System

Abstract

An object of a system according to an embodiment is to allow a user to efficiently find an optimal supplier.SOLUTION: A system includes a leaflet reading part, a food material list registration part, a purchase destination proposal part, and a display part. The leaflet reading unit reads a leaflet of a store. The food ingredient list registration unit receives a food ingredient list registered by a user. A purchase destination proposal part collates the leaflet of the store read by the leaflet reading part with the food material list to be purchased received by the food material list registration part, and proposes an optimum purchase destination. The display unit displays the optimum supplier proposed by the supplier proposal unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that users have to spend a lot of time and effort to find the best place to buy, making it difficult to shop efficiently.

[0005] The system according to the embodiment aims to enable a user to efficiently find the most suitable supplier. [Means for solving the problem]

[0006] The system according to the embodiment includes a flyer reader, an ingredient list registration unit, a purchasing source suggestion unit, and a display unit. The flyer reader reads store flyers. The ingredient list registration unit accepts a desired ingredient list registered by the user. The purchasing source suggestion unit compares the store flyer read by the flyer reader with the desired ingredient list accepted by the ingredient list registration unit to suggest the most suitable purchasing source. The display unit displays the most suitable purchasing source suggested by the purchasing source suggestion unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to efficiently find the most suitable supplier. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The recommendation system according to an embodiment of the present invention uses a generation AI to read flyers from registered stores and suggests what to buy at which supermarket based on the list of ingredients the user has registered. This allows the recommendation system to suggest the best place to buy ingredients based on the list of ingredients the user wants to buy.

[0029] The recommendation system according to the embodiment includes a flyer reader, an ingredient list registration unit, a purchasing suggestion unit, and a display unit. The flyer reader reads store flyers. For example, the flyer's image data or PDF file is read, and the generation AI analyzes its contents. The flyer reader can also extract product names, prices, sale information, and other information. For example, the generation AI can extract text information from the flyer using OCR technology and store it in a database. The ingredient list registration unit accepts a user's desired ingredient list. For example, the user can input a list in text format, such as "tomatoes, milk, eggs, and bread." The ingredient list registration unit can also accept voice and image input. The purchasing suggestion unit compares the store flyer read by the flyer reader with the desired ingredient list accepted by the ingredient list registration unit to suggest optimal purchasing locations. For example, the generation AI calculates and suggests the store where the user can purchase ingredients at the lowest price based on the product prices and sale information at each store. The display unit displays the optimal purchasing locations suggested by the purchasing suggestion unit to the user. For example, a smartphone app or website may display a message such as, "The best deal is to buy tomatoes and milk at supermarket A, and eggs and bread at supermarket B." This allows the recommendation system according to the embodiment to suggest the best place to buy ingredients based on the list of ingredients that the user wants to buy.

[0030] The flyer reading unit can analyze product placement and design patterns from flyer image data and infer a store's marketing strategy. In the flyer reading unit, for example, a generation AI analyzes flyer image data and extracts product placement and design patterns. For example, if a specific product is placed in a prominent position, it is inferred that the product is the store's recommended product. The flyer design pattern is also analyzed to infer the store's marketing strategy. For example, if a specific color or font is frequently used, the impact of that design on users is evaluated. The generation AI also analyzes flyer image data and infers the store's marketing strategy based on product placement and design patterns. For example, if a specific product is featured across multiple pages, it is determined that the product is the store's main product. This makes it possible to infer the store's marketing strategy.

[0031] The flyer reading unit can predict the product inventory status and expected arrival date based on the analysis results of the flyer and notify the user. In the flyer reading unit, for example, the generation AI predicts the product inventory status based on the analysis results of the flyer. For example, if a sale item is listed, it will estimate that the product is low in stock and notify the user. The generation AI also predicts the expected arrival date of the product based on the analysis results of the flyer. For example, if a new product is listed, it will estimate the expected arrival date of the product and notify the user. The generation AI also predicts the product inventory status and expected arrival date based on the analysis results of the flyer and notify the user. For example, if a particular product is frequently listed, it will determine that the product's inventory is stable. This makes it possible to predict the product inventory status and expected arrival date and notify the user.

[0032] The flyer reading unit can integrate the analysis results of the flyer with data from other digital advertisements and online marketplaces to perform comprehensive marketing analysis. In the flyer reading unit, for example, the generation AI integrates the analysis results of the flyer with data from other digital advertisements to perform comprehensive marketing analysis. For example, it compares the click-through rate of online advertisements with the sale information in the flyer. It also integrates the analysis results of the flyer with data from online marketplaces to perform comprehensive marketing analysis. For example, it compares online sales data with the sale information in the flyer. It also integrates the analysis results of the flyer with data from other digital advertisements and online marketplaces to perform comprehensive marketing analysis. For example, it integrates and evaluates the effectiveness of the digital advertisement with the sale information in the flyer. This allows the analysis results of the flyer to be integrated with data from other digital advertisements and online marketplaces to perform comprehensive marketing analysis.

[0033] The flyer reading unit can add a function to read out the contents of the flyer aloud, making it suitable for visually impaired people. The flyer reading unit can add a function for the generation AI to read out the contents of the flyer aloud, making it suitable for visually impaired people. For example, it can provide sale information and product details aloud. In addition, a function to read out the contents of the flyer aloud can be added, making it easier for visually impaired people to obtain information. For example, it can read out the product name and price aloud. In addition, a function for the generation AI to read out the contents of the flyer aloud can be added, making it suitable for visually impaired people. For example, it can analyze the image data of the flyer and provide information aloud. This makes it suitable for visually impaired people.

[0034] The ingredient list registration unit allows the generation AI to suggest recipes based on the ingredient list entered by the user and automatically add the necessary ingredients to the list. The ingredient list registration unit allows the generation AI to suggest recipes based on the ingredient list entered by the user, for example. For example, if "tomatoes, milk, eggs, bread" is entered, a recipe using these ingredients is suggested and the necessary ingredients are added to the list. The generation AI also analyzes the user's ingredient list and suggests recipes. For example, if "tomatoes, milk, eggs, bread" is entered, a recipe using these ingredients is suggested and the additional ingredients are added to the list. The generation AI also suggests recipes based on the ingredient list entered by the user and automatically adds the necessary ingredients to the list. For example, if "tomatoes, milk, eggs, bread" is entered, a recipe using these ingredients is suggested and the additional ingredients are added to the list. This allows recipes to be suggested based on the ingredient list entered by the user and the necessary ingredients to be automatically added to the list.

[0035] The ingredient list registration unit can analyze the user's past purchase history and automatically add frequently purchased ingredients to the list. In the ingredient list registration unit, for example, the generation AI analyzes the user's past purchase history and automatically adds frequently purchased ingredients to the list. For example, ingredients that the user purchases weekly are automatically added to the list. In addition, frequently purchased ingredients are automatically added to the list based on the user's past purchase history. For example, ingredients that the user purchases regularly are added to the list. In addition, the generation AI analyzes the user's past purchase history and automatically adds frequently purchased ingredients to the list. For example, ingredients that the user often purchases are automatically added to the list. In this way, the user's past purchase history can be analyzed and frequently purchased ingredients can be automatically added to the list.

[0036] The ingredient list registration unit can use voice recognition technology to enable the user to create a list just by speaking. For example, the generation AI can use voice recognition technology to enable the user to create an ingredient list just by speaking. For example, when the user speaks "tomato, milk, egg, bread," it is added to the list. Also, voice recognition technology can be used to enable the user to create an ingredient list just by speaking. For example, when the user speaks the name of an ingredient, it is automatically added to the list. Also, the generation AI can use voice recognition technology to enable the user to create an ingredient list just by speaking. For example, when the user speaks "tomato, milk, egg, bread," it is added to the list. This makes it possible for the user to create a list just by speaking.

[0037] The ingredient list registration unit can add a function to share an ingredient list with other users and do joint shopping. The ingredient list registration unit, for example, adds a function to allow the generation AI to share an ingredient list with other users and do joint shopping. For example, a list can be shared with family and friends and the shopping responsibilities can be divided. A function to share an ingredient list with other users and do joint shopping can also be added. For example, multiple users can share the same list and each user can purchase the ingredients they need. A function to share an ingredient list with other users and do joint shopping can also be added. For example, a list can be shared with family and friends and the shopping responsibilities can be divided. This allows a function to share an ingredient list with other users and do joint shopping.

[0038] The shopping suggestion unit can propose the most efficient shopping route by taking into account the user's travel route and means of transportation. In the shopping suggestion unit, for example, the generation AI proposes the most efficient shopping route based on the user's current location and destination. For example, it proposes supermarkets that the user can stop by on the way from home to work. It also proposes the most efficient shopping route by taking into account the user's means of transportation (car, bicycle, walking, etc.). For example, it proposes supermarkets with parking for users who use cars. The generation AI also proposes the most efficient shopping route by taking into account the user's travel route and means of transportation. For example, if the user uses public transportation, it proposes supermarkets near the station. This makes it possible to propose the most efficient shopping route by taking into account the user's travel route and means of transportation.

[0039] The purchase suggestion unit can analyze the reviews and ratings of each store and prioritize suggest stores that match the user's preferences. In the purchase suggestion unit, for example, the generation AI analyzes the reviews and ratings of each store and prioritizes suggesting stores that match the user's preferences. For example, if the user prefers highly rated stores, the unit will prioritize suggesting those stores. In addition, stores that match the user's preferences are suggested based on the reviews and ratings of each store. For example, if the user places importance on the quality of a particular product, stores that have a high rating for that product are suggested. In addition, the generation AI analyzes the reviews and ratings of each store and prioritizes suggesting stores that match the user's preferences. For example, if the user places importance on a particular service (e.g., parking, product selection), the unit will suggest stores that have a high rating for that service. In this way, the generation AI can analyze the reviews and ratings of each store and prioritize suggesting stores that match the user's preferences.

[0040] The purchase suggestion unit can automatically collect sale information and coupons for the suggested stores and provide them to the user. In the purchase suggestion unit, for example, the generation AI automatically collects sale information and coupons for the suggested stores and provides them to the user. For example, it notifies the user of the latest sale information for stores that the user plans to visit. It also automatically collects coupon information for the suggested stores and provides it to the user. For example, it displays a list of coupons that the user can use. It also automatically collects sale information and coupons for the suggested stores and provides it to the user. For example, it updates sale information for stores that the user plans to visit in real time. This allows sale information and coupons for the suggested stores to be automatically collected and provided to the user.

[0041] The purchase suggestion unit can analyze the shopping data of other users and suggest popular stores and products. In the purchase suggestion unit, for example, the generation AI analyzes the shopping data of other users and suggests popular stores and products. For example, it suggests stores where a specific product is purchased by many users. Also, popular stores and products are suggested based on the shopping data of other users. For example, it suggests stores where a specific product is highly rated. Also, the generation AI analyzes the shopping data of other users and suggests popular stores and products. For example, it suggests stores where a specific product is purchased by many users. In this way, it is possible to analyze the shopping data of other users and suggest popular stores and products.

[0042] The display unit can use interactive maps and graphs to display the proposal results in a visually easy-to-understand manner. The display unit, for example, uses an interactive map so that the generation AI can display the proposal results in a visually easy-to-understand manner. For example, detailed information is displayed when the user clicks. Also, an interactive graph is used to display the proposal results in a visually easy-to-understand manner. For example, a price comparison of each store is displayed in a graph. Also, an interactive map or graph is used so that the generation AI can display the proposal results in a visually easy-to-understand manner. For example, the user can check the location of the store on a map. This allows the use of interactive maps and graphs to display the proposal results in a visually easy-to-understand manner.

[0043] The display unit can add a function that allows the user to edit the shopping list based on the suggestion results. The display unit, for example, adds a function that allows the user to edit the shopping list based on the suggestion results from the generation AI. For example, the user deletes suggested stores or products from the list. Also, a function that allows the user to edit the shopping list based on the suggestion results is added. For example, the user adds a new product to the list. Also, a function that allows the user to edit the shopping list based on the suggestion results from the generation AI. For example, the user deletes suggested stores or products from the list. This makes it possible to add a function that allows the user to edit the shopping list based on the suggestion results.

[0044] The display unit can also display the suggestion results on other devices (smartwatches and smart speakers) so that the user can check them anywhere. For example, the display unit can cause the generation AI to display the suggestion results on a smartwatch so that the user can check them anywhere. For example, the user can check the suggestion results even when they are out and about. The suggestion results can also be displayed on a smart speaker so that the user can check them by voice. For example, the user can check the suggestion results using a voice command. The generation AI can also display the suggestion results on other devices (smartwatches and smart speakers) so that the user can check them anywhere. For example, the user can check the suggestion results even when they are out and about. This allows the suggestion results to also be displayed on other devices so that the user can check them anywhere.

[0045] The display unit adds a function that allows the suggestion results to be shared on social media, making it possible to share information with other users. The display unit, for example, adds a function that allows the generation AI to share the suggestion results on social media, making it possible to share information with other users. For example, a user shares a suggested store or product on social media. The display unit also adds a function that allows the suggestion results to be shared on social media, making it possible to share information with other users. For example, a user shares a suggested store or product with a friend. The display unit also adds a function that allows the generation AI to share the suggestion results on social media, making it possible to share information with other users. For example, a user shares a suggested store or product on social media. This adds a function that allows the suggestion results to be shared on social media, making it possible to share information with other users.

[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 recommendation system can also be equipped with a food ingredient suggestion function that takes into account the user's health condition. For example, if the user connects to a health management app and inputs their current health condition and allergy information, the recommendation system can suggest appropriate food ingredients based on that information. Also, if the user wants to consume a specific nutrient, the system can prioritize suggesting food ingredients that contain a large amount of that nutrient. Furthermore, it is possible to suggest food ingredients based on the user's health goals (e.g., weight loss, muscle building).

[0048] The recommendation system can also suggest seasonal food recommendations based on the user's past purchase history. For example, it can analyze past data to identify ingredients that the user prefers to buy in the summer and suggest those ingredients for the summer. It can also take into account seasonal sales information to suggest the best places to buy them for the user. It can also suggest seasonal recipes and provide ideas for users to enjoy seasonal ingredients.

[0049] The recommendation system can also suggest environmentally friendly shopping sources based on the user's grocery list, for example prioritizing locally sourced ingredients to reduce the environmental impact of transportation. It can also suggest eco-friendly stores and stores that sell organic foods. It can also provide information (e.g., carbon footprint) to help users make environmentally conscious choices.

[0050] The system can also suggest recipes based on the user's ingredient list, taking into account the cooking difficulty and cooking time. For example, it can suggest recipes that can be cooked quickly on busy days and more elaborate recipes on days when the user has more time. It can also allow the user to select the cooking difficulty level, providing easy recipes for beginners and more challenging recipes for advanced users. It can also provide advice and tips based on the user's cooking skill.

[0051] The recommendation system can also have a function to notify the user of food storage methods and expiration dates based on the user's food list. For example, it can suggest storage methods for purchased ingredients and notify the user of appropriate storage methods. It can also manage expiration dates of ingredients and notify the user of ingredients that are approaching their expiration date. It can also suggest recipes using ingredients that are close to their expiration date, reducing food waste.

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

[0053] Step 1: The flyer reader reads the store's flyer. For example, the flyer's image data or PDF file is read, and the generation AI analyzes its contents. The flyer reader can also extract product names, prices, sale information, etc. The generation AI uses OCR technology to extract text information from the flyer and saves it in a database. Step 2: The ingredient list registration unit accepts the ingredient list registered by the user. For example, the user can enter a list in text format such as "tomatoes, milk, eggs, bread." The ingredient list registration unit can also accept voice and image input. Step 3: The purchasing suggestion unit compares the store flyers read by the flyer reading unit with the desired food list accepted by the food list registration unit to suggest the most suitable purchasing locations. Based on the product prices and sale information of each store, the generation AI calculates and suggests the store where the user can purchase the items at the lowest price. Step 4: The display unit displays the optimal shopping locations suggested by the shopping location suggestion unit to the user. For example, through a smartphone app or website, it displays the message, "The best deal is to buy tomatoes and milk at supermarket A, and eggs and bread at supermarket B."

[0054] (Example 2) The recommendation system according to an embodiment of the present invention uses a generation AI to read flyers from registered stores and suggests what to buy at which supermarket based on the list of ingredients the user has registered. This allows the recommendation system to suggest the best place to buy ingredients based on the list of ingredients the user wants to buy.

[0055] The recommendation system according to the embodiment includes a flyer reader, an ingredient list registration unit, a purchasing suggestion unit, and a display unit. The flyer reader reads store flyers. For example, the flyer's image data or PDF file is read, and the generation AI analyzes its contents. The flyer reader can also extract product names, prices, sale information, and other information. For example, the generation AI can extract text information from the flyer using OCR technology and store it in a database. The ingredient list registration unit accepts a user's desired ingredient list. For example, the user can input a list in text format, such as "tomatoes, milk, eggs, and bread." The ingredient list registration unit can also accept voice and image input. The purchasing suggestion unit compares the store flyer read by the flyer reader with the desired ingredient list accepted by the ingredient list registration unit to suggest optimal purchasing locations. For example, the generation AI calculates and suggests the store where the user can purchase ingredients at the lowest price based on the product prices and sale information at each store. The display unit displays the optimal purchasing locations suggested by the purchasing suggestion unit to the user. For example, a smartphone app or website may display a message such as, "The best deal is to buy tomatoes and milk at supermarket A, and eggs and bread at supermarket B." This allows the recommendation system according to the embodiment to suggest the best place to buy ingredients based on the list of ingredients that the user wants to buy.

[0056] The flyer reading unit can analyze product placement and design patterns from flyer image data and infer a store's marketing strategy. In the flyer reading unit, for example, a generation AI analyzes flyer image data and extracts product placement and design patterns. For example, if a specific product is placed in a prominent position, it is inferred that the product is the store's recommended product. The flyer design pattern is also analyzed to infer the store's marketing strategy. For example, if a specific color or font is frequently used, the impact of that design on users is evaluated. The generation AI also analyzes flyer image data and infers the store's marketing strategy based on product placement and design patterns. For example, if a specific product is featured across multiple pages, it is determined that the product is the store's main product. This makes it possible to infer the store's marketing strategy.

[0057] The flyer reading unit can predict the product inventory status and expected arrival date based on the analysis results of the flyer and notify the user. In the flyer reading unit, for example, the generation AI predicts the product inventory status based on the analysis results of the flyer. For example, if a sale item is listed, it will estimate that the product is low in stock and notify the user. The generation AI also predicts the expected arrival date of the product based on the analysis results of the flyer. For example, if a new product is listed, it will estimate the expected arrival date of the product and notify the user. The generation AI also predicts the product inventory status and expected arrival date based on the analysis results of the flyer and notify the user. For example, if a particular product is frequently listed, it will determine that the product's inventory is stable. This makes it possible to predict the product inventory status and expected arrival date and notify the user.

[0058] The flyer reading unit uses the emotion estimation function to analyze the emotional impact that flyer designs and catch phrases have on users, and can identify the most effective flyers. In the flyer reading unit, for example, the generation AI analyzes the flyer design and catch phrases and evaluates the emotional impact on users. For example, it identifies designs and catch phrases that evoke positive emotions. The emotion estimation function is also used to analyze the emotional impact that flyer designs and catch phrases have on users. For example, it evaluates the emotional impact that specific colors and fonts have on users. The generation AI also uses the emotion estimation function to analyze the emotional impact that flyer designs and catch phrases have on users, and identify the most effective flyers. For example, it selects the flyer that evokes the most positive emotions based on the user's emotional response. This allows the emotional impact that flyer designs and catch phrases have on users to be analyzed, and the most effective flyers to be identified.

[0059] The flyer reading unit can integrate the analysis results of the flyer with data from other digital advertisements and online marketplaces to perform comprehensive marketing analysis. In the flyer reading unit, for example, the generation AI integrates the analysis results of the flyer with data from other digital advertisements to perform comprehensive marketing analysis. For example, it compares the click-through rate of online advertisements with the sale information in the flyer. It also integrates the analysis results of the flyer with data from online marketplaces to perform comprehensive marketing analysis. For example, it compares online sales data with the sale information in the flyer. It also integrates the analysis results of the flyer with data from other digital advertisements and online marketplaces to perform comprehensive marketing analysis. For example, it integrates and evaluates the effectiveness of the digital advertisement with the sale information in the flyer. This allows the analysis results of the flyer to be integrated with data from other digital advertisements and online marketplaces to perform comprehensive marketing analysis.

[0060] The flyer reading unit can add a function to read out the contents of the flyer aloud, making it suitable for visually impaired people. The flyer reading unit can add a function for the generation AI to read out the contents of the flyer aloud, making it suitable for visually impaired people. For example, it can provide sale information and product details aloud. In addition, a function to read out the contents of the flyer aloud can be added, making it easier for visually impaired people to obtain information. For example, it can read out the product name and price aloud. In addition, a function for the generation AI to read out the contents of the flyer aloud can be added, making it suitable for visually impaired people. For example, it can analyze the image data of the flyer and provide information aloud. This makes it suitable for visually impaired people.

[0061] The flyer reading unit uses an emotion estimation function to monitor the emotions of users when they view flyers in real time, and can display optimal advertisements. In the flyer reading unit, for example, the generation AI uses the emotion estimation function to monitor the emotions of users when they view flyers in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. It also uses the emotion estimation function to monitor the emotions of users when they view flyers in real time, and display optimal advertisements. For example, it prioritizes displaying advertisements that evoke positive emotions. It also uses the emotion estimation function to monitor the emotions of users when they view flyers in real time, and display optimal advertisements. For example, it dynamically adjusts advertisements based on the user's emotional response. This allows it to monitor the emotions of users when they view flyers in real time, and display optimal advertisements.

[0062] The ingredient list registration unit allows the generation AI to suggest recipes based on the ingredient list entered by the user and automatically add the necessary ingredients to the list. The ingredient list registration unit allows the generation AI to suggest recipes based on the ingredient list entered by the user, for example. For example, if "tomatoes, milk, eggs, bread" is entered, a recipe using these ingredients is suggested and the necessary ingredients are added to the list. The generation AI also analyzes the user's ingredient list and suggests recipes. For example, if "tomatoes, milk, eggs, bread" is entered, a recipe using these ingredients is suggested and the additional ingredients are added to the list. The generation AI also suggests recipes based on the ingredient list entered by the user and automatically adds the necessary ingredients to the list. For example, if "tomatoes, milk, eggs, bread" is entered, a recipe using these ingredients is suggested and the additional ingredients are added to the list. This allows recipes to be suggested based on the ingredient list entered by the user and the necessary ingredients to be automatically added to the list.

[0063] The ingredient list registration unit can analyze the user's past purchase history and automatically add frequently purchased ingredients to the list. In the ingredient list registration unit, for example, the generation AI analyzes the user's past purchase history and automatically adds frequently purchased ingredients to the list. For example, ingredients that the user purchases weekly are automatically added to the list. In addition, frequently purchased ingredients are automatically added to the list based on the user's past purchase history. For example, ingredients that the user purchases regularly are added to the list. In addition, the generation AI analyzes the user's past purchase history and automatically adds frequently purchased ingredients to the list. For example, ingredients that the user often purchases are automatically added to the list. In this way, the user's past purchase history can be analyzed and frequently purchased ingredients can be automatically added to the list.

[0064] The ingredient list registration unit can use the emotion estimation function to analyze the emotion a user has when entering an ingredient list and provide an interface for reducing stress. In the ingredient list registration unit, for example, the generation AI uses the emotion estimation function to analyze the emotion a user has when entering an ingredient list. For example, it analyzes the user's facial expressions and voice and provides an interface for reducing stress. The emotion estimation function can also be used to analyze the emotion a user has when entering an ingredient list and provide an interface for reducing stress. For example, it can provide a design or audio guide that helps the user relax. The generation AI can also use the emotion estimation function to analyze the emotion a user has when entering an ingredient list and provide an interface for reducing stress. For example, it can adjust the color or layout of the interface depending on the user's emotion. In this way, it can analyze the emotion a user has when entering an ingredient list and provide an interface for reducing stress.

[0065] The ingredient list registration unit can use voice recognition technology to enable the user to create a list just by speaking. For example, the generation AI can use voice recognition technology to enable the user to create an ingredient list just by speaking. For example, when the user speaks "tomato, milk, egg, bread," it is added to the list. Also, voice recognition technology can be used to enable the user to create an ingredient list just by speaking. For example, when the user speaks the name of an ingredient, it is automatically added to the list. Also, the generation AI can use voice recognition technology to enable the user to create an ingredient list just by speaking. For example, when the user speaks "tomato, milk, egg, bread," it is added to the list. This makes it possible for the user to create a list just by speaking.

[0066] The ingredient list registration unit can add a function to share an ingredient list with other users and do joint shopping. The ingredient list registration unit, for example, adds a function to allow the generation AI to share an ingredient list with other users and do joint shopping. For example, a list can be shared with family and friends and the shopping responsibilities can be divided. A function to share an ingredient list with other users and do joint shopping can also be added. For example, multiple users can share the same list and each user can purchase the ingredients they need. A function to share an ingredient list with other users and do joint shopping can also be added. For example, a list can be shared with family and friends and the shopping responsibilities can be divided. This allows a function to share an ingredient list with other users and do joint shopping.

[0067] The ingredient list registration unit uses the emotion estimation function to analyze the emotion toward ingredients entered by the user and can suggest ingredients that elicit positive emotions. In the ingredient list registration unit, for example, the generation AI uses the emotion estimation function to analyze the emotion toward ingredients entered by the user. For example, it identifies ingredients for which the user has positive emotions and suggests them. The emotion estimation function is also used to analyze the emotion toward ingredients entered by the user and suggest ingredients that elicit positive emotions. For example, it adds ingredients that the user likes to the list. The generation AI also uses the emotion estimation function to analyze the emotion toward ingredients entered by the user and suggest ingredients that elicit positive emotions. For example, it adds ingredients that the user likes to the list. In this way, it is possible to analyze the emotion toward ingredients entered by the user and suggest ingredients that elicit positive emotions.

[0068] The shopping suggestion unit can propose the most efficient shopping route by taking into account the user's travel route and means of transportation. In the shopping suggestion unit, for example, the generation AI proposes the most efficient shopping route based on the user's current location and destination. For example, it proposes supermarkets that the user can stop by on the way from home to work. It also proposes the most efficient shopping route by taking into account the user's means of transportation (car, bicycle, walking, etc.). For example, it proposes supermarkets with parking for users who use cars. The generation AI also proposes the most efficient shopping route by taking into account the user's travel route and means of transportation. For example, if the user uses public transportation, it proposes supermarkets near the station. This makes it possible to propose the most efficient shopping route by taking into account the user's travel route and means of transportation.

[0069] The purchase suggestion unit can analyze the reviews and ratings of each store and prioritize suggest stores that match the user's preferences. In the purchase suggestion unit, for example, the generation AI analyzes the reviews and ratings of each store and prioritizes suggesting stores that match the user's preferences. For example, if the user prefers highly rated stores, the unit will prioritize suggesting those stores. In addition, stores that match the user's preferences are suggested based on the reviews and ratings of each store. For example, if the user places importance on the quality of a particular product, stores that have a high rating for that product are suggested. In addition, the generation AI analyzes the reviews and ratings of each store and prioritizes suggesting stores that match the user's preferences. For example, if the user places importance on a particular service (e.g., parking, product selection), the unit will suggest stores that have a high rating for that service. In this way, the generation AI can analyze the reviews and ratings of each store and prioritize suggesting stores that match the user's preferences.

[0070] The purchase suggestion unit uses the emotion estimation function to analyze the emotions the user has toward the proposed store and can suggest the store that will provide the highest level of satisfaction. In the purchase suggestion unit, for example, the generation AI uses the emotion estimation function to analyze the emotions the user has toward the proposed store. For example, it analyzes the user's facial expressions and voice and suggests stores that evoke positive emotions. It also uses the emotion estimation function to analyze the emotions the user has toward the proposed store and suggests the store that will provide the highest level of satisfaction. For example, it makes suggestions based on emotional data of stores that the user has visited in the past. It also uses the emotion estimation function to analyze the emotions the user has toward the proposed store and suggests the store that will provide the highest level of satisfaction. For example, it dynamically adjusts the store based on the user's emotional response. In this way, it is possible to analyze the emotions the user has toward the proposed store and suggest the store that will provide the highest level of satisfaction.

[0071] The purchase suggestion unit can automatically collect sale information and coupons for the suggested stores and provide them to the user. In the purchase suggestion unit, for example, the generation AI automatically collects sale information and coupons for the suggested stores and provides them to the user. For example, it notifies the user of the latest sale information for stores that the user plans to visit. It also automatically collects coupon information for the suggested stores and provides it to the user. For example, it displays a list of coupons that the user can use. It also automatically collects sale information and coupons for the suggested stores and provides it to the user. For example, it updates sale information for stores that the user plans to visit in real time. This allows sale information and coupons for the suggested stores to be automatically collected and provided to the user.

[0072] The purchase suggestion unit can analyze the shopping data of other users and suggest popular stores and products. In the purchase suggestion unit, for example, the generation AI analyzes the shopping data of other users and suggests popular stores and products. For example, it suggests stores where a specific product is purchased by many users. Also, popular stores and products are suggested based on the shopping data of other users. For example, it suggests stores where a specific product is highly rated. Also, the generation AI analyzes the shopping data of other users and suggests popular stores and products. For example, it suggests stores where a specific product is purchased by many users. In this way, it is possible to analyze the shopping data of other users and suggest popular stores and products.

[0073] The shopping destination suggestion unit uses an emotion estimation function to monitor the user's emotions toward the suggested shopping destinations in real time and continuously make optimal suggestions. In the shopping destination suggestion unit, for example, the generation AI uses the emotion estimation function to monitor the user's emotions toward the suggested shopping destinations in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The emotion estimation function is also used to monitor the user's emotions toward the suggested shopping destinations in real time and continuously make optimal suggestions. For example, it prioritizes suggesting stores that evoke positive emotions. The generation AI also uses the emotion estimation function to monitor the user's emotions toward the suggested shopping destinations in real time and continuously make optimal suggestions. For example, it dynamically adjusts stores based on the user's emotional response. This allows the user's emotions toward the suggested shopping destinations to be monitored in real time and continuously make optimal suggestions.

[0074] The display unit can use interactive maps and graphs to display the proposal results in a visually easy-to-understand manner. The display unit, for example, uses an interactive map so that the generation AI can display the proposal results in a visually easy-to-understand manner. For example, detailed information is displayed when the user clicks. Also, an interactive graph is used to display the proposal results in a visually easy-to-understand manner. For example, a price comparison of each store is displayed in a graph. Also, an interactive map or graph is used so that the generation AI can display the proposal results in a visually easy-to-understand manner. For example, the user can check the location of the store on a map. This allows the use of interactive maps and graphs to display the proposal results in a visually easy-to-understand manner.

[0075] The display unit can add a function that allows the user to edit the shopping list based on the suggestion results. The display unit, for example, adds a function that allows the user to edit the shopping list based on the suggestion results from the generation AI. For example, the user deletes suggested stores or products from the list. Also, a function that allows the user to edit the shopping list based on the suggestion results is added. For example, the user adds a new product to the list. Also, a function that allows the user to edit the shopping list based on the suggestion results from the generation AI. For example, the user deletes suggested stores or products from the list. This makes it possible to add a function that allows the user to edit the shopping list based on the suggestion results.

[0076] The display unit can use the emotion estimation function to analyze the emotions the user feels when viewing the proposal results and optimize the display method. For example, the generation AI in the display unit uses the emotion estimation function to analyze the emotions the user feels when viewing the proposal results. For example, it analyzes the user's facial expressions and voice and selects a display method that evokes positive emotions. The emotion estimation function can also be used to analyze the emotions the user feels when viewing the proposal results and optimize the display method. For example, it can provide a design or layout that helps the user relax. The generation AI can also use the emotion estimation function to analyze the emotions the user feels when viewing the proposal results and optimize the display method. For example, it can dynamically adjust the display content according to the user's emotions. This makes it possible to analyze the emotions the user feels when viewing the proposal results and optimize the display method.

[0077] The display unit can also display the suggestion results on other devices (smartwatches and smart speakers) so that the user can check them anywhere. For example, the display unit can cause the generation AI to display the suggestion results on a smartwatch so that the user can check them anywhere. For example, the user can check the suggestion results even when they are out and about. The suggestion results can also be displayed on a smart speaker so that the user can check them by voice. For example, the user can check the suggestion results using a voice command. The generation AI can also display the suggestion results on other devices (smartwatches and smart speakers) so that the user can check them anywhere. For example, the user can check the suggestion results even when they are out and about. This allows the suggestion results to also be displayed on other devices so that the user can check them anywhere.

[0078] The display unit adds a function that allows the suggestion results to be shared on social media, making it possible to share information with other users. The display unit, for example, adds a function that allows the generation AI to share the suggestion results on social media, making it possible to share information with other users. For example, a user shares a suggested store or product on social media. The display unit also adds a function that allows the suggestion results to be shared on social media, making it possible to share information with other users. For example, a user shares a suggested store or product with a friend. The display unit also adds a function that allows the generation AI to share the suggestion results on social media, making it possible to share information with other users. For example, a user shares a suggested store or product on social media. This adds a function that allows the suggestion results to be shared on social media, making it possible to share information with other users.

[0079] The display unit uses the emotion estimation function to monitor in real time the emotions a user feels when viewing the proposal results, and is able to continuously search for the optimal display method. For example, the generation AI in the display unit uses the emotion estimation function to monitor in real time the emotions a user feels when viewing the proposal results. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The emotion estimation function is also used to monitor in real time the emotions a user feels when viewing the proposal results, and is able to continuously search for the optimal display method. For example, it prioritizes the adoption of a display method that evokes positive emotions. The generation AI also uses the emotion estimation function to monitor in real time the emotions a user feels when viewing the proposal results, and is able to continuously search for the optimal display method. For example, it dynamically adjusts the display content based on the user's emotional response. This makes it possible to monitor in real time the emotions a user feels when viewing the proposal results, and is able to continuously search for the optimal display method.

[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 recommendation system can also be equipped with a food ingredient suggestion function that takes into account the user's health condition. For example, if the user connects to a health management app and inputs their current health condition and allergy information, the recommendation system can suggest appropriate food ingredients based on that information. Also, if the user wants to consume a specific nutrient, the system can prioritize suggesting food ingredients that contain a large amount of that nutrient. Furthermore, it is possible to suggest food ingredients based on the user's health goals (e.g., weight loss, muscle building).

[0082] The recommendation system can also suggest seasonal food recommendations based on the user's past purchase history. For example, it can analyze past data to identify ingredients that the user prefers to buy in the summer and suggest those ingredients for the summer. It can also take into account seasonal sales information to suggest the best places to buy them for the user. It can also suggest seasonal recipes and provide ideas for users to enjoy seasonal ingredients.

[0083] The recommendation system can also suggest environmentally friendly shopping sources based on the user's grocery list, for example prioritizing locally sourced ingredients to reduce the environmental impact of transportation. It can also suggest eco-friendly stores and stores that sell organic foods. It can also provide information (e.g., carbon footprint) to help users make environmentally conscious choices.

[0084] The system can also suggest recipes based on the user's ingredient list, taking into account the cooking difficulty and cooking time. For example, it can suggest recipes that can be cooked quickly on busy days and more elaborate recipes on days when the user has more time. It can also allow the user to select the cooking difficulty level, providing easy recipes for beginners and more challenging recipes for advanced users. It can also provide advice and tips based on the user's cooking skill.

[0085] The recommendation system can also have a function to notify the user of food storage methods and expiration dates based on the user's food list. For example, it can suggest storage methods for purchased ingredients and notify the user of appropriate storage methods. It can also manage expiration dates of ingredients and notify the user of ingredients that are approaching their expiration date. It can also suggest recipes using ingredients that are close to their expiration date, reducing food waste.

[0086] The proposed system uses emotion estimation to analyze the user's emotions toward the proposed recipes and suggest the most satisfying recipes. For example, it analyzes the user's facial expressions and voice to prioritize recipes that evoke positive emotions. It can also identify and suggest recipes that the user prefers based on the user's past emotional data. Furthermore, it can monitor the user's emotional responses in real time and dynamically adjust the suggestions.

[0087] The proposed system uses emotion estimation to analyze the user's emotions toward the suggested shopping locations and recommend the store that will provide the highest level of satisfaction. For example, it analyzes the user's facial expressions and voice to prioritize recommendations of stores that evoke positive emotions. It can also identify and recommend preferred stores based on the user's past emotional data. Furthermore, it can monitor the user's emotional responses in real time and dynamically adjust the recommendations.

[0088] The proposed system uses emotion estimation to analyze the user's emotions toward the suggested ingredients and recommend ingredients that evoke the most positive emotions. For example, it can analyze the user's facial expressions and voice to identify the ingredients they prefer. It can also recommend ingredients that evoke positive emotions based on the user's past emotional data. Furthermore, it can monitor the user's emotional responses in real time and dynamically adjust the suggestions.

[0089] The proposed system uses emotion estimation to analyze the user's emotions toward the proposed recipes and suggest the most satisfying recipes. For example, it analyzes the user's facial expressions and voice to prioritize recipes that evoke positive emotions. It can also identify and suggest recipes that the user prefers based on the user's past emotional data. Furthermore, it can monitor the user's emotional responses in real time and dynamically adjust the suggestions.

[0090] The proposed system uses an emotion estimation function to monitor the user's emotions toward suggested shopping locations in real time and continuously make optimal recommendations. For example, it can analyze the user's facial expressions and voice to calculate an emotion score. It can also prioritize recommendations for stores that evoke positive emotions based on the user's emotional response. Furthermore, it is possible to monitor the user's emotional response in real time and dynamically adjust the content of recommendations.

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

[0092] Step 1: The flyer reader reads the store's flyer. For example, the flyer's image data or PDF file is read, and the generation AI analyzes its contents. The flyer reader can also extract product names, prices, sale information, etc. The generation AI uses OCR technology to extract text information from the flyer and saves it in a database. Step 2: The ingredient list registration unit accepts the ingredient list registered by the user. For example, the user can enter a list in text format such as "tomatoes, milk, eggs, bread." The ingredient list registration unit can also accept voice and image input. Step 3: The purchasing suggestion unit compares the store flyers read by the flyer reading unit with the desired food list accepted by the food list registration unit to suggest the most suitable purchasing locations. Based on the product prices and sale information of each store, the generation AI calculates and suggests the store where the user can purchase the items at the lowest price. Step 4: The display unit displays the optimal shopping locations suggested by the shopping location suggestion unit to the user. For example, through a smartphone app or website, it displays the message, "The best deal is to buy tomatoes and milk at supermarket A, and eggs and bread at supermarket B."

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

[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. Using generative AI, A flyer reader that reads store flyers; an ingredient list registration unit that accepts the ingredient list registered by the user; a purchase suggestion unit that compares the store flyer read by the flyer reading unit with the desired ingredient list received by the ingredient list registration unit to suggest the most suitable purchase location; a display unit that displays the optimal purchasing destination suggested by the purchasing destination suggestion unit to the user. A system characterized by:

2. The flyer reading unit The product layout or design pattern is analyzed from the image data of the flyer, and the marketing strategy of the store is estimated.

2. The system of claim 1.

3. The flyer reading unit Based on the analysis results of the flyer, the inventory status or expected arrival date of the product is predicted and notified to the user.

2. The system of claim 1.

4. The flyer reading unit Analyzing the emotional impact of the flyer design or catch phrase on the user, and identifying the most effective flyer.

2. The system of claim 1.

5. The flyer reading unit The analysis results of the flyers will be integrated with data from digital advertising or online marketplaces to conduct comprehensive marketing analysis.

2. The system of claim 1.

6. The flyer reading unit A function to read out the contents of the flyer aloud has been added to accommodate visually impaired people.

2. The system of claim 1.

7. The flyer reading unit The emotions of the user when viewing the flyer are monitored in real time, and the optimal advertisement is displayed.

2. The system of claim 1.

8. The ingredient list registration unit Based on the ingredient list entered by the user, the generation AI proposes a recipe and automatically adds the necessary ingredients to the list.

2. The system of claim 1.

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