System

Abstract

An object of a system according to an embodiment is to provide information on bargain items and bargain items by efficiently utilizing local advertisement information.SOLUTION: A system includes an advertisement learning part, a notification part, a recipe generation part, a daily commodity notification part, and a point advice part. The advertisement learning unit learns an advertisement. The notification unit notifies the bargain item based on the advertisement information learned by the advertisement learning unit. The recipe generation unit generates a recipe using the commodity for special sale learned by the advertisement learning unit. The daily commodity notification unit notifies the user of special sale information of the daily commodities registered by the user. The point advice unit gives advice on effective use of points of the store.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to efficiently utilize local advertising information to obtain information on bargains and special offers.

[0005] The system according to the embodiment aims to provide information on bargains and special sales by efficiently utilizing local advertising information. [Means for solving the problem]

[0006] The system according to the embodiment includes an advertisement learning unit, a notification unit, a recipe generation unit, a household goods notification unit, and a points advice unit. The advertisement learning unit learns advertisements. The notification unit notifies the user of bargains based on advertising information learned by the advertisement learning unit. The recipe generation unit generates recipes using sale items learned by the advertisement learning unit. The household goods notification unit notifies the user of sale information for household goods registered by the user. The points advice unit provides advice on how to effectively use store points. [Effects of the Invention]

[0007] The system according to the embodiment can provide information on bargains and special sales by efficiently utilizing local advertising information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A bargain notification system according to an embodiment of the present invention learns local advertisements registered in LINE (registered trademark) flyers and notifies users of bargains. It also provides functions such as introducing recipes that can be made using only local sale items, providing information on household items that users want to purchase if they register the items, and offering advice on how to effectively use store points. This allows the bargain notification system to help users save on living expenses by providing them with information on local sale items, recipes, and sale information on household items, as well as effective ways to use points.

[0029] A bargain notification system according to an embodiment includes an advertising learning unit, a notification unit, a recipe generation unit, a daily necessities notification unit, and a points advice unit. The advertising learning unit learns advertisements. For example, the generation AI analyzes local advertisements registered in LINE (registered trademark) flyers and collects sale and discount information. The generation AI can also use a machine learning algorithm to learn advertising data and provide optimal advertising information to users. The generation AI can also analyze local event information and weather forecasts to identify related sale items. The notification unit notifies users of bargains based on the advertising information learned by the advertising learning unit. For example, the generation AI can notify users of sale information at supermarkets and drugstores in their local area. The generation AI can also learn users' purchasing histories and notify users of bargains optimized for each individual user. The generation AI can also estimate users' emotions and prioritize notifications of advertisements that evoke positive emotions. The recipe generation unit generates recipes using sale items learned by the advertising learning unit. For example, if chicken and vegetables are on sale, the generation AI will suggest a simple recipe using them. The generation AI can also learn the user's past cooking history and suggest recipes optimized for each individual user. Furthermore, the generation AI can analyze seasonal ingredient information and suggest recipes using seasonal sale items. The household goods notification unit notifies the user of sale information for household goods registered by the user. For example, if a user registers toilet paper or detergent, the generation AI will collect sale information for those items and notify the user. The generation AI can also learn the user's consumption patterns and notify the user of sale information based on the timing of consumption. Furthermore, the generation AI can analyze the user's lifestyle and living environment and notify the user of sale information based on that. The point advice unit provides advice on how to effectively use store points. For example, the generation AI can suggest days when the most points are accumulated and ways to use points to get the most value from shopping. The generation AI can also learn the user's point usage history and suggest optimal ways to use points. Furthermore, the generative AI can estimate the user's emotions and prioritize suggesting ways to use points that will elicit positive emotions.As a result, the bargain notification system according to the embodiment can help users save on living expenses by providing them with information on local sales, recipes, sales information on household goods, and ways to effectively use points. For example, the output unit displays sales information, recipes, and ways to use points to the user via a web application or a mobile application. If the user desires feedback in paper form, the results are printed using a printer. Sending the results via email provides quick feedback by sending the results directly to the user.

[0030] The advertising learning unit can learn a user's purchasing history and notify them of bargains optimized for each individual user. For example, the advertising learning unit uses a generation AI to analyze a user's past purchasing history and identify frequently purchased products and brands. For example, it prioritizes notifications of sale information for milk and bread, which the user frequently purchases. The advertising learning unit also identifies products related to specific seasons or events based on the user's purchasing history and notifies them at the appropriate time. For example, it provides sale information for sweets and decorations before Christmas. The advertising learning unit also learns purchasing history and suggests bargains tailored to the user's preferences and lifestyle. For example, it notifies a health-conscious user of sale information for organic foods. This improves the user's purchasing experience by notifying them of bargains optimized based on their purchasing history.

[0031] The advertising learning unit can analyze local event information and notify users of special sales related to the events. For example, the advertising learning unit analyzes a local event calendar and identifies products related to the events. For example, it notifies users of special sales information on related food and decorations in conjunction with local festivals and festivals. The advertising learning unit also prioritizes notifying users of products related to specific events based on the event information. For example, it provides information on special sales on barbecue supplies and drinks before a fireworks display. The advertising learning unit also collects local event information in real time and immediately notifies users of special sales related to the events. For example, it quickly suggests related products in conjunction with an event that is held at short notice. In this way, notifying users of special sales based on local event information allows users to efficiently purchase products related to the events.

[0032] The advertising learning unit can analyze local weather forecasts and notify users of bargains that correspond to the weather. For example, the advertising learning unit notifies users of special sales information for umbrellas and raincoats on rainy days based on the weather forecast. For example, it provides special sales information for umbrellas on days when rain is expected. The advertising learning unit also notifies users of special sales information for food and beverages that correspond to the weather. For example, it provides special sales information for ice cream and cold beverages on hot days. The advertising learning unit also analyzes seasonal weather forecasts and notifies users of bargains that correspond to the season. For example, it provides special sales information for heating appliances and hot drinks in winter. In this way, by notifying users of bargains that correspond to the weather, users can purchase the products they need at the appropriate time.

[0033] The advertising learning unit can analyze local traffic information and notify users of sale items at easily accessible stores. For example, the advertising learning unit, based on traffic information, notifies users of sale information at stores located on routes with less congestion and traffic restrictions. For example, it prioritizes providing sale information at easily accessible stores. The advertising learning unit also analyzes the operation status of public transportation and notifies users of sale information at stores close to easily accessible stations or bus stops. For example, it provides sale information at stores within walking distance of the nearest station. The advertising learning unit also collects traffic information in real time and immediately notifies users of sale items at easily accessible stores. For example, it provides sale information at stores in areas where traffic congestion does not occur. This allows users to shop efficiently by notifying them of sale items at easily accessible stores.

[0034] The recipe generation unit can learn the user's past cooking history and suggest recipes optimized for each individual user. For example, the recipe generation unit uses a generation AI to analyze the user's past cooking history and identify frequently cooked dishes and favorite ingredients. For example, it suggests special sale recipes that go well with pasta dishes the user often makes. The recipe generation unit also suggests recipes related to specific seasons or events based on the user's cooking history. For example, it provides a recipe for Christmas dinner using special sale items before Christmas. The recipe generation unit also learns the cooking history and suggests recipes that match the user's preferences and lifestyle. For example, it notifies a health-conscious user of low-calorie special sale recipes. This improves the user's cooking experience by suggesting optimized recipes based on the user's past cooking history.

[0035] The recipe generation unit can analyze seasonal ingredient information and suggest recipes using seasonal special sale items. For example, the recipe generation unit suggests recipes using seasonal special sale items based on seasonal ingredient information. For example, in spring, it provides a salad recipe using fresh vegetables. The recipe generation unit also suggests special sale recipes that match seasonal events and occasions. For example, it provides a barbecue recipe to match a summer festival. The recipe generation unit also collects seasonal ingredient information in real time and instantly suggests recipes using seasonal special sale items. For example, it provides a recipe for a dish using mushrooms in autumn. In this way, by suggesting recipes based on seasonal ingredient information, users can enjoy cooking using seasonal ingredients.

[0036] The recipe generation unit can analyze the user's health information and suggest recipes using special sale items according to the user's health condition. For example, the recipe generation unit suggests healthy recipes using special sale items based on the user's health information. For example, it provides low-carbohydrate recipes to a user with diabetes. The recipe generation unit also analyzes the health information and suggests special sale recipes that are rich in specific nutrients. For example, it provides recipes using ingredients that are rich in vitamin C. The recipe generation unit also suggests special sale recipes in real time according to the user's health condition. For example, it provides low-calorie recipes to a user who is on a diet. In this way, it is possible to support the user's health by suggesting recipes based on the user's health information.

[0037] The recipe generation unit can analyze the user's family composition and suggest recipes using special sale items that the whole family can enjoy. For example, the recipe generation unit suggests special sale recipes that the whole family can enjoy based on the user's family composition. For example, it can provide a hamburger steak recipe that children will enjoy. The recipe generation unit can also analyze the family composition and suggest special sale recipes tailored to a specific age group. For example, it can provide recipes that are easy to digest for the elderly. The recipe generation unit can also suggest special sale recipes that the whole family can enjoy in real time. For example, it can provide recipes for party menus that the whole family can enjoy. In this way, by suggesting recipes based on the user's family composition, it is possible to provide dishes that the whole family can enjoy.

[0038] The household goods notification unit can learn the user's consumption patterns and notify them of sale information according to the timing of consumption. For example, the generation AI analyzes the user's past consumption patterns and identifies household goods that are frequently purchased. For example, it notifies the user of sale information for toilet paper, which the user often purchases. The household goods notification unit also identifies household goods related to specific seasons or events based on consumption patterns and notifies them at the appropriate time. For example, it provides sale information for sunscreen before summer. The household goods notification unit also learns consumption patterns and suggests sale information tailored to the user's lifestyle. For example, it notifies a user who likes the outdoors of sale information for camping equipment. In this way, by notifying the user of sale information based on the user's consumption patterns, the user can purchase the household goods they need at the appropriate time.

[0039] The lifestyle goods notification unit can analyze a user's lifestyle and notify the user of sale information that suits the lifestyle. For example, the generation AI in the lifestyle goods notification unit analyzes the user's lifestyle and notifies the user of sale information that suits the specific lifestyle. For example, a health-conscious user can be provided with sale information on organic foods and fitness products. The lifestyle goods notification unit can also notify the user of sale information related to specific hobbies and interests based on the user's lifestyle. For example, a user who loves gardening can be provided with sale information on gardening products. The lifestyle goods notification unit can also analyze a user's lifestyle in real time and instantly notify the user of sale information that suits the user's lifestyle. For example, a user who loves traveling can be provided with sale information on travel goods. In this way, by notifying the user of sale information based on their lifestyle, it is possible to provide products that suit the user's lifestyle.

[0040] The household goods notification unit can analyze the user's living environment and notify the user of sale information appropriate for that living environment. For example, the generation AI analyzes the user's living environment and notifies the user of sale information tailored to the specific living environment. For example, a user living in an urban area is provided with sale information on compact furniture and storage items. The household goods notification unit also notifies the user of sale information related to a specific climate or region based on the user's living environment. For example, a user living in a cold region is provided with sale information on heating appliances. The household goods notification unit also analyzes the living environment in real time and immediately notifies the user of sale information appropriate for the user's living environment. For example, a user living by the sea is provided with sale information on beach goods. In this way, by notifying the user of sale information based on their living environment, it is possible to provide products that suit the user's living environment.

[0041] The household goods notification unit can analyze the user's hobbies and interests and notify the user of sale information that matches the hobbies and interests. For example, the generation AI in the household goods notification unit analyzes the user's hobbies and interests and notifies the user of sale information tailored to the specific hobbies and interests. For example, a user who loves music can be provided with sale information on musical instruments and audio equipment. The household goods notification unit can also notify the user of sale information related to specific activities based on the user's hobbies and interests. For example, a user who loves the outdoors can be provided with sale information on camping equipment. The household goods notification unit can also analyze the hobbies and interests in real time and instantly notify the user of sale information that matches the user's hobbies and interests. For example, a user who loves cooking can be provided with sale information on kitchenware. This can improve the user's purchasing experience by notifying the user of sale information based on the user's hobbies and interests.

[0042] The point advice unit can learn the user's point usage history and suggest the optimal way to use points. For example, the point advice unit uses a generation AI to analyze the user's past point usage history and identify frequently used stores and services. For example, it can suggest ways to use points at supermarkets that the user frequently visits. The point advice unit can also suggest ways to use points related to specific seasons or events based on the point usage history. For example, it can provide ways to use points to get great deals on shopping before Christmas. The point advice unit can also learn the point usage history and suggest ways to use points that suit the user's lifestyle. For example, a user who likes to travel can be notified of travel-related point usage methods. This makes it possible to maximize the user's point utilization by suggesting the optimal way to use points based on the user's point usage history.

[0043] The point advice unit can analyze store campaign information and suggest point usage methods that correspond to the campaign. For example, the point advice unit suggests point usage methods that match a specific campaign based on the store campaign information. For example, it can provide a method for shopping on a double points day. The point advice unit can also analyze campaign information and suggest point usage methods related to a specific event or sale. For example, it can provide a point usage method that matches a Black Friday sale. The point advice unit can also collect campaign information in real time and instantly suggest the most suitable point usage method for the user. For example, it can provide a point usage method that matches a sale that is suddenly held. In this way, by suggesting point usage methods based on the store's campaign information, the user can make the most of the campaign.

[0044] The point advice unit can analyze a user's purchase history and suggest ways to use points based on the purchase history. For example, the point advice unit uses a generation AI to analyze a user's past purchase history and identify frequently purchased products and services. For example, it suggests ways to use points for daily necessities that the user often purchases. The point advice unit also suggests ways to use points related to specific seasons or events based on the purchase history. For example, it provides ways to use points to get great deals on shopping before summer. The point advice unit also learns the purchase history and suggests ways to use points that suit the user's lifestyle. For example, it notifies a user who loves the outdoors how to use points for camping equipment. In this way, it is possible to maximize the user's use of points by suggesting ways to use points based on the user's purchase history.

[0045] The point advice unit can analyze the user's household information and suggest ways to use points that suit the household finances. For example, the point advice unit uses a generation AI to analyze the user's household information and suggest ways to use points that suit a specific household situation. For example, it provides a way to use points to get good deals on shopping for a budget-conscious user. The point advice unit also suggests ways to use points related to specific expenditure items based on the household information. For example, it provides ways to use points to reduce food costs. The point advice unit also analyzes the household information in real time and instantly suggests ways to use points that suit the user's household situation. For example, it provides ways to save money by using points when an unexpected expense occurs. In this way, it is possible to support the user's household finances by suggesting ways to use points based on the user's household information.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The bargain notification system can also include a health advice unit that analyzes the user's health information and notifies them of special offers based on their health condition. For example, the generation AI can provide information on special offers on low-calorie foods and vitamin-rich foods based on the user's health data. The generation AI can also analyze the user's allergy information and notify them of special offers that do not contain allergens. Furthermore, the generation AI can analyze the user's exercise habits and provide information on special offers on sports equipment and health supplements. This can support the user in maintaining their health by notifying them of special offers based on their health condition.

[0048] The bargain notification system can also include a hobby advice unit that analyzes the user's hobbies and interests and notifies them of special offers based on their hobbies and interests. For example, the generation AI can provide information on special offers on gardening supplies and DIY supplies based on the user's hobby data. The generation AI can also analyze the user's music preferences and notify them of special offers on musical instruments and audio equipment. Furthermore, the generation AI can analyze the user's travel history and provide information on special offers on travel goods and outdoor equipment. This can enrich the user's lifestyle by notifying them of special offers based on their hobbies and interests.

[0049] The bargain notification system can also include a family advice unit that analyzes the user's family composition and notifies the user of special sales items that the whole family can enjoy. For example, the generation AI can provide information on special sales items for children's toys and educational supplies based on the user's family composition data. The generation AI can also provide information on special sales items for health foods and nursing care products for the elderly. The generation AI can also provide information on special sales items for leisure and party supplies that the whole family can enjoy. This makes it possible to provide products that the whole family can enjoy by notifying the user of special sales items that suit the user's family composition.

[0050] The bargain notification system can also include a living environment advice unit that analyzes the user's living environment and notifies them of special sales items appropriate for that living environment. For example, the generation AI can provide users living in urban areas with sale information on compact furniture and storage items based on the user's living environment data. The generation AI can also notify users living in cold regions of sale information on heating appliances and cold weather gear. Furthermore, the generation AI can provide users living by the sea with sale information on beach and outdoor gear. This allows the system to provide products that suit the user's living environment by notifying them of sale items appropriate for their living environment.

[0051] The bargain notification system can also be equipped with a consumption advice unit that analyzes the user's consumption patterns and notifies them of sale information according to the timing of consumption. For example, the generation AI can provide sale information for frequently purchased household goods based on the user's past consumption patterns. The generation AI can also notify them of sale information for household goods related to specific seasons or events. Furthermore, the generation AI can provide sale information tailored to the user's lifestyle. In this way, by notifying them of sale information based on their consumption patterns, they can purchase the products they need at the appropriate time.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The advertising learning unit learns advertisements. For example, the generation AI analyzes local advertisements registered in LINE (registered trademark) flyers and collects information on special sales and discounts. The generation AI can also use machine learning algorithms to learn from advertising data and provide optimal advertising information to users. Furthermore, the generation AI can analyze local event information and weather forecasts to identify related sales items. Step 2: The notification unit notifies the user of bargains based on the advertising information learned by the advertising learning unit. For example, the generation AI notifies the user of sale information at supermarkets and drugstores in the user's area. The generation AI can also learn the user's purchasing history and notify the user of bargains optimized for each individual user. Furthermore, the generation AI can estimate the user's emotions and prioritize advertisements that evoke positive emotions. Step 3: The recipe generation unit generates recipes using the sale items learned by the advertising learning unit. For example, if the sale items include chicken and vegetables, the generation AI will suggest a simple recipe using them. The generation AI can also learn the user's past cooking history and suggest recipes optimized for each individual user. Furthermore, the generation AI can analyze seasonal ingredient information and suggest recipes using sale items according to the season. Step 4: The household goods notification unit notifies the user of sale information for household goods registered by the user. For example, if the user registers toilet paper or detergent, the generation AI will collect sale information for those items and notify the user. The generation AI can also learn the user's consumption patterns and notify the user of sale information according to the timing of consumption. Furthermore, the generation AI can analyze the user's lifestyle and living environment and notify the user of sale information accordingly. Step 5: The point advice unit provides advice on how to effectively use store points. For example, the generation AI suggests days when points are most likely to be accumulated and ways to use points to get great deals on shopping. The generation AI can also learn the user's point usage history and suggest optimal ways to use points. Furthermore, the generation AI can estimate the user's emotions and prioritize suggesting ways to use points that will elicit positive emotions.

[0054] (Example 2) A bargain notification system according to an embodiment of the present invention learns local advertisements registered in LINE (registered trademark) flyers and notifies users of bargains. It also provides functions such as introducing recipes that can be made using only local sale items, providing information on household items that users want to purchase if they register the items, and offering advice on how to effectively use store points. This allows the bargain notification system to help users save on living expenses by providing them with information on local sale items, recipes, and sale information on household items, as well as effective ways to use points.

[0055] A bargain notification system according to an embodiment includes an advertising learning unit, a notification unit, a recipe generation unit, a daily necessities notification unit, and a points advice unit. The advertising learning unit learns advertisements. For example, the generation AI analyzes local advertisements registered in LINE (registered trademark) flyers and collects sale and discount information. The generation AI can also use a machine learning algorithm to learn advertising data and provide optimal advertising information to users. The generation AI can also analyze local event information and weather forecasts to identify related sale items. The notification unit notifies users of bargains based on the advertising information learned by the advertising learning unit. For example, the generation AI can notify users of sale information at supermarkets and drugstores in their local area. The generation AI can also learn users' purchasing histories and notify users of bargains optimized for each individual user. The generation AI can also estimate users' emotions and prioritize notifications of advertisements that evoke positive emotions. The recipe generation unit generates recipes using sale items learned by the advertising learning unit. For example, if chicken and vegetables are on sale, the generation AI will suggest a simple recipe using them. The generation AI can also learn the user's past cooking history and suggest recipes optimized for each individual user. Furthermore, the generation AI can analyze seasonal ingredient information and suggest recipes using seasonal sale items. The household goods notification unit notifies the user of sale information for household goods registered by the user. For example, if a user registers toilet paper or detergent, the generation AI will collect sale information for those items and notify the user. The generation AI can also learn the user's consumption patterns and notify the user of sale information based on the timing of consumption. Furthermore, the generation AI can analyze the user's lifestyle and living environment and notify the user of sale information based on that. The point advice unit provides advice on how to effectively use store points. For example, the generation AI can suggest days when the most points are accumulated and ways to use points to get the most value from shopping. The generation AI can also learn the user's point usage history and suggest optimal ways to use points. Furthermore, the generative AI can estimate the user's emotions and prioritize suggesting ways to use points that will elicit positive emotions.As a result, the bargain notification system according to the embodiment can help users save on living expenses by providing them with information on local sales, recipes, sales information on household goods, and ways to effectively use points. For example, the output unit displays sales information, recipes, and ways to use points to the user via a web application or a mobile application. If the user desires feedback in paper form, the results are printed using a printer. Sending the results via email provides quick feedback by sending the results directly to the user.

[0056] The advertising learning unit can learn a user's purchasing history and notify them of bargains optimized for each individual user. For example, the advertising learning unit uses a generation AI to analyze a user's past purchasing history and identify frequently purchased products and brands. For example, it prioritizes notifications of sale information for milk and bread, which the user frequently purchases. The advertising learning unit also identifies products related to specific seasons or events based on the user's purchasing history and notifies them at the appropriate time. For example, it provides sale information for sweets and decorations before Christmas. The advertising learning unit also learns purchasing history and suggests bargains tailored to the user's preferences and lifestyle. For example, it notifies a health-conscious user of sale information for organic foods. This improves the user's purchasing experience by notifying them of bargains optimized based on their purchasing history.

[0057] The advertising learning unit can analyze local event information and notify users of special sales related to the events. For example, the advertising learning unit analyzes a local event calendar and identifies products related to the events. For example, it notifies users of special sales information on related food and decorations in conjunction with local festivals and festivals. The advertising learning unit also prioritizes notifying users of products related to specific events based on the event information. For example, it provides information on special sales on barbecue supplies and drinks before a fireworks display. The advertising learning unit also collects local event information in real time and immediately notifies users of special sales related to the events. For example, it quickly suggests related products in conjunction with an event that is held at short notice. In this way, notifying users of special sales based on local event information allows users to efficiently purchase products related to the events.

[0058] The advertising learning unit uses the emotion estimation function to analyze the emotions a user feels toward a particular advertisement and can prioritize the notification of advertisements that elicit positive emotions. For example, the advertising learning unit analyzes the user's facial expressions and voice when viewing an advertisement and calculates an emotion score. For example, if a smile or an excited voice is detected, the advertising learning unit prioritizes the notification of that advertisement. The advertising learning unit also uses the emotion estimation function to identify advertisements that evoke positive emotions in the user and prioritizes the display of similar advertisements. For example, the advertising learning unit notifies the user of advertisements that elicit joy. The advertising learning unit also monitors the user's emotional responses in real time and dynamically selects advertisements that elicit positive emotions. For example, the advertising learning unit immediately notifies the user of advertisements that interest the user. In this way, by selecting advertisements based on the user's emotions, the user's purchasing motivation can be increased.

[0059] The advertising learning unit can analyze local weather forecasts and notify users of bargains that correspond to the weather. For example, the advertising learning unit notifies users of special sales information for umbrellas and raincoats on rainy days based on the weather forecast. For example, it provides special sales information for umbrellas on days when rain is expected. The advertising learning unit also notifies users of special sales information for food and beverages that correspond to the weather. For example, it provides special sales information for ice cream and cold beverages on hot days. The advertising learning unit also analyzes seasonal weather forecasts and notifies users of bargains that correspond to the season. For example, it provides special sales information for heating appliances and hot drinks in winter. In this way, by notifying users of bargains that correspond to the weather, users can purchase the products they need at the appropriate time.

[0060] The advertising learning unit can analyze local traffic information and notify users of sale items at easily accessible stores. For example, the advertising learning unit, based on traffic information, notifies users of sale information at stores located on routes with less congestion and traffic restrictions. For example, it prioritizes providing sale information at easily accessible stores. The advertising learning unit also analyzes the operation status of public transportation and notifies users of sale information at stores close to easily accessible stations or bus stops. For example, it provides sale information at stores within walking distance of the nearest station. The advertising learning unit also collects traffic information in real time and immediately notifies users of sale items at easily accessible stores. For example, it provides sale information at stores in areas where traffic congestion does not occur. This allows users to shop efficiently by notifying them of sale items at easily accessible stores.

[0061] The advertising learning unit uses the emotion estimation function to analyze the emotions of a user when viewing sale items in real time, and can prioritize notification of sale items that elicit positive emotions. For example, the advertising learning unit analyzes the user's facial expressions and voice when viewing sale items and calculates an emotion score. For example, if a smile or excited voice is detected, the advertising learning unit prioritizes notification of those sale items. The advertising learning unit also uses the emotion estimation function to identify sale items that evoke positive emotions in the user and prioritizes displaying similar sale items. For example, the advertising learning unit notifies the user of sale items that elicit joy. The advertising learning unit also monitors the user's emotional responses in real time and dynamically selects sale items that elicit positive emotions. For example, the advertising learning unit immediately notifies the user of sale items that interest the user. This allows the user's purchasing motivation to be increased by selecting sale items based on the user's emotions.

[0062] The recipe generation unit can learn the user's past cooking history and suggest recipes optimized for each individual user. For example, the recipe generation unit uses a generation AI to analyze the user's past cooking history and identify frequently cooked dishes and favorite ingredients. For example, it suggests special sale recipes that go well with pasta dishes the user often makes. The recipe generation unit also suggests recipes related to specific seasons or events based on the user's cooking history. For example, it provides a recipe for Christmas dinner using special sale items before Christmas. The recipe generation unit also learns the cooking history and suggests recipes that match the user's preferences and lifestyle. For example, it notifies a health-conscious user of low-calorie special sale recipes. This improves the user's cooking experience by suggesting optimized recipes based on the user's past cooking history.

[0063] The recipe generation unit can analyze seasonal ingredient information and suggest recipes using seasonal special sale items. For example, the recipe generation unit suggests recipes using seasonal special sale items based on seasonal ingredient information. For example, in spring, it provides a salad recipe using fresh vegetables. The recipe generation unit also suggests special sale recipes that match seasonal events and occasions. For example, it provides a barbecue recipe to match a summer festival. The recipe generation unit also collects seasonal ingredient information in real time and instantly suggests recipes using seasonal special sale items. For example, it provides a recipe for a dish using mushrooms in autumn. In this way, by suggesting recipes based on seasonal ingredient information, users can enjoy cooking using seasonal ingredients.

[0064] The recipe generation unit uses the emotion estimation function to analyze the user's emotions toward a specific recipe and can prioritize suggesting recipes that elicit positive emotions. For example, the recipe generation unit analyzes the user's facial expressions and voice when browsing recipes and calculates an emotion score. For example, if a smile or excited voice is detected, the recipe generation unit prioritizes suggesting that recipe. The recipe generation unit also uses the emotion estimation function to identify recipes that evoke positive emotions in the user and prioritizes displaying similar recipes. For example, it suggests many recipes that make the user feel happy. The recipe generation unit also monitors the user's emotional responses in real time and dynamically selects recipes that elicit positive emotions. For example, it instantly suggests recipes that the user is interested in. This improves the user's cooking experience by selecting recipes based on the user's emotions.

[0065] The recipe generation unit can analyze the user's health information and suggest recipes using special sale items according to the user's health condition. For example, the recipe generation unit suggests healthy recipes using special sale items based on the user's health information. For example, it provides low-carbohydrate recipes to a user with diabetes. The recipe generation unit also analyzes the health information and suggests special sale recipes that are rich in specific nutrients. For example, it provides recipes using ingredients that are rich in vitamin C. The recipe generation unit also suggests special sale recipes in real time according to the user's health condition. For example, it provides low-calorie recipes to a user who is on a diet. In this way, it is possible to support the user's health by suggesting recipes based on the user's health information.

[0066] The recipe generation unit can analyze the user's family composition and suggest recipes using special sale items that the whole family can enjoy. For example, the recipe generation unit suggests special sale recipes that the whole family can enjoy based on the user's family composition. For example, it can provide a hamburger steak recipe that children will enjoy. The recipe generation unit can also analyze the family composition and suggest special sale recipes tailored to a specific age group. For example, it can provide recipes that are easy to digest for the elderly. The recipe generation unit can also suggest special sale recipes that the whole family can enjoy in real time. For example, it can provide recipes for party menus that the whole family can enjoy. In this way, by suggesting recipes based on the user's family composition, it is possible to provide dishes that the whole family can enjoy.

[0067] The recipe generation unit uses the emotion estimation function to analyze the emotions of a user when viewing a recipe in real time and can preferentially suggest recipes that elicit positive emotions. For example, the recipe generation unit analyzes the user's facial expressions and voice when viewing a recipe and calculates an emotion score. For example, if a smile or an excited voice is detected, the recipe generation unit preferentially suggests that recipe. The recipe generation unit also uses the emotion estimation function to identify recipes that evoke positive emotions in the user and preferentially display similar recipes. For example, it suggests many recipes that make the user feel happy. The recipe generation unit also monitors the user's emotional responses in real time and dynamically selects recipes that elicit positive emotions. For example, it immediately suggests recipes that the user is interested in. This allows the user's cooking experience to be improved by selecting recipes based on the user's emotions.

[0068] The household goods notification unit can learn the user's consumption patterns and notify them of sale information according to the timing of consumption. For example, the generation AI analyzes the user's past consumption patterns and identifies household goods that are frequently purchased. For example, it notifies the user of sale information for toilet paper, which the user often purchases. The household goods notification unit also identifies household goods related to specific seasons or events based on consumption patterns and notifies them at the appropriate time. For example, it provides sale information for sunscreen before summer. The household goods notification unit also learns consumption patterns and suggests sale information tailored to the user's lifestyle. For example, it notifies a user who likes the outdoors of sale information for camping equipment. In this way, by notifying the user of sale information based on the user's consumption patterns, the user can purchase the household goods they need at the appropriate time.

[0069] The lifestyle goods notification unit can analyze a user's lifestyle and notify the user of sale information that suits the lifestyle. For example, the generation AI in the lifestyle goods notification unit analyzes the user's lifestyle and notifies the user of sale information that suits the specific lifestyle. For example, a health-conscious user can be provided with sale information on organic foods and fitness products. The lifestyle goods notification unit can also notify the user of sale information related to specific hobbies and interests based on the user's lifestyle. For example, a user who loves gardening can be provided with sale information on gardening products. The lifestyle goods notification unit can also analyze a user's lifestyle in real time and instantly notify the user of sale information that suits the user's lifestyle. For example, a user who loves traveling can be provided with sale information on travel goods. In this way, by notifying the user of sale information based on their lifestyle, it is possible to provide products that suit the user's lifestyle.

[0070] The household goods notification unit uses an emotion estimation function to analyze the user's emotions toward specific household goods and prioritizes notifying the user of sale information that elicits positive emotions. For example, the household goods notification unit analyzes the user's facial expressions and voice when browsing sale information for household goods and calculates an emotion score. For example, if a smile or an excited voice is detected, the unit prioritizes notifying the user of sale information. The household goods notification unit also uses the emotion estimation function to identify household goods that the user evokes positive emotions for and prioritizes displaying similar sale information. For example, the unit notifies the user of more sale information for household goods that elicit joy. The household goods notification unit also monitors the user's emotional responses in real time and dynamically selects sale information that elicits positive emotions. For example, the unit immediately notifies the user of sale information for household goods that the user is interested in. This allows the user's purchasing motivation to be increased by selecting sale information based on the user's emotions.

[0071] The household goods notification unit can analyze the user's living environment and notify the user of sale information appropriate for that living environment. For example, the generation AI analyzes the user's living environment and notifies the user of sale information tailored to the specific living environment. For example, a user living in an urban area is provided with sale information on compact furniture and storage items. The household goods notification unit also notifies the user of sale information related to a specific climate or region based on the user's living environment. For example, a user living in a cold region is provided with sale information on heating appliances. The household goods notification unit also analyzes the living environment in real time and immediately notifies the user of sale information appropriate for the user's living environment. For example, a user living by the sea is provided with sale information on beach goods. In this way, by notifying the user of sale information based on their living environment, it is possible to provide products that suit the user's living environment.

[0072] The household goods notification unit can analyze the user's hobbies and interests and notify the user of sale information that matches the hobbies and interests. For example, the generation AI in the household goods notification unit analyzes the user's hobbies and interests and notifies the user of sale information tailored to the specific hobbies and interests. For example, a user who loves music can be provided with sale information on musical instruments and audio equipment. The household goods notification unit can also notify the user of sale information related to specific activities based on the user's hobbies and interests. For example, a user who loves the outdoors can be provided with sale information on camping equipment. The household goods notification unit can also analyze the hobbies and interests in real time and instantly notify the user of sale information that matches the user's hobbies and interests. For example, a user who loves cooking can be provided with sale information on kitchenware. This can improve the user's purchasing experience by notifying the user of sale information based on the user's hobbies and interests.

[0073] The daily necessities notification unit uses the emotion estimation function to analyze the user's emotions in real time when viewing sale information for daily necessities, and can prioritize notifying the user of sale information that elicits positive emotions. For example, the daily necessities notification unit analyzes the user's facial expressions and voice when viewing sale information for daily necessities and calculates an emotion score. For example, if a smile or an excited voice is detected, the unit prioritizes notifying the user of sale information. The daily necessities notification unit also uses the emotion estimation function to identify daily necessities that the user feels positive about and prioritizes displaying similar sale information. For example, the unit notifies the user of more sale information for daily necessities that elicit joy. The daily necessities notification unit also monitors the user's emotional reactions in real time and dynamically selects sale information that elicits positive emotions. For example, the unit immediately notifies the user of sale information for daily necessities that the user is interested in. In this way, sale information can be selected based on the user's emotions, thereby increasing the user's purchasing motivation.

[0074] The point advice unit can learn the user's point usage history and suggest the optimal way to use points. For example, the point advice unit uses a generation AI to analyze the user's past point usage history and identify frequently used stores and services. For example, it can suggest ways to use points at supermarkets that the user frequently visits. The point advice unit can also suggest ways to use points related to specific seasons or events based on the point usage history. For example, it can provide ways to use points to get great deals on shopping before Christmas. The point advice unit can also learn the point usage history and suggest ways to use points that suit the user's lifestyle. For example, a user who likes to travel can be notified of travel-related point usage methods. This makes it possible to maximize the user's point utilization by suggesting the optimal way to use points based on the user's point usage history.

[0075] The point advice unit can analyze store campaign information and suggest point usage methods that correspond to the campaign. For example, the point advice unit suggests point usage methods that match a specific campaign based on the store campaign information. For example, it can provide a method for shopping on a double points day. The point advice unit can also analyze campaign information and suggest point usage methods related to a specific event or sale. For example, it can provide a point usage method that matches a Black Friday sale. The point advice unit can also collect campaign information in real time and instantly suggest the most suitable point usage method for the user. For example, it can provide a point usage method that matches a sale that is suddenly held. In this way, by suggesting point usage methods based on the store's campaign information, the user can make the most of the campaign.

[0076] The point advice unit uses an emotion estimation function to analyze the user's emotions regarding point usage and can prioritize suggesting point usage methods that elicit positive emotions. The point advice unit, for example, analyzes the user's facial expressions and voice when browsing point usage methods and calculates an emotion score. For example, if a smile or an excited voice is detected, the point advice unit prioritizes suggesting those point usage methods. The point advice unit also uses the emotion estimation function to identify point usage methods that evoke positive emotions in the user and prioritizes displaying similar methods. For example, it suggests many point usage methods that elicit joy in the user. The point advice unit also monitors the user's emotional responses in real time and dynamically selects point usage methods that elicit positive emotions. For example, it instantly suggests point usage methods that the user is interested in. This allows the user's point utilization to be maximized by selecting point usage methods based on the user's emotions.

[0077] The point advice unit can analyze a user's purchase history and suggest ways to use points based on the purchase history. For example, the point advice unit uses a generation AI to analyze a user's past purchase history and identify frequently purchased products and services. For example, it suggests ways to use points for daily necessities that the user often purchases. The point advice unit also suggests ways to use points related to specific seasons or events based on the purchase history. For example, it provides ways to use points to get great deals on shopping before summer. The point advice unit also learns the purchase history and suggests ways to use points that suit the user's lifestyle. For example, it notifies a user who loves the outdoors how to use points for camping equipment. In this way, it is possible to maximize the user's use of points by suggesting ways to use points based on the user's purchase history.

[0078] The point advice unit can analyze the user's household information and suggest ways to use points that suit the household finances. For example, the point advice unit uses a generation AI to analyze the user's household information and suggest ways to use points that suit a specific household situation. For example, it provides a way to use points to get good deals on shopping for a budget-conscious user. The point advice unit also suggests ways to use points related to specific expenditure items based on the household information. For example, it provides ways to use points to reduce food costs. The point advice unit also analyzes the household information in real time and instantly suggests ways to use points that suit the user's household situation. For example, it provides ways to save money by using points when an unexpected expense occurs. In this way, it is possible to support the user's household finances by suggesting ways to use points based on the user's household information.

[0079] The point advice unit uses an emotion estimation function to analyze the user's emotions in real time when viewing point usage methods, and can prioritize suggesting point usage methods that elicit positive emotions. The point advice unit, for example, analyzes the user's facial expressions and voice when viewing point usage methods and calculates an emotion score. For example, if a smile or an excited voice is detected, the point advice unit prioritizes suggesting those point usage methods. The point advice unit also uses the emotion estimation function to identify point usage methods that evoke positive emotions in the user and prioritizes displaying similar methods. For example, it suggests many point usage methods that elicit joy in the user. The point advice unit also monitors the user's emotional responses in real time and dynamically selects point usage methods that elicit positive emotions. For example, it immediately suggests point usage methods that the user is interested in. This allows the user's point utilization to be maximized by selecting point usage methods based on the user's emotions.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The bargain notification system can also include a health advice unit that analyzes the user's health information and notifies them of special offers based on their health condition. For example, the generation AI can provide information on special offers on low-calorie foods and vitamin-rich foods based on the user's health data. The generation AI can also analyze the user's allergy information and notify them of special offers that do not contain allergens. Furthermore, the generation AI can analyze the user's exercise habits and provide information on special offers on sports equipment and health supplements. This can support the user in maintaining their health by notifying them of special offers based on their health condition.

[0082] The bargain notification system can also include a hobby advice unit that analyzes the user's hobbies and interests and notifies them of special offers based on their hobbies and interests. For example, the generation AI can provide information on special offers on gardening supplies and DIY supplies based on the user's hobby data. The generation AI can also analyze the user's music preferences and notify them of special offers on musical instruments and audio equipment. Furthermore, the generation AI can analyze the user's travel history and provide information on special offers on travel goods and outdoor equipment. This can enrich the user's lifestyle by notifying them of special offers based on their hobbies and interests.

[0083] The bargain notification system can also include a family advice unit that analyzes the user's family composition and notifies the user of special sales items that the whole family can enjoy. For example, the generation AI can provide information on special sales items for children's toys and educational supplies based on the user's family composition data. The generation AI can also provide information on special sales items for health foods and nursing care products for the elderly. The generation AI can also provide information on special sales items for leisure and party supplies that the whole family can enjoy. This makes it possible to provide products that the whole family can enjoy by notifying the user of special sales items that suit the user's family composition.

[0084] The bargain notification system can also include a living environment advice unit that analyzes the user's living environment and notifies them of special sales items appropriate for that living environment. For example, the generation AI can provide users living in urban areas with sale information on compact furniture and storage items based on the user's living environment data. The generation AI can also notify users living in cold regions of sale information on heating appliances and cold weather gear. Furthermore, the generation AI can provide users living by the sea with sale information on beach and outdoor gear. This allows the system to provide products that suit the user's living environment by notifying them of sale items appropriate for their living environment.

[0085] The bargain notification system can also be equipped with a consumption advice unit that analyzes the user's consumption patterns and notifies them of sale information according to the timing of consumption. For example, the generation AI can provide sale information for frequently purchased household goods based on the user's past consumption patterns. The generation AI can also notify them of sale information for household goods related to specific seasons or events. Furthermore, the generation AI can provide sale information tailored to the user's lifestyle. In this way, by notifying them of sale information based on their consumption patterns, they can purchase the products they need at the appropriate time.

[0086] The bargain notification system can also include an emotion advice unit that estimates the user's emotions and optimizes notifications of sale items based on the estimated user emotions. For example, the generation AI analyzes the user's facial expressions and voice when browsing sale items and calculates an emotion score. For example, if it detects a smile or an excited voice, it will prioritize notifications of those sale items. The generation AI can also identify sale items that evoke positive emotions in the user and prioritize displaying similar sale items. Furthermore, the generation AI can monitor the user's emotional reactions in real time and dynamically select sale items that elicit positive emotions. This can increase the user's desire to purchase by selecting sale items based on the user's emotions.

[0087] The bargain notification system can also include an emotional recipe unit that estimates the user's emotions and optimizes recipe suggestions based on the estimated user emotions. For example, the generation AI can analyze the user's facial expressions and voice when browsing recipes and calculate an emotion score. For example, if it detects a smile or an excited voice, it can prioritize suggesting those recipes. The generation AI can also identify recipes that evoke positive emotions in the user and prioritize displaying similar recipes. Furthermore, the generation AI can monitor the user's emotional responses in real time and dynamically select recipes that elicit positive emotions. This can improve the user's cooking experience by selecting recipes based on the user's emotions.

[0088] The bargain notification system can also include an emotion point unit that estimates the user's emotions and optimizes the point usage method suggestions based on the estimated user emotions. For example, the generation AI analyzes the user's facial expressions and voice when browsing point usage methods and calculates an emotion score. For example, if it detects a smile or an excited voice, it will prioritize suggesting those point usage methods. The generation AI can also identify point usage methods that evoke positive emotions in the user and prioritize displaying similar methods. Furthermore, the generation AI can monitor the user's emotional reactions in real time and dynamically select point usage methods that elicit positive emotions. This allows the user's point usage to be maximized by selecting point usage methods based on the user's emotions.

[0089] The bargain notification system can also include an emotional lifestyle product section that estimates the user's emotions and optimizes sale information for daily necessities based on the estimated user emotions. For example, the generation AI can analyze the user's facial expressions and voice when browsing sale information for daily necessities and calculate an emotion score. For example, if it detects a smile or an excited voice, it can prioritize notifications of sale information for that item. The generation AI can also identify daily necessities that evoke positive emotions in the user and prioritize displaying similar sale information. Furthermore, the generation AI can monitor the user's emotional reactions in real time and dynamically select sale information that elicits positive emotions. This can increase the user's desire to purchase by selecting sale information based on the user's emotions.

[0090] The bargain notification system can also include an emotional advertising unit that estimates a user's emotions and optimizes the display of advertisements based on the estimated user emotions. For example, the generation AI analyzes the user's facial expressions and voice when viewing an advertisement and calculates an emotion score. For example, if it detects a smile or an excited voice, it will prioritize displaying those advertisements. The generation AI can also identify advertisements that evoke positive emotions in the user and prioritize displaying similar advertisements. Furthermore, the generation AI can monitor the user's emotional responses in real time and dynamically select advertisements that elicit positive emotions. This allows the selection of advertisements based on the user's emotions to increase the user's purchasing motivation.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The advertising learning unit learns advertisements. For example, the generation AI analyzes local advertisements registered in LINE (registered trademark) flyers and collects information on special sales and discounts. The generation AI can also use machine learning algorithms to learn from advertising data and provide optimal advertising information to users. Furthermore, the generation AI can analyze local event information and weather forecasts to identify related sales items. Step 2: The notification unit notifies the user of bargains based on the advertising information learned by the advertising learning unit. For example, the generation AI notifies the user of sale information at supermarkets and drugstores in the user's area. The generation AI can also learn the user's purchasing history and notify the user of bargains optimized for each individual user. Furthermore, the generation AI can estimate the user's emotions and prioritize advertisements that evoke positive emotions. Step 3: The recipe generation unit generates recipes using the sale items learned by the advertising learning unit. For example, if the sale items include chicken and vegetables, the generation AI will suggest a simple recipe using them. The generation AI can also learn the user's past cooking history and suggest recipes optimized for each individual user. Furthermore, the generation AI can analyze seasonal ingredient information and suggest recipes using sale items according to the season. Step 4: The household goods notification unit notifies the user of sale information for household goods registered by the user. For example, if the user registers toilet paper or detergent, the generation AI will collect sale information for those items and notify the user. The generation AI can also learn the user's consumption patterns and notify the user of sale information according to the timing of consumption. Furthermore, the generation AI can analyze the user's lifestyle and living environment and notify the user of sale information accordingly. Step 5: The point advice unit provides advice on how to effectively use store points. For example, the generation AI suggests days when points are most likely to be accumulated and ways to use points to get great deals on shopping. The generation AI can also learn the user's point usage history and suggest optimal ways to use points. Furthermore, the generation AI can estimate the user's emotions and prioritize suggesting ways to use points that will elicit positive emotions.

[0093] 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.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0121] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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]

[0160] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an advertisement learning unit that learns advertisements; a notification unit that notifies of bargains based on the advertising information learned by the advertising learning unit; a recipe generation unit that generates recipes using the sale items learned by the advertisement learning unit; a daily necessities notification unit that notifies the user of sale information for daily necessities registered by the user; A point advice unit that gives advice on how to effectively use store points. A system characterized by:

2. The advertisement learning unit Learn the user's purchasing history and notify them of deals optimized for each individual user 2. The system of claim 1.

3. The advertisement learning unit Analyze local event information and notify you of special sales related to the event 2. The system of claim 1.

4. The advertisement learning unit Analyzing how the user feels about a particular advertisement and notifying the user preferentially of advertisements that evoke positive emotions 2. The system of claim 1.

5. The advertisement learning unit Analyzes local weather forecasts and notifies you of weather-related deals 2. The system of claim 1.

6. The advertisement learning unit Analyze local traffic information and notify customers of special offers at easily accessible stores 2. The system of claim 1.

7. The advertisement learning unit The emotions of the user when viewing a sale item are analyzed in real time, and sale items that elicit positive emotions are given priority.

2. The system of claim 1.

8. The recipe generation unit It learns the user's past cooking history and suggests recipes optimized for each individual user.

2. The system of claim 1.

Images