality of the dishes it recommends based on the collected feedback. For example, if a user gives a low rating to a particular dish, the reason for this can be analyzed and reflected in the next recommendation. This can increase the user's satisfaction.

[0079] The recommendation system can further include a timing management unit that manages the timing of the user's meals. The timing management unit, for example, records the time periods and frequency of the user's meals and suggests optimal dishes based on that information. For example, it can provide recipes according to the time periods for breakfast, lunch, and dinner. The timing management unit can also make suggestions to help the user regulate their eating rhythm. For example, it can send reminders to ensure regular meal times. This can support the user's healthy eating habits.

[0080] The proposal system can further include a cost management unit that manages the cost of a user's meals. The cost management unit, for example, collects and analyzes price information on ingredients purchased by the user. This makes it possible to suggest dishes that fit the user's budget. For example, it can provide recipes that can be made on a low budget or recipes that use cost-effective ingredients. The cost management unit can also generate monthly food expense reports to support the user's food expense management. This allows the user to enjoy balanced meals while being conscious of saving on food expenses.

[0081] The recommendation system may further include an emotion analysis unit that estimates the user's emotions and suggests dishes based on the estimated emotions. The emotion analysis unit, for example, analyzes the user's facial expressions and voice when looking at a photo of a dish and calculates an emotion score. For example, it can suggest recipes based on photos of dishes that make the user smile. The emotion analysis unit can also analyze the user's emotional reactions in real time and suggest dishes that elicit positive emotions. For example, it can provide recipes for dishes that show the user's expressions of surprise or joy. This makes it possible to suggest dishes that correspond to the user's emotions.

[0082] The suggestion system can further include an emotion selection unit that estimates the user's emotion and selects ingredients based on the estimated emotion. The emotion selection unit, for example, analyzes the user's facial expressions and voice when selecting ingredients at a supermarket and calculates an emotion score. For example, it can suggest recipes based on ingredients that the user has selected with enjoyment. The emotion selection unit can also analyze the user's emotional response in real time and select ingredients that elicit positive emotions. For example, it can provide recipes for dishes using ingredients that make the user smile. This makes it possible to select ingredients according to the user's emotion.

[0083] The suggestion system can further include an emotion adjustment unit that estimates the user's emotions and adjusts the cooking steps based on the estimated emotions. The emotion adjustment unit, for example, analyzes the user's facial expressions and voice while cooking and calculates an emotion score. For example, if the user is feeling stressed, it can make suggestions to simplify the cooking steps. The emotion adjustment unit can also analyze the user's emotional reactions in real time and suggest cooking steps that elicit positive emotions. For example, it can provide steps that allow the user to enjoy cooking. This makes it possible to adjust the cooking steps according to the user's emotions.

[0084] The suggestion system may further include an emotion timing unit that estimates the user's emotions and suggests meal times based on the estimated emotions. The emotion timing unit, for example, analyzes the facial expressions and voices of the user during mealtimes and calculates an emotion score. For example, it can suggest meals during times when the user is relaxed. The emotion timing unit can also analyze the user's emotional responses in real time and suggest meal times that will elicit positive emotions. For example, it can provide recipes tailored to the times when the user is most relaxed. This makes it possible to suggest meal times that correspond to the user's emotions.

[0085] The suggestion system may further include an emotional environment unit that estimates the user's emotions and suggests a dining environment based on the estimated emotions. The emotional environment unit, for example, analyzes the user's emotions regarding the dining environment (lighting, music, table setting, etc.) and calculates an emotional score. For example, it may suggest a meal based on an environment that makes the user feel relaxed. The emotional environment unit may also analyze the user's emotional response in real time and suggest a dining environment that elicits positive emotions. For example, it may suggest lighting and music that are most relaxing for the user. This makes it possible to suggest a dining environment that matches the user's emotions.

[0086] The processing flow of the second embodiment will be briefly explained below.

[0087] Step 1: The photo analysis unit uses the generation AI to analyze photos of food. For example, a user can input a photo of food taken with a smartphone into the generation AI, which then analyzes the photo to identify the type of food and the ingredients used. The generation AI can also analyze a photo of curry to identify the type of curry and the ingredients used (meat, vegetables, spices, etc.). Step 2: The receipt analysis unit uses the generation AI to analyze supermarket receipts. For example, after a user goes shopping at a supermarket, the generation AI inputs the receipt, which analyzes the product names and quantities listed on the receipt to identify the ingredients purchased. In addition, if the receipt lists "chicken, onions, and carrots," the generation AI can also identify these ingredients. Step 3: The recipe analysis unit uses the generation AI to analyze the famous chef's recipe. For example, a user can input a recipe from a famous chef they found online into the generation AI, and the generation AI will analyze the recipe and identify the necessary ingredients and cooking steps. The generation AI can also analyze a recipe for "famous chef A's special pasta" and identify the necessary ingredients (pasta, tomatoes, basil, etc.) and cooking steps for that recipe. Step 4: The suggestion unit suggests the best dish for the user based on the information analyzed by the photo analysis unit, receipt analysis unit, and recipe analysis unit. For example, by combining the information analyzed by the generative AI, it can suggest "chicken and vegetable curry" or "special pasta" to the user. This allows the user to make effective use of ingredients in the refrigerator or purchased at the supermarket, and create delicious dishes based on recipes from famous chefs.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0092] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0101] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0107] 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.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The 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.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 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.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 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.

[0121] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0122] 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.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The 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.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] 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.

[0129] 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.

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

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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."

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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]

[0155] 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 photo analysis section that uses generative AI to analyze photos of food, A receipt analysis unit that uses generative AI to analyze supermarket receipts, A recipe analysis section that uses generative AI to analyze recipes from famous chefs, a suggestion unit that suggests optimal dishes to a user based on the information analyzed by the photo analysis unit, the receipt analysis unit, and the recipe analysis unit. A system characterized by:

2. The photo analysis unit Analyzes the freshness and quality of ingredients in photos of dishes and suggests using fresh ingredients first 2. The system of claim 1.

3. The receipt analysis unit Analyze the expiration dates of products printed on receipts and suggest dishes that prioritize ingredients that should be consumed quickly 2. The system of claim 1.

4. The recipe analysis unit Analyzes the cooking times listed in recipes and suggests dishes that can be made in a short amount of time 2. The system of claim 1.

5. The proposal unit Increase the variety of dishes offered and provide multiple options to suit users' preferences 2. The system of claim 1.

6. The photo analysis unit Analyzes the user's emotions when viewing food photos and suggests dishes that evoke positive emotions 2. The system of claim 1.

7. The receipt analysis unit Analyzes the user's emotions when purchasing items listed on the receipt and suggests dishes that evoke positive emotions 2. The system of claim 1.

8. The recipe analysis unit Analyzing the user's emotions when viewing a recipe and suggesting dishes that evoke positive emotions 2. The system of claim 1.

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