System

Abstract

A system is provided.SOLUTION: A means for collecting inventory data of a retailer, a means for collecting sales data, a means for collecting environmental data using an IoT sensor, a means for collecting data such as a purchase history and a preference of a consumer, a means for analyzing the inventory data, the sales data, the IoT sensor data, and the consumer data to specify a product whose expiration date is approaching, a means for generating discount information for the product whose expiration date is approaching, a means for transmitting the discount information to each consumer, and a means for receiving data of an ingredient held by the consumer; A system comprising: means for generating a customized recipe based thereon; means for generating an additional ingredient list and discount information thereof based on the recipe; means for providing the additional ingredient list and discount information to a consumer; and means for collecting consumer feedback data and improving the analyzing means based thereon.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] Modern retailers face serious problems of excess inventory and expired food waste due to misforecasting of consumer demand and improper management of the supply chain. This not only results in economic losses but also increases the burden on the environment. Meanwhile, consumers also lack the appropriate information and tools to reduce food waste. This invention aims to solve these problems for both retailers and consumers. [Means for solving the problem]

[0005] This invention provides a system that collects and analyzes retailers' inventory data, sales data, IoT sensor data, and consumer data. Specifically, it identifies products with approaching expiration dates based on the collected data and generates discount information for them. It also generates customized recipes based on data on ingredients the consumer owns, and provides a list of additional ingredients and their discount information. By collecting consumer feedback data and improving analytical methods based on that data, it is possible to reduce food waste and achieve efficient inventory management. A system that includes this series of functions can solve the problems of both retailers and consumers.

[0006] "Retailer" refers to a business or store that sells products to consumers.

[0007] "Inventory data" refers to data that includes information such as the quantity of specific products, storage location, and expiration date.

[0008] "Sales data" refers to data related to sales, including past sales history, prices, and purchaser information.

[0009] "IoT sensor data" refers to environmental data such as temperature, humidity, and location information collected from sensors connected to the Internet.

[0010] "Consumer data" refers to information such as consumer purchasing history, product preferences, and feedback.

[0011] "Analysis" refers to the process of using collected data to extract specific information and make judgments or predictions.

[0012] "Best before date" refers to the date by which food is safe and tasty to consume.

[0013] "Discount Information" refers to the conditions and details of price reductions for specific products.

[0014] "Customized recipe" refers to cooking instructions that are specially created based on an individual consumer's inventory, preferences, and ingredients.

[0015] "Additional Ingredient List" refers to a list of additional ingredients that the consumer must purchase to complete the suggested recipe.

[0016] "Feedback data" refers to post-purchase ratings and opinions provided by consumers.

[0017] A "generative AI model" refers to an algorithm that uses techniques such as machine learning and deep learning to analyze data and generate new information and predictions. [Brief explanation of the drawings]

[0018] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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, a 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), and an APU (Accelerated Processing Unit).

[0022] 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.

[0023] 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.

[0024] 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), Bluetooth (registered trademark), etc.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0030] 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.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] Overall system configuration

[0040] This invention is a system that collects and analyzes retailers' inventory data, sales data, IoT sensor data, and consumer data, presents discount information for products with approaching expiration dates, and generates customized recipes based on the ingredients the consumer has on hand.

[0041] Program processing

[0042] Data collection

[0043] 1. Server

[0044] Accessing retailer databases to periodically collect inventory and sales data, including product quantities, storage locations, expiration dates, and past sales history.

[0045] In addition, IoT sensors will be used to collect environmental data within the store (temperature, humidity, shopping cart position, etc.).

[0046] Access consumer databases to collect consumer purchasing history, preferences, and feedback information.

[0047] Data analysis

[0048] 2. Server

[0049] All collected data is passed to a generative AI model for analysis, which identifies products with an approaching expiration date and generates discount information for those products.

[0050] For example, if you have tomatoes in stock with a best-by date of two days, set a 20% discount coupon for tomatoes.

[0051] It also takes into account the consumer's purchasing history and preferences to create individually optimized discount offers.

[0052] Delivery of bargain information

[0053] 3. Server

[0054] The generated discount information is customized for each consumer, and an electronic notification is sent to each consumer's terminal.

[0055] For example, a notification may be sent to the consumer saying, "Tomatoes are 20% off, expiration date is in 2 days."

[0056] 4. Terminal

[0057] App notifications are displayed on the user's smartphone, visually presenting bargain information to consumers.

[0058] Collecting User Input

[0059] 5. Users

[0060] Open the smartphone app and enter the ingredients you have and the amounts. For example, enter "300g chicken breast, 2 cloves of garlic."

[0061] 6. Terminal

[0062] The entered information is sent to the server.

[0063] Creating a customized recipe

[0064] 7. Server

[0065] Based on the received user data, the generative AI model generates customized recipes, such as suggesting "pasta with chicken breast and tomatoes."

[0066] Calculates the additional ingredients needed and their quantities and generates a shopping list. For example, it determines that you need 200g of pasta and 2 tablespoons of olive oil.

[0067] 8. Terminal

[0068] Display a suggested recipe and additional ingredient list to the user.

[0069] Shopping list and discount coupons provided

[0070] 9. Server

[0071] Generate a shopping list of the ingredients you need and add corresponding discount coupons, for example, "Pasta 200g - Discount coupon 10% off", "Tomato - Discount coupon 20% off".

[0072] 10. Terminal

[0073] A shopping list and discount coupons are displayed on the user's smartphone, allowing them to shop efficiently.

[0074] Purchasing data feedback

[0075] 11. Users

[0076] After shopping, users enter their purchases and their ratings through the app. For example, "I bought five tomatoes, and the chicken breast and tomato pasta was delicious."

[0077] 12. Terminal

[0078] Send the feedback data to the server.

[0079] 13. Server

[0080] Based on your feedback, we will update our database and reflect it in our next analysis and offer of bargains.

[0081] Specific examples

[0082] server

[0083] Collect inventory data for tomatoes from a retailer and identify that five of them have a best-by date two days later. This will qualify for a discount, so set up a 20% off coupon for the tomatoes.

[0084] Terminal

[0085] A notification appears on the user's smartphone saying, "Tomatoes 20% off, expiration date in 2 days."

[0086] User

[0087] Log in to the app and enter the information you have: 300g chicken breast, 2 cloves of garlic.

[0088] server

[0089] The received data is analyzed and a recipe for "chicken breast and tomato pasta" is suggested, with the additional recommendation of "200g of pasta and 2 tablespoons of olive oil."

[0090] Terminal

[0091] It displays suggested recipes and ingredient lists to users and allows them to apply discount coupons.

[0092] User

[0093] After shopping, customers provide feedback, which is received by the server and updated in the database.

[0094] In this way, the system of the present invention allows retailers to efficiently improve inventory management and food waste, and also allows consumers to shop more efficiently.

[0095] The processing flow will be explained below.

[0096] Step 1:

[0097] server

[0098] Access the retailer's database and collect inventory data. Here, obtain information such as product quantity, storage location, and expiration date. For example, obtain data such as "Tomatoes: 10 units in stock, expiration date in 2 days."

[0099] Step 2:

[0100] server

[0101] Collect sales data. Obtain data including past sales history, prices, and buyer information. For example, collect data such as "Tomatoes: 60 units sold in the past 30 days, an average of 2 units per day."

[0102] Step 3:

[0103] server

[0104] IoT sensors are used to collect environmental data. For example, sensor data such as the temperature, humidity, and location of shopping carts in the store is acquired. Specifically, data such as "Store temperature: 20°C, humidity: 50%" is collected.

[0105] Step 4:

[0106] server

[0107] Collect data on consumer purchasing history and preferences. For example, obtain a list of products purchased by Consumer A in the past and their preferences, such as "Consumer A: has purchased tomatoes five times."

[0108] Step 5:

[0109] server

[0110] It analyzes collected inventory data, sales data, IoT sensor data, and consumer data, and uses a generative AI model to identify products that are approaching their expiration date. For example, it detects five tomatoes that have two days left until their expiration date.

[0111] Step 6:

[0112] server

[0113] Based on the analyzed data, discount information is generated for products that are close to their expiration date. For example, a 20% discount coupon is created for tomatoes.

[0114] Step 7:

[0115] server

[0116] The generated discount information is sent to individual consumers. For example, a notification of 20% off tomatoes with an expiration date of 2 days is sent to "Consumer A."

[0117] Step 8:

[0118] Terminal

[0119] Display a notification on the user's smartphone. For example, a notification like "Tomatoes 20% off, expiration date in 2 days" will be displayed on the smartphone.

[0120] Step 9:

[0121] User

[0122] Open the app and enter the ingredients you have and the quantities. For example, enter "300g chicken breast, 2 cloves of garlic" into the app.

[0123] Step 10:

[0124] Terminal

[0125] The entered data is sent to the server. For example, the data "300g chicken breast, 2 cloves of garlic" is sent to the server.

[0126] Step 11:

[0127] server

[0128] Based on the received user data, the generative AI model generates customized recipes, such as suggesting a recipe for "pasta with chicken breast and tomatoes."

[0129] Step 12:

[0130] server

[0131] Calculate the amount of additional ingredients needed for the recipe. For example, include "200g pasta, 2 tablespoons olive oil" in your list.

[0132] Step 13:

[0133] Terminal

[0134] Presents the user with a customized recipe and a list of additional ingredients, for example, "Chicken breast and tomato pasta, plus 200g pasta and 2 tablespoons olive oil."

[0135] Step 14:

[0136] server

[0137] Add discount coupons to a list of required additional ingredients, for example, "Pasta 200g - 10% off discount coupon, Tomato - 20% off discount coupon".

[0138] Step 15:

[0139] Terminal

[0140] It displays shopping lists and discount coupons on the user's smartphone, allowing them to shop efficiently.

[0141] Step 16:

[0142] User

[0143] After shopping, users enter their purchases and their ratings through the app. For example, they can provide feedback such as, "I bought five tomatoes, and the chicken breast and tomato pasta was delicious."

[0144] Step 17:

[0145] Terminal

[0146] The provided feedback data is sent to the server. For example, data such as "I bought 5 tomatoes and the pasta was delicious" is sent to the server.

[0147] Step 18:

[0148] server

[0149] We use the feedback we receive to update our database so that it can be reflected in our next analysis and offer.

[0150] Example 1

[0151] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0152] Traditional retail inventory management has been problematic due to the difficulty of efficient inventory management and expiration date management, resulting in food waste. It has also been difficult to provide appropriate discount information to consumers, resulting in insufficient motivation to purchase. Furthermore, it has been difficult to provide customized recipe suggestions that take into account the ingredients that consumers have on hand, making it difficult to provide an efficient shopping experience.

[0153] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0154] In this invention, the server includes: means for collecting inventory information from retailers; means for collecting sales information; means for collecting environmental information using IoT technology; means for collecting information on consumer purchasing behavior and preferences; means for analyzing the inventory information, sales information, IoT information, and consumer information to identify products with upcoming expiration dates; means for generating discount information for the products with upcoming expiration dates; means for transmitting the discount information to each consumer; means for receiving information on ingredients owned by the consumer and generating customized recipes based thereon; means for generating an additional ingredient list and its discount information based on the recipe; means for providing the additional ingredient list and discount information to consumers; and means for collecting consumer evaluation information and improving the analysis means based thereon. This enables more efficient inventory management and reduced food waste, and enables an efficient shopping experience by providing consumers with optimized discount information and customized recipe suggestions.

[0155] "Inventory information" refers to data about the quantity, storage location, expiration date, etc. of products held by a retailer.

[0156] "Sales information" refers to data obtained by retailers regarding the past sales history and sales amount of each product.

[0157] "IoT technology" is a technology that allows physical devices and sensors to communicate with each other via the Internet and collect and exchange data.

[0158] "Environmental information" refers to data about the environment collected using IoT technology, such as the temperature and humidity inside the store and the location of shopping carts.

[0159] "Consumer information" refers to data relating to consumer purchasing behavior, preferences, evaluations, etc.

[0160] "Analysis means" refers to the function of analyzing data using collected inventory information, sales information, IoT information, and consumer information.

[0161] "Discount information" is data relating to discount coupons and discount rates that are applied to products that are close to their expiration date.

[0162] An "electronic notification" is a notification message sent from a server to a consumer's terminal.

[0163] "Customized recipes" are personalized recipes that are suggested based on the ingredients a consumer has on hand.

[0164] The "additional ingredient list" is data listing additional ingredients and their amounts required to realize a customized recipe.

[0165] "Evaluation information" refers to data regarding evaluations and feedback that consumers have given regarding products or services they have actually purchased.

[0166] This invention builds a system that allows for efficient inventory management, reduces food waste, and provides consumers with an optimal shopping experience through collaboration between servers, terminals, and users.

[0167] Hardware and software used

[0168] The server utilizes a high-performance cloud infrastructure, using MySQL for database management and TensorFlow for data analysis. The MQTT protocol is also used to collect data from IoT sensors. A mobile app is installed on user devices, and the app communicates with the server via a Node.js server and REST API.

[0169] Data collection

[0170] The server periodically queries the retailer's database to obtain inventory and sales information. This data includes product quantities, storage locations, expiration dates, and past sales history. It also collects environmental information such as temperature, humidity, and shopping cart locations from IoT sensors in the store. Consumer information, such as purchasing behavior, preferences, and feedback, is obtained through the consumer database.

[0171] Data analysis

[0172] The server passes all collected data to a generative AI model for data analysis. This model identifies products that are close to their expiration date and generates discount information for them. For example, if there are tomatoes in stock that are close to their expiration date, the generative AI model will set a 20% discount coupon for the tomatoes. The model also creates individually optimized discount offers based on the consumer's purchasing history and preferences.

[0173] Delivery of bargain information

[0174] The server customizes the generated discount information for each consumer and sends an electronic notification to each consumer's device. For example, a notification may be sent saying, "Tomatoes 20% off, expiration date in 2 days." The device receives this notification and displays it as an app notification on the user's smartphone.

[0175] Collecting User Input

[0176] The user opens the smartphone app and inputs the ingredients they have and their quantities. For example, they might input "300g chicken breast, 2 cloves of garlic." The device then sends this information in JSON format to the server.

[0177] Creating a customized recipe

[0178] The server uses the received user data to generate a customized recipe using a generative AI model. For example, it suggests a recipe for "pasta with chicken breast and tomatoes." It also calculates the additional ingredients needed and their quantities, and generates a shopping list. For example, it determines that "200g of pasta and 2 tablespoons of olive oil" are needed.

[0179] Shopping list and discount coupons provided

[0180] The server generates a shopping list of the necessary ingredients and provides corresponding discount coupons. For example, "Pasta 200g - Discount coupon 10% off" or "Tomato - Discount coupon 20% off." The device receives this information and displays the shopping list and discount coupons on the user's smartphone.

[0181] Purchasing data feedback

[0182] After shopping, users enter the items they actually purchased and their ratings through the app. For example, they might enter, "I bought five tomatoes, and the chicken breast and tomato pasta was delicious." The device receives this feedback data and sends it to the server. The server updates the database based on the feedback, and reflects it in future analyses and discount information.

[0183] This allows retailers to efficiently improve inventory management and food waste, while providing consumers with an optimal shopping experience.

[0184] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0185] Step 1:

[0186] Data collection

[0187] The server accesses the retailer's database and periodically retrieves inventory and sales information.

[0188] Specific actions

[0189] The server queries a MySQL database to retrieve product quantities, storage locations, expiration dates, and past sales history.

[0190] Input: The server uses the connection information to the database and the query as input.

[0191] Output: The server stores the acquired inventory and sales information in its internal data storage.

[0192] Step 2:

[0193] Collecting environmental data using IoT sensors

[0194] The server collects environmental information such as temperature, humidity, and shopping cart location inside the store from IoT sensors.

[0195] Specific actions

[0196] The server uses the MQTT protocol to collect data from each sensor.

[0197] Input: Real-time data sent from IoT sensors.

[0198] Output: The server stores the collected environmental information in its internal data storage.

[0199] Step 3:

[0200] Collection of Consumer Information

[0201] The server accesses a consumer database to obtain purchasing behavior, preferences, and feedback information.

[0202] Specific actions

[0203] The server sends a request to the consumer database via an API to retrieve the required information.

[0204] Input: A request to a consumer database via an API.

[0205] Output: Store consumer purchase history, preferences, and feedback information in the server's internal data storage.

[0206] Step 4:

[0207] Data analysis

[0208] The server passes the collected inventory information, sales information, IoT information, and consumer information to the generative AI model for data analysis.

[0209] Specific actions

[0210] The server uses TensorFlow to identify products that are nearing their expiration date and generate discount offers.

[0211] Input: Inventory information, sales information, IoT information, consumer information.

[0212] Output: Products with upcoming expiration dates and discount information.

[0213] Step 5:

[0214] Discount information generation and notification

[0215] The server customizes the discount information for each consumer and sends an electronic notification to the consumer's terminal.

[0216] Specific actions

[0217] Use a Node.js server to send notifications using FCM (Firebase Cloud Messaging).

[0218] Input: Generated discount information, consumer's device information.

[0219] Output: A customized discount notification for each consumer.

[0220] Step 6:

[0221] Consumer food ingredient data entry

[0222] Users input the information about ingredients they have into the smartphone app.

[0223] Specific actions

[0224] The user enters the names and quantities of ingredients into the app's input form and clicks the submit button.

[0225] Input: Ingredient information entered by the consumer.

[0226] Output: The entered data is sent from the terminal to the server.

[0227] Step 7:

[0228] Creating a customized recipe

[0229] The server generates a customized recipe using a generative AI model based on the received user data.

[0230] Specific actions

[0231] The server uses a Python script to generate recipes that take into account the ingredients the user has and create additional ingredient lists.

[0232] Input: Ingredient information entered by the consumer.

[0233] Output: A customized recipe with an additional ingredients list.

[0234] Step 8:

[0235] View recipes and ingredient lists

[0236] The device displays the suggested recipe and a list of additional ingredients to the user.

[0237] Specific actions

[0238] The smartphone app displays the recipe information and ingredient list received from the server on its interface.

[0239] Input: Recipe information received from the server and additional ingredient list.

[0240] Output: Detailed information displayed for the user to see.

[0241] Step 9:

[0242] Shopping list and discount coupons provided

[0243] The server generates a shopping list of the necessary ingredients and provides corresponding discount coupons.

[0244] Specific actions

[0245] The server generates discount coupons for additional ingredients calculated by the generative AI model and adds them to the shopping list.

[0246] Input: Customized recipe information, additional ingredient information.

[0247] Output: Additional ingredients list and corresponding discount coupon.

[0248] Step 10:

[0249] View shopping list and coupons

[0250] The terminal displays a shopping list and discount coupons on the user's smartphone.

[0251] Specific actions

[0252] The app displays the shopping list and discount coupons received from the server on the screen for easy access by the user.

[0253] Input: Shopping list and discount coupons received from the server.

[0254] Output: Detailed information displayed for the user to see.

[0255] Step 11:

[0256] Collecting consumer evaluation data

[0257] After shopping, users enter the items they purchased and their ratings into the app.

[0258] Specific actions

[0259] Users fill out the evaluation form through the app interface and click the submit button.

[0260] Input: Rating information entered by the consumer.

[0261] Output: The entered rating information is sent from the terminal to the server.

[0262] Step 12:

[0263] Submitting evaluation data

[0264] The terminal transmits the collected evaluation data to the server.

[0265] Specific actions

[0266] The app sends rating data in JSON format to the server, communicating via HTTP POST requests.

[0267] Input: Rating information entered by the consumer.

[0268] Output: The rating data is sent to the server.

[0269] Step 13:

[0270] Database Update

[0271] The server updates the database based on the received evaluation information and reflects it in the next analysis and the provision of discount information.

[0272] Specific actions

[0273] The server analyzes the rating data and performs a process to update its internal database.

[0274] Input: Evaluation data.

[0275] Output: The updated database.

[0276] (Application example 1)

[0277] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0278] Conventional inventory management systems have difficulty efficiently identifying products with approaching expiration dates and providing appropriate discount information to consumers. Furthermore, few systems offer customized recipes based on the ingredients a consumer has, failing to improve the consumer's purchasing experience. Furthermore, there is a lack of a way to visually present product discount information, creating technical challenges for improving the work efficiency of store staff.

[0279] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0280] In this invention, the server includes: means for collecting inventory data of retailers; means for collecting sales data; means for collecting environmental data using IoT sensors; means for collecting data such as consumer purchasing history and preferences; means for analyzing the inventory data, sales data, IoT sensor data, and consumer data using a generative AI model to identify products with upcoming expiration dates; means for generating discount information for the products with upcoming expiration dates; means for transmitting the discount information to each consumer; means for receiving data on ingredients owned by the consumer and generating customized recipes based thereon; means for generating an additional ingredient list and its discount information based on the recipes; means for providing the additional ingredient list and its discount information to the consumer; means for visually presenting the discount information on smart glasses or a head-mounted display; and means for collecting consumer feedback data and improving the analysis means based thereon. This enables efficient inventory management, providing appropriate discount information to consumers, generating customized recipes, and improving the work efficiency of store staff.

[0281] A "retailer" is a business that sells products directly to general consumers.

[0282] "Inventory data" refers to information relating to the quantity, storage location, expiration date, etc. of products in stores and warehouses.

[0283] "Sales data" refers to information relating to past sales history and sales.

[0284] An "IoT sensor" is a sensor device for collecting physical environmental data (e.g., temperature, humidity, location data, etc.).

[0285] "Environmental data" refers to data such as the temperature and humidity inside the store, and the location of shopping carts.

[0286] "Consumer data" refers to data such as consumer purchasing history, preferences, and feedback information.

[0287] A "generative AI model" is an artificial intelligence model that analyzes collected data and is generated to achieve a specific purpose.

[0288] "Discount information" is information such as the discount rate for a product.

[0289] A "customized recipe" is an individually optimized recipe generated based on the ingredients the consumer has on hand.

[0290] The "additional ingredient list" is a list showing the additional ingredients required and their quantities based on the generated recipe.

[0291] "Smart glasses or head-mounted display" refers to a wearable device for visually displaying information.

[0292] "Visually presented" means displaying information directly in the user's field of vision.

[0293] "Feedback data" refers to data such as ratings and post-purchase opinions provided by consumers.

[0294] This invention provides a system that allows retailers to improve the efficiency of inventory management in brick-and-mortar stores and provide appropriate discount information to consumers. The overall configuration and specific operation of the system are described below.

[0295] Overall system overview

[0296] The system mainly consists of a server, smart glasses or head-mounted displays (HMDs), and IoT sensors. The server collects and analyzes various data, generates the necessary information, and sends it to the smart devices. The smart devices then visually present the information to the user.

[0297] Data collection and analysis

[0298] The server accesses the retailer's database to periodically collect inventory and sales data. It also collects environmental data (temperature, humidity, location information) from IoT sensors installed in the store. It also accesses the consumer database to collect consumer purchasing history, preferences, and feedback information.

[0299] All collected data is passed to a generative AI model for analysis. The model identifies products that are close to expiring and generates discount offers for those products. For example, a 20% off coupon for tomatoes that are close to expiring will be set. The model also creates discount offers optimized for each consumer.

[0300] Discount information and feedback

[0301] Discount and stock information is visually presented to users using smart glasses or HMDs, such as Microsoft HoloLens or Google Glass. When users stand in front of a shelf, discount information for products is displayed on the device's transparent display.

[0302] It also generates recipes: users input the ingredients they have on hand, and the generative AI model generates the optimal recipe, calculating any additional ingredients needed and discount information, allowing consumers to shop efficiently.

[0303] After shopping, users can provide feedback through the app. The feedback data is sent to the server and reflected in the database. This will be used for future analysis and to provide discount information.

[0304] Specific examples

[0305] The server analyzes the store's inventory data and generates discount information such as a 20% discount on tomatoes with a best-by date of two days. This information is visually communicated to the user via smart glasses or an HMD. When the user inputs 300g of chicken breast and two cloves of garlic as ingredients on hand, the generative AI model suggests a recipe for "chicken breast and tomato pasta," and determines that "200g of pasta and two tablespoons of olive oil" are also required, generating discount information for this.

[0306] Prompt Sentence Examples

[0307] Consider a specific example of an AR application that assists in inventory management in a physical store. When approaching a shelf, the system displays discount information for products with an approaching expiration date in the field of view of smart glasses. Please explain in detail what kind of interface and notification method you could use.

[0308] In this way, the system of the present invention can improve the work efficiency of store staff and provide consumers with appropriate information in real time, thereby providing an efficient shopping experience.

[0309] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0310] Step 1:

[0311] The server accesses the retailer's database and collects inventory and sales data. This includes information such as product quantity, storage location, and expiration date. The inventory and sales data is periodically retrieved using an API. The input is the database query results, and the output is organized inventory and sales data stored in the server's memory.

[0312] Step 2:

[0313] The server collects environmental data from IoT sensors. Data such as temperature, humidity, and shopping cart location information is obtained from the IoT sensors via an API. The input is real-time data sent from the sensors, and the output is organized environmental data that is stored in the server's memory. Specifically, the server receives data sent from sensors in the store and converts it into an analyzable format.

[0314] Step 3:

[0315] The server accesses the consumer database to collect consumer purchasing history, preferences, and feedback information. The input is the query results from the consumer database, and the output is organized consumer data stored in the server's memory. Specifically, it obtains each consumer's past purchasing history and provided feedback in an analyzable format.

[0316] Step 4:

[0317] The server passes the collected inventory data, sales data, IoT sensor data, and consumer data to the generative AI model for analysis. All collected data is input, and the output is a list of products with upcoming expiration dates and their discount information. Specifically, the data is input into the AI ​​model, and discount information for specific products is generated as an analysis result. For example, a 20% off coupon is set for tomatoes with an expiration date in two days.

[0318] Step 5:

[0319] The server sends the generated discount information to each consumer's device. The input is the generated discount information, and the output is a notification sent to the consumer's device. Specifically, the discount coupon information is sent to the consumer's smartphone as an electronic notification.

[0320] Step 6:

[0321] The user opens the smartphone app and inputs the ingredients they have and the amount. The input is the ingredient data the user has, and the output is the ingredient data sent to the server. Specifically, the user enters the ingredient data into the app's input form and presses the send button, which sends the data to the server.

[0322] Step 7:

[0323] The server uses a generative AI model to generate customized recipes based on the received user data. The inputs are the user's ingredient data and inventory data, and the output is the generated recipe and a list of additional ingredients. Specifically, the ingredient data is input into the AI ​​model, which generates the optimal recipe and a list of required additional ingredients. For example, it proposes a recipe for "pasta with chicken breast and tomatoes" and determines that "200g of pasta and 2 tablespoons of olive oil" are required.

[0324] Step 8:

[0325] The server generates a shopping list of the necessary ingredients and assigns corresponding discount coupons. The input is a customized recipe and a list of additional ingredients, and the output is a shopping list with discount coupons. Specifically, the server sets discount information for the additional ingredients and generates the entire list.

[0326] Step 9:

[0327] The user can check the suggested recipe and additional ingredient list on their smartphone. The input is the recipe information sent from the server, and the output is the information displayed on the user's device. Specifically, the user opens the smartphone app to check the recipe and shopping list.

[0328] Step 10:

[0329] Users enter their post-shopping experience and product evaluation as feedback through a smartphone app. The input is the user's feedback data, and the output is feedback information sent to the server. Specifically, users enter their thoughts in the app's feedback form and press the send button.

[0330] Step 11:

[0331] The server analyzes the received feedback data and updates the database to reflect it in the next analysis and discount information provision. The input is the user's feedback data, and the output is an updated database. Specifically, the feedback information is input into the analysis model to improve the accuracy of future analysis algorithms.

[0332] The above processing steps enable efficient inventory management, appropriate discount information to consumers, and the creation of customized recipes.

[0333] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0334] Overall system configuration

[0335] This system collects and analyzes retailers' inventory data, sales data, IoT sensor data, and consumer data, presents discount information for products with approaching expiration dates, and generates customized recipes based on the ingredients the consumer has and the user's emotional data. It also collects actual consumer purchasing data and feedback, and optimizes the system based on that data.

[0336] Program processing

[0337] Data collection

[0338] 1. Server

[0339] Access the retailer's database to collect inventory data. This includes information such as product quantity, storage location, and expiration date. For example, this includes data such as "Tomatoes: 10 units in stock, expiration date in 2 days."

[0340] Sales data is also collected in the same way. Past sales history, prices, purchaser information, etc. are obtained. For example, data such as "Tomatoes: 60 units sold in the past 30 days, an average of 2 units per day" is collected.

[0341] IoT sensors are used to collect in-store environmental data (temperature, humidity, shopping cart location, etc.). Specifically, data is collected for "in-store temperature: 20°C, humidity: 50%."

[0342] Access consumer databases to collect consumer purchasing history, preferences, and feedback information, such as "Consumer A: Purchased tomatoes 5 times."

[0343] Data analysis

[0344] 2. Server

[0345] All collected data is passed to a generative AI model for analysis. Here, products with an approaching expiration date are identified and discount information for those products is generated. For example, if "five tomatoes with an expiration date in two days" are identified, a "20% off discount coupon for tomatoes" is created.

[0346] Delivery of bargain information

[0347] 3. Server

[0348] Based on the analysis results, the system generates bargain information tailored to each individual consumer and sends an electronic notification to their device. For example, a notification will be sent to "Consumer A" that "Tomatoes are 20% off, with the expiration date in two days."

[0349] 4. Terminal

[0350] A notification will appear on the consumer's smartphone, for example, "Tomatoes 20% off, expiration date in 2 days."

[0351] User input collection and sentiment data collection

[0352] 5. Users

[0353] Open the smartphone app and enter the ingredients you have and the quantities. For example, enter "300g chicken breast, 2 cloves of garlic" into the app.

[0354] The app uses a built-in emotion engine to recognize the user's emotions (e.g., happy, tired, etc.).

[0355] 6. Terminal

[0356] The input food ingredient data and emotion data are sent to the server. For example, the data "300g chicken breast, 2 cloves of garlic" and "Emotion: fun" are sent to the server.

[0357] Creating a customized recipe

[0358] 7. Server

[0359] Based on the received user data and emotion data, the generative AI model generates customized recipes. For example, it determines that "it's okay if the cooking time is long because the user seems to enjoy it," and suggests a recipe such as "chicken breast and tomato pasta."

[0360] It calculates the additional ingredients needed and their quantities and generates a shopping list. For example, the list might include "200g pasta, 2 tablespoons olive oil."

[0361] 8. Terminal

[0362] The suggested recipe and a list of additional ingredients are displayed to the user, for example, "Chicken breast and tomato pasta, plus 200g pasta and 2 tablespoons olive oil."

[0363] Shopping list and discount coupons provided

[0364] 9. Server

[0365] Add discount coupons to a list of required additional ingredients, for example, "Pasta 200g - 10% off discount coupon, Tomato - 20% off discount coupon".

[0366] 10. Terminal

[0367] A shopping list and discount coupons are displayed on the user's smartphone, allowing them to shop efficiently.

[0368] Purchasing data feedback

[0369] 11. Users

[0370] After shopping, users enter their purchases and their ratings through the app. For example, they can provide feedback such as, "I bought five tomatoes, and the chicken breast and tomato pasta was delicious."

[0371] 12. Terminal

[0372] Send feedback data to the server. For example, "I bought 5 tomatoes and the pasta was delicious."

[0373] 13. Server

[0374] We use your feedback to update our database, which can be reflected in our next analysis and offer.

[0375] Specific examples

[0376] server

[0377] Collect inventory data for tomatoes from a retailer and identify that five of them have a best-by date two days later. This will qualify for a discount, so set up a 20% off coupon for the tomatoes.

[0378] Terminal

[0379] A notification appears on the user's smartphone saying, "Tomatoes 20% off, expiration date in 2 days."

[0380] User

[0381] You log in to the app and enter the information you have, such as "300g chicken breast and 2 cloves of garlic," and the emotion engine recognizes that you are in a happy state.

[0382] server

[0383] The received data is analyzed and a recipe for "chicken breast and tomato pasta" is suggested, with the additional recommendation of "200g of pasta and 2 tablespoons of olive oil."

[0384] Terminal

[0385] It displays suggested recipes and ingredient lists to users and allows them to take advantage of discount coupons.

[0386] User

[0387] After shopping, customers provide feedback, which is received by the server and updated in the database.

[0388] In this way, the system of the present invention can also utilize user emotional data to optimize inventory management for retailers and the consumer purchasing experience.

[0389] The processing flow will be explained below.

[0390] Step 1:

[0391] server

[0392] Access the retailer's database and collect inventory data. Specifically, obtain information such as product quantity, storage location, expiration date, etc. For example, obtain data such as "Tomatoes: 10 units in stock, expiration date in 2 days."

[0393] Step 2:

[0394] server

[0395] Collect sales data, such as past sales history, prices, and buyer information. For example, obtain data such as "Tomatoes: 60 units sold in the past 30 days, an average of 2 units per day."

[0396] Step 3:

[0397] server

[0398] IoT sensors are used to collect environmental data. Specifically, sensor data such as the temperature, humidity, and location of shopping carts in the store is collected. For example, data such as "Store temperature: 20°C, humidity: 50%" is acquired.

[0399] Step 4:

[0400] server

[0401] Access consumer databases to collect consumer purchasing history, preferences, and feedback information. For example, obtain data such as "Consumer A: Purchased tomatoes five times in the past."

[0402] Step 5:

[0403] server

[0404] The collected inventory data, sales data, IoT sensor data, and consumer data are passed to a generative AI model for analysis. The analysis identifies products with an approaching expiration date. For example, "identify five tomatoes with an expiration date in two days."

[0405] Step 6:

[0406] server

[0407] Generate discount information for identified products that are close to their expiration date. For example, "Set a 20% discount coupon for tomatoes."

[0408] Step 7:

[0409] server

[0410] Customize the discount information generated for each consumer and prepare an electronic notification to send to each consumer. For example, send a notification to "Consumer A" that "Tomatoes are 20% off, expiration date is 2 days away."

[0411] Step 8:

[0412] Terminal

[0413] A notification will be displayed on the consumer's smartphone, for example, "Tomatoes 20% off, expiration date in 2 days."

[0414] Step 9:

[0415] User

[0416] Open the smartphone app and enter the ingredients you have and the quantities. For example, enter "300g chicken breast, 2 cloves of garlic."

[0417] Step 10:

[0418] Terminal

[0419] The app's emotion engine recognizes the user's emotions, for example, detecting emotions such as "fun" using the camera and microphone.

[0420] Step 11:

[0421] Terminal

[0422] The input food ingredient data and emotion data are sent to the server. For example, the data "300g chicken breast, 2 cloves of garlic" and "Emotion: happy" are sent to the server.

[0423] Step 12:

[0424] server

[0425] Based on the received user data and emotion data, the generative AI model generates customized recipes. For example, it determines that the user will enjoy cooking, so it may be okay to wait a long time and suggests a recipe for "chicken breast and tomato pasta."

[0426] Step 13:

[0427] server

[0428] Generate a shopping list by calculating additional ingredients and their quantities, for example, "200g pasta, 2 tablespoons olive oil"

[0429] Step 14:

[0430] Terminal

[0431] Presents the user with a customized recipe and a list of additional ingredients, such as "Chicken breast and tomato pasta, plus 200g pasta and 2 tablespoons olive oil."

[0432] Step 15:

[0433] server

[0434] Add discount coupons to a list of required additional ingredients, for example, "Pasta 200g - 10% off discount coupon, Tomato - 20% off discount coupon".

[0435] Step 16:

[0436] Terminal

[0437] It displays shopping lists and discount coupons on the user's smartphone, allowing them to shop efficiently.

[0438] Step 17:

[0439] User

[0440] After shopping, users enter their purchases and their ratings through the app. For example, they can provide feedback such as, "I bought five tomatoes, and the chicken breast and tomato pasta was delicious."

[0441] Step 18:

[0442] Terminal

[0443] Send feedback data to the server. For example, "I bought 5 tomatoes, and the chicken breast and tomato pasta was delicious."

[0444] Step 19:

[0445] server

[0446] We use the feedback we receive to update our database, which can be used to analyze data and provide deals to you next time.

[0447] In this way, by linking the server, terminal, and user, a system can be created that utilizes user emotional data and provides a more personalized purchasing experience.

[0448] Example 2

[0449] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0450] Modern retailers are faced with the complex task of managing a wide variety of products, making inventory management and expiration date management cumbersome. Consumers also struggle to find new recipes that make the most of the ingredients they already have. Providing recipes that match consumers' emotions and current desires is particularly challenging, posing many challenges for providing an optimal shopping experience.

[0451] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0452] In this invention, the server includes means for collecting inventory data, means for collecting sales data, means for collecting environmental data, means for collecting data such as consumer purchasing history and preferences, means for analyzing the inventory data, sales data, environmental data, and consumer data to identify products with upcoming expiration dates, means for generating discount information for the products with upcoming expiration dates, means for transmitting the discount information to each consumer, means for receiving data on ingredients owned by the consumer and generating customized recipes based thereon, means for generating an additional ingredient list and its discount information based on the recipe, means for providing the additional ingredient list and discount information to the consumer, means for collecting consumer feedback data and improving the analysis means based thereon, and means for collecting emotion data and suggesting optimal recipes to the consumer based thereon. This makes it possible to improve inventory management efficiency and provide optimal recipes and discount information to individual consumers.

[0453] "Inventory data" refers to information about inventory, such as product quantities, storage locations, and expiration dates.

[0454] "Sales data" refers to data including transaction records, prices, and purchaser information when a retailer sells a product.

[0455] "Environmental data" refers to information about the environment, such as temperature, humidity, and the location of shopping carts within a store, collected through IoT sensors.

[0456] "Consumer data" refers to information about consumer behavior and trends, such as consumer purchasing history, preferences, and feedback information.

[0457] "Best before" refers to the period during which food or consumable products can maintain their quality, after which the quality may deteriorate.

[0458] "Discount Information" refers to information about price discounts and coupons offered for specific products.

[0459] "Customized recipes" refer to cooking instructions that are individually generated based on the consumer's ingredients, preferences, and emotional data.

[0460] "Emotional data" refers to data that indicates a consumer's emotional state (e.g., happy, tired, etc.).

[0461] "Additional Ingredient List" means a list of additional ingredients and their quantities required to prepare a customized recipe.

[0462] "Feedback data" refers to data containing reactions and impressions about products and services, such as ratings and opinions provided by consumers.

[0463] "Generative AI model" refers to an artificial intelligence model that analyzes collected data and performs a specific task (e.g., generating a recipe or presenting a discount coupon).

[0464] This system collects and analyzes inventory data, sales data, environmental data, and consumer data from retailers, generates discount information for products approaching their expiration date, and provides customized recipes based on the consumer's ingredients and emotional data. The system consists of the following main components:

[0465] 1. Data Collection

[0466] server:

[0467] The server accesses the retailer's database to collect inventory data. Specific techniques include using SQL queries. For example, data is retrieved using "SELECT FROM inventory WHERE expiration_date < '2023-10-20'". Sales data is also collected, including transaction records, prices, and customer information. Furthermore, IoT sensors are used to collect environmental data such as in-store temperature, humidity, and shopping cart location. Finally, a consumer database is accessed to retrieve consumer purchasing history, preferences, and feedback information.

[0468] 2. Data Analysis

[0469] server:

[0470] The server inputs the collected inventory data, sales data, environmental data, and consumer data into a generative AI model for analysis. The generative AI model uses libraries such as TensorFlow and PyTorch. Here, it identifies products with an approaching expiration date and generates discount information for those products. For example, it creates a 20% discount coupon for "five tomatoes with an expiration date in two days."

[0471] 3. Delivery of bargain information

[0472] server:

[0473] Based on the analysis results, the system generates bargain information tailored to each individual consumer and sends an electronic notification to each consumer's device. For example, the notification might read, "Consumer A receives 20% off tomatoes with a best-by date in two days."

[0474] Device:

[0475] The device displays a notification on the consumer's smartphone, for example, "Tomatoes 20% off, expiration date in 2 days."

[0476] 4. Collecting user input and emotion data

[0477] User:

[0478] The user opens the smartphone app and inputs the ingredients they have and the amounts. Specifically, they input "300g chicken breast, 2 cloves of garlic" into the app. The app also uses its built-in emotion engine to recognize the user's emotions.

[0479] Device:

[0480] The device sends the input food ingredient data and emotion data to the server. For example, data such as "300g chicken breast, 2 cloves of garlic" and "emotion: fun" are sent to the server.

[0481] 5. Creating a customized recipe

[0482] server:

[0483] The server uses the received user data and emotional data to generate a customized recipe using a generative AI model. For example, it determines that "it seems like the user will enjoy cooking it, so it's okay if it takes a long time to cook," and suggests a recipe such as "chicken breast and tomato pasta." It also calculates the additional ingredients needed and their quantities, and generates a shopping list. For example, the list might include "200g of pasta and 2 tablespoons of olive oil."

[0484] Device:

[0485] The device will then display a suggested recipe and a list of additional ingredients to the user, such as "Chicken breast and tomato pasta, plus 200g pasta and 2 tablespoons olive oil."

[0486] 6. Providing shopping lists and discount coupons

[0487] server:

[0488] The server will add discount coupons to the list of required additional ingredients, for example "Pasta 200g - 10% off discount coupon, Tomato - 20% off discount coupon".

[0489] Device:

[0490] The device displays a shopping list and discount coupons on the user's smartphone, allowing them to shop efficiently.

[0491] 7. Feedback of purchasing data

[0492] User:

[0493] After shopping, users can enter their purchases and their ratings through the app, for example, "I bought five tomatoes, and the chicken breast and tomato pasta was delicious."

[0494] Device:

[0495] The device sends feedback data to the server, for example, "I bought five tomatoes and the pasta was delicious."

[0496] server:

[0497] The server can update the database based on the feedback and reflect it in the next analysis or offer of bargains.

[0498] This allows the system to utilize user emotion data to improve the efficiency of inventory management for retailers and optimize the consumer purchasing experience. For example, the following prompt can be used: "User emotion: Fun, Ingredients on hand: 300g chicken breast, 2 cloves garlic."

[0499] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0500] Step 1: Data collection

[0501] Server: The server accesses the retailer's database to collect inventory data. This data includes product quantities, storage locations, expiration dates, and so on. For example, it retrieves data using an SQL query like "SELECT FROM inventory WHERE expiration_date < '2023-10-20'". Sales data includes transaction records, prices, and purchaser information, and is collected in a similar manner. Furthermore, it retrieves environmental data such as in-store temperature, humidity, and shopping cart location through IoT sensors. An example of environmental data is "store temperature: 20°C, humidity: 50%". Finally, it retrieves consumer purchasing history, preferences, and feedback information from the consumer database. These data are collected as input and stored in the database as output.

[0502] Step 2: Data analysis

[0503] Server: Collected inventory data, sales data, environmental data, and consumer data are input into the generative AI model and data analysis is performed. For example, a generative AI model written in Python uses libraries such as TensorFlow and PyTorch. Based on the input data, products with an approaching expiration date are identified. As a result of this analysis, for example, if "five tomatoes with an expiration date in two days" are identified, a "20% off discount coupon for tomatoes" is created. Data input is information from various databases, and discount information is obtained as output.

[0504] Step 3: Generate and distribute deals

[0505] Server: Based on the analysis results, it generates bargain information suitable for each individual consumer and sends this information to each consumer's device in an electronic notification. For example, it sends a notification to consumer A offering tomatoes at 20% off with the expiration date in two days. The input is the analysis results and individual consumer information, and the output is the delivery of an electronic notification.

[0506] Terminal: The terminal displays a notification on the consumer's smartphone. For example, it displays a notification saying, "Tomatoes are 20% off, expiration date is in 2 days." The input is the notification information from the server, and the output is the display on the smartphone screen.

[0507] Step 4: Collecting user input and emotion data

[0508] User: The user opens the smartphone app and inputs the ingredients they have and the quantities. For example, they input "300g chicken breast, 2 cloves of garlic" into the app. The app also uses its built-in emotion engine to recognize the user's emotion data. The input is the user's actions, and the output is the input data.

[0509] Terminal: The terminal sends the input food ingredient data and emotion data to the server. For example, the data "300g chicken breast, 2 cloves of garlic" and "Emotion: fun" are sent to the server. The input is the data entered by the user, and the output is the data sent to the server.

[0510] Step 5: Generate a customized recipe

[0511] Server: The server uses the generative AI model to generate a customized recipe based on the received user data and emotional data. For example, it determines that "it's okay if the cooking time is long because the user seems to enjoy it," and suggests a recipe such as "chicken breast and tomato pasta." It also calculates the additional ingredients needed and their quantities, and generates a shopping list. For example, the list might include "200g pasta and 2 tablespoons olive oil." The input is user data and emotional data, and the output is a customized recipe and a shopping list.

[0512] Terminal: The terminal displays the suggested recipe and a list of additional ingredients to the user. For example, "Chicken breast and tomato pasta, plus 200g of pasta and 2 tablespoons of olive oil." The input is the recipe information from the server, and the output is the display on the smartphone screen.

[0513] Step 6: Provide a shopping list and discount coupons

[0514] Server: The server adds discount coupons to a list of required additional ingredients. For example, it generates "Pasta 200g - 10% off discount coupon, Tomato - 20% off discount coupon". The input is a shopping list, and the output is a list with discount coupons.

[0515] Terminal: The terminal displays a shopping list and discount coupons on the user's smartphone, allowing for efficient shopping. The input is discount coupon information from the server, and the output is the display on the smartphone screen.

[0516] Step 7: Feedback on purchasing data

[0517] User: After shopping, the user enters the items they actually purchased and their evaluation through the app. For example, they provide feedback such as, "I bought five tomatoes, and the chicken breast and tomato pasta was delicious." The input is the evaluation after the purchase, and the output is the feedback data.

[0518] Terminal: The terminal sends feedback data to the server. For example, the terminal sends data such as "I bought five tomatoes and the pasta was delicious." The input is the user's feedback data, and the output is the data sent to the server.

[0519] Server: The server updates the database based on the feedback and reflects it in the next analysis and the provision of bargain information. The input is the feedback data, and the output is the updated database information.

[0520] (Application example 2)

[0521] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0522] In today's retail industry, there is a demand for more efficient inventory management and improved consumer satisfaction. Unsold and discarded products approaching their expiration date present significant challenges for retailers. There is also a need to improve the purchasing experience through services such as personalized recommendations and recipe provision. However, simultaneously addressing these challenges requires the collection and analysis of various data and real-time notifications to consumers, which existing systems are not yet able to adequately address. Further advances are needed, particularly in real-time notifications utilizing the latest technologies, including smart glasses, and the provision of customized recipes that take emotional data into account.

[0523] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0524] In this invention, the server includes: means for collecting inventory data of retailers; means for collecting sales data; means for collecting environmental data using IoT sensors; means for collecting data such as consumer purchasing history and preferences; means for analyzing the inventory data, sales data, IoT sensor data, and consumer data to identify products with upcoming expiration dates; means for generating discount information for the products with upcoming expiration dates; means for transmitting the discount information to each consumer; means for receiving data on ingredients owned by the consumer and generating customized recipes based thereon; means for generating an additional ingredient list and its discount information based on the recipes; means for providing the additional ingredient list and the discount information to the consumer; means for collecting consumer feedback data and improving the analysis means based thereon; means for notifying discount information for products with upcoming expiration dates in real time using smart glasses; and means for collecting consumer emotion data and taking the emotion data into consideration when generating the recipes. This improves the accuracy of inventory management and enables personalized suggestions and recipes to be provided to consumers in real time, thereby improving the purchasing experience.

[0525] "Inventory data" is a collection of information such as the quantity, type, expiration date, and storage location of products held by a retailer.

[0526] "Sales data" is a collection of data including the history of past sales of products, prices, purchaser information, and so on.

[0527] "IoT sensor data" refers to environmental data such as temperature, humidity, and shopping cart location collected using IoT sensors.

[0528] "Consumer data" is a collection of information such as a consumer's purchasing history, preferences, and feedback.

[0529] "Analysis means" refers to a technical means for analyzing collected data and identifying products that are close to their expiration date.

[0530] "Discount information" refers to information about price reductions offered for products that are close to their expiration date.

[0531] "Notification means" refers to a technical means for sending discount information and recipe information to each consumer.

[0532] A "customized recipe" is a personalized cooking procedure generated based on the consumer's ingredient and emotional data.

[0533] The "additional ingredient list" is a list showing the additional ingredients and their quantities required for cooking based on the customized recipe.

[0534] "Smart glasses" are eyeglass-type devices that have the ability to display information in real time.

[0535] "Emotional data" refers to information used to recognize a consumer's emotional state and analyze that data.

[0536] "Feedback data" refers to data such as post-purchase ratings and impressions provided by consumers.

[0537] Overall system overview

[0538] This invention is a system that collects and analyzes retailers' inventory data, sales data, IoT sensor data, and consumer data, presents discount information for products approaching their expiration date, and generates customized recipes based on the ingredients and emotional data of consumers. It also collects actual consumer purchasing data and feedback, and optimizes the system based on that data.

[0539] Hardware and software used

[0540] The main hardware used is as follows:

[0541] Smart glasses (e.g., general smart wearable devices)

[0542] IoT sensors (e.g. temperature sensors, humidity sensors)

[0543] Server (analyzes and stores data)

[0544] The main software used is as follows:

[0545] Generative AI models (e.g., GPT-4)

[0546] Database management system (e.g. MySQL)

[0547] Emotion Recognition Engine

[0548] Detailed process description

[0549] The server performs the following steps:

[0550] 1. Data Collection

[0551] The server accesses the retailer's database and collects inventory data, sales data, IoT sensor data, and consumer data. For example, inventory data includes information such as product quantity, storage location, and expiration date. IoT sensors are also used to collect environmental data such as temperature, humidity, and shopping cart location within the store. Consumer data includes purchasing history, preferences, and feedback information.

[0552] 2. Data Analysis

[0553] The collected data is analyzed by a generative AI model (e.g., GPT-4). As a result of the analysis, products with an approaching expiration date are identified, and discount information for those products is generated. For example, if there are "five tomatoes with an expiration date in two days," a "20% off discount coupon for tomatoes" is generated.

[0554] 3. Notification function

[0555] The server sends the generated discount information to the consumer's smart glasses in real time. For example, a notification such as "Tomatoes 20% off, expiration date in 2 days" is displayed. This information is displayed on the smart glasses' display, allowing the consumer to understand it immediately.

[0556] 4. Collecting User Input

[0557] The user inputs the information about the ingredients they have through the smart glasses, and the emotion engine collects emotional data. For example, if the user inputs "300g chicken breast, 2 cloves of garlic," the emotion recognition engine will recognize the user's emotion as "fun." This data is then sent to the server.

[0558] 5. Creating a customized recipe

[0559] The server uses a generative AI model (e.g., GPT-4) to generate a customized recipe based on the received user's ingredient and emotion data. For example, a recipe such as "chicken breast and tomato pasta" is suggested. Additionally, additional ingredients and their amounts are calculated, resulting in a list of "200g pasta and 2 tablespoons olive oil."

[0560] 6. Providing shopping lists and discount coupons

[0561] The generated recipe, additional ingredients list, and discount coupons are displayed on the smart glasses, allowing users to shop efficiently.

[0562] Examples of concrete examples and prompts

[0563] Examples:

[0564] A user is shopping in a physical store while wearing smart glasses. Data collected by the server includes inventory information for tomatoes (10 in stock, expiration date in 2 days). The user inputs the ingredients they have (300g chicken breast, 2 cloves of garlic) and recognizes that their emotion is "happy." Based on this, the server suggests "pasta with chicken breast and tomatoes," lists "200g pasta, 2 tablespoons olive oil" as additional ingredients, and provides a discount coupon.

[0565] Example prompt sentence:

[0566] prompt:

[0567] Analyze the following data and suggest suitable recipes for the user.

[0568] Stock Data: Tomatoes - 10 in stock, expiration date in 2 days

[0569] Emotional data: Fun

[0570] Ingredients I have: 300g chicken breast, 2 cloves of garlic

[0571] Required steps:

[0572] 1. Identify products that are nearing their expiration date and generate discount information.

[0573] 2. Suggest recipes that take into account the ingredients and emotional data of the user.

[0574] 3. Make a list of any additional ingredients you need.

[0575] Example output: Chicken breast and tomato pasta, 200g extra pasta, 2 tablespoons olive oil

[0576] This invention improves the accuracy of inventory management and makes it possible to provide individual suggestions and recipes to consumers in real time, thereby improving the purchasing experience.

[0577] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0578] Step 1:

[0579] The server accesses the retailer's database and collects inventory data. Specifically, it obtains information such as product quantity, expiration date, and storage location. For example, it collects data such as "Tomatoes: 10 in stock, expiration date 2 days away."

[0580] Input: Inventory data from the database

[0581] Output: Collected inventory data

[0582] What it does: Retrieves inventory information from a database using an SQL query.

[0583] Step 2:

[0584] The server collects sales data, including past sales history, prices, and buyer information. For example, data such as "Tomatoes: 60 units sold in the past 30 days, an average of 2 units per day" is collected.

[0585] Input: Sales data from the database

[0586] Output: Collected sales data

[0587] What it does: Retrieves sales information from a database using an SQL query.

[0588] Step 3:

[0589] The server uses IoT sensors to collect environmental data, such as the temperature and humidity inside the store and the location of shopping carts. For example, it obtains data such as "Store temperature: 20°C, humidity: 50%."

[0590] Input: Environmental data from IoT sensors

[0591] Output: Collected environmental data

[0592] Specific operation: Call the IoT sensor's data collection API to obtain environmental information.

[0593] Step 4:

[0594] The server collects data such as consumer purchasing history and preferences. For example, it collects information such as "Consumer A: purchased tomatoes 5 times."

[0595] Input: Consumer data from a database

[0596] Output: Collected consumer data

[0597] What it does: Retrieves consumer information using an SQL query.

[0598] Step 5:

[0599] The server analyzes the collected inventory data, sales data, IoT sensor data, and consumer data to identify products with an approaching expiration date. For example, it identifies "five tomatoes with an expiration date in two days."

[0600] Input: Inventory data, sales data, IoT sensor data, consumer data

[0601] Output: Identified products with an approaching expiration date

[0602] Specific operation: Using a generative AI model, data is analyzed and target products are identified.

[0603] Step 6:

[0604] The server generates discount information for products that are close to their expiration date. For example, it generates a "20% off discount coupon for tomatoes."

[0605] Input: Identified products with an approaching expiration date

[0606] Output: Generated discount coupon

[0607] Specific operation: Executes the discount information generation algorithm to create a coupon.

[0608] Step 7:

[0609] The server sends the generated discount information to the consumer, who then sends a notification to the smart glasses, displaying information such as "20% off tomatoes, expiration date in 2 days."

[0610] Input: Generated discount coupon

[0611] Output: Notification sent to smart glasses

[0612] What it does: Sends information to consumer devices using the Notification API.

[0613] Step 8:

[0614] The user inputs the information about the ingredients they have through the smart glasses, for example, "300g chicken breast, 2 cloves of garlic."

[0615] Input: User's ingredient information

[0616] Output: Input food ingredients data

[0617] Specific operation: Collect food ingredient data through the input interface of smart glasses.

[0618] Step 9:

[0619] The emotion engine of the device recognizes the user's emotion data and sends it to the server. For example, the user's emotion is recognized as "happy."

[0620] Input: User's emotional state

[0621] Output: Recognized emotion data

[0622] Specific operation: Analyzes input data using an emotion recognition engine and recognizes the emotional state.

[0623] Step 10:

[0624] The server generates a customized recipe based on the received user's ingredients and emotion data. For example, it might suggest "chicken breast and tomato pasta" and add "200g pasta, 2 tablespoons olive oil."

[0625] Input: food data, emotion data

[0626] Output: Customized recipe, additional ingredients list

[0627] How it works: Uses a generative AI model to generate a customized recipe and calculate any additional ingredients needed.

[0628] Step 11:

[0629] The server displays the generated recipe, additional ingredients list, and discount coupons on the smart glasses, allowing users to shop efficiently.

[0630] Input: Customized recipes, additional ingredient lists, discount coupons

[0631] Output: Information displayed on the smart glasses

[0632] Specific operation: Send information to the smart glasses through the display interface.

[0633] Step 12:

[0634] After shopping, the user provides feedback through the smart glasses, for example, "I bought five tomatoes and the pasta was delicious."

[0635] Input: User feedback data

[0636] Output: Input feedback data

[0637] Specific operation: Collect feedback data through the input interface of smart glasses.

[0638] Step 13:

[0639] The server analyzes the collected feedback data and updates the database to reflect it in the next analysis and offer of deals.

[0640] Input: Feedback data

[0641] Output: Updated database

[0642] Specific behavior: Parse the feedback data and execute SQL queries to update the database.

[0643] 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.

[0644] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0645] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0646] [Second embodiment]

[0647] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0648] 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.

[0649] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0650] 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.

[0651] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0652] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0653] 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.

[0654] 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.

[0655] 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 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.

[0656] 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.

[0657] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0658] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0659] Overall system configuration

[0660] This invention is a system that collects and analyzes retailers' inventory data, sales data, IoT sensor data, and consumer data, presents discount information for products with approaching expiration dates, and generates customized recipes based on the ingredients the consumer has on hand.

[0661] Program processing

[0662] Data collection

[0663] 1. Server

[0664] Accessing retailer databases to periodically collect inventory and sales data, including product quantities, storage locations, expiration dates, and past sales history.

[0665] In addition, IoT sensors will be used to collect environmental data within the store (temperature, humidity, shopping cart position, etc.).

[0666] Access consumer databases to collect consumer purchasing history, preferences, and feedback information.

[0667] Data analysis

[0668] 2. Server

[0669] All collected data is passed to a generative AI model for analysis, which identifies products with an approaching expiration date and generates discount information for those products.

[0670] For example, if you have tomatoes in stock with a best-by date of two days, set a 20% discount coupon for tomatoes.

[0671] It also takes into account the consumer's purchasing history and preferences to create individually optimized discount offers.

[0672] Delivery of bargain information

[0673] 3. Server

[0674] The generated discount information is customized for each consumer, and an electronic notification is sent to each consumer's terminal.

[0675] For example, a notification may be sent to the consumer saying, "Tomatoes are 20% off, expiration date is in 2 days."

[0676] 4. Terminal

[0677] App notifications are displayed on the user's smartphone, visually presenting bargain information to consumers.

[0678] Collecting User Input

[0679] 5. Users

[0680] Open the smartphone app and enter the ingredients you have and the amounts. For example, enter "300g chicken breast, 2 cloves of garlic."

[0681] 6. Terminal

[0682] The entered information is sent to the server.

[0683] Creating a customized recipe

[0684] 7. Server

[0685] Based on the received user data, the generative AI model generates customized recipes, such as suggesting "pasta with chicken breast and tomatoes."

[0686] Calculates the additional ingredients needed and their quantities and generates a shopping list. For example, it determines that you need 200g of pasta and 2 tablespoons of olive oil.

[0687] 8. Terminal

[0688] Display a suggested recipe and additional ingredient list to the user.

[0689] Shopping list and discount coupons provided

[0690] 9. Server

[0691] Generate a shopping list of the ingredients you need and add corresponding discount coupons, for example, "Pasta 200g - Discount coupon 10% off", "Tomato - Discount coupon 20% off".

[0692] 10. Terminal

[0693] A shopping list and discount coupons are displayed on the user's smartphone, allowing them to shop efficiently.

[0694] Purchasing data feedback

[0695] 11. Users

[0696] After shopping, users enter their purchases and their ratings through the app. For example, "I bought five tomatoes, and the chicken breast and tomato pasta was delicious."

[0697] 12. Terminal

[0698] Send the feedback data to the server.

[0699] 13. Server

[0700] Based on your feedback, we will update our database and reflect it in our next analysis and offer of bargains.

[0701] Specific examples

[0702] server

[0703] Collect inventory data for tomatoes from a retailer and identify that five of them have a best-by date two days later. This will qualify for a discount, so set up a 20% off coupon for the tomatoes.

[0704] Terminal

[0705] A notification appears on the user's smartphone saying, "Tomatoes 20% off, expiration date in 2 days."

[0706] User

[0707] Log in to the app and enter the information you have: 300g chicken breast, 2 cloves of garlic.

[0708] server

[0709] The received data is analyzed and a recipe for "chicken breast and tomato pasta" is suggested, with the additional recommendation of "200g of pasta and 2 tablespoons of olive oil."

[0710] Terminal

[0711] It displays suggested recipes and ingredient lists to users and allows them to apply discount coupons.

[0712] User

[0713] After shopping, customers provide feedback, which is received by the server and updated in the database.

[0714] In this way, the system of the present invention allows retailers to efficiently improve inventory management and food waste, and also allows consumers to shop more efficiently.

[0715] The processing flow will be explained below.

[0716] Step 1:

[0717] server

[0718] Access the retailer's database and collect inventory data. Here, obtain information such as product quantity, storage location, and expiration date. For example, obtain data such as "Tomatoes: 10 units in stock, expiration date in 2 days."

[0719] Step 2:

[0720] server

[0721] Collect sales data. Obtain data including past sales history, prices, and buyer information. For example, collect data such as "Tomatoes: 60 units sold in the past 30 days, an average of 2 units per day."

[0722] Step 3:

[0723] server

[0724] IoT sensors are used to collect environmental data. For example, sensor data such as the temperature, humidity, and location of shopping carts in the store is acquired. Specifically, data such as "Store temperature: 20°C, humidity: 50%" is collected.

[0725] Step 4:

[0726] server

[0727] Collect data on consumer purchasing history and preferences. For example, obtain a list of products purchased by Consumer A in the past and their preferences, such as "Consumer A: has purchased tomatoes five times."

[0728] Step 5:

[0729] server

[0730] It analyzes collected inventory data, sales data, IoT sensor data, and consumer data, and uses a generative AI model to identify products that are approaching their expiration date. For example, it detects five tomatoes that have two days left until their expiration date.

[0731] Step 6:

[0732] server

[0733] Based on the analyzed data, discount information is generated for products that are close to their expiration date. For example, a 20% discount coupon is created for tomatoes.

[0734] Step 7:

[0735] server

[0736] The generated discount information is sent to individual consumers. For example, a notification of 20% off tomatoes with an expiration date of 2 days is sent to "Consumer A."

[0737] Step 8:

[0738] Terminal

[0739] Display a notification on the user's smartphone. For example, a notification like "Tomatoes 20% off, expiration date in 2 days" will be displayed on the smartphone.

[0740] Step 9:

[0741] User

[0742] Open the app and enter the ingredients you have and the quantities. For example, enter "300g chicken breast, 2 cloves of garlic" into the app.

[0743] Step 10:

[0744] Terminal

[0745] The entered data is sent to the server. For example, the data "300g chicken breast, 2 cloves of garlic" is sent to the server.

[0746] Step 11:

[0747] server

[0748] Based on the received user data, the generative AI model generates customized recipes, such as suggesting a recipe for "pasta with chicken breast and tomatoes."

[0749] Step 12:

[0750] server

[0751] Calculate the amount of additional ingredients needed for the recipe. For example, include "200g pasta, 2 tablespoons olive oil" in your list.

[0752] Step 13:

[0753] Terminal

[0754] Presents the user with a customized recipe and a list of additional ingredients, for example, "Chicken breast and tomato pasta, plus 200g pasta and 2 tablespoons olive oil."

[0755] Step 14:

[0756] server

[0757] Add discount coupons to a list of required additional ingredients, for example, "Pasta 200g - 10% off discount coupon, Tomato - 20% off discount coupon".

[0758] Step 15:

[0759] Terminal

[0760] It displays shopping lists and discount coupons on the user's smartphone, allowing them to shop efficiently.

[0761] Step 16:

[0762] User

[0763] After shopping, users enter their purchases and their ratings through the app. For example, they can provide feedback such as, "I bought five tomatoes, and the chicken breast and tomato pasta was delicious."

[0764] Step 17:

[0765] Terminal

[0766] The provided feedback data is sent to the server. For example, data such as "I bought 5 tomatoes and the pasta was delicious" is sent to the server.

[0767] Step 18:

[0768] server

[0769] We use the feedback we receive to update our database so that it can be reflected in our next analysis and offer.

[0770] Example 1

[0771] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0772] Traditional retail inventory management has been problematic due to the difficulty of efficient inventory management and expiration date management, resulting in food waste. It has also been difficult to provide appropriate discount information to consumers, resulting in insufficient motivation to purchase. Furthermore, it has been difficult to provide customized recipe suggestions that take into account the ingredients that consumers have on hand, making it difficult to provide an efficient shopping experience.

[0773] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0774] In this invention, the server includes: means for collecting inventory information from retailers; means for collecting sales information; means for collecting environmental information using IoT technology; means for collecting information on consumer purchasing behavior and preferences; means for analyzing the inventory information, sales information, IoT information, and consumer information to identify products with upcoming expiration dates; means for generating discount information for the products with upcoming expiration dates; means for transmitting the discount information to each consumer; means for receiving information on ingredients owned by the consumer and generating customized recipes based thereon; means for generating an additional ingredient list and its discount information based on the recipe; means for providing the additional ingredient list and discount information to consumers; and means for collecting consumer evaluation information and improving the analysis means based thereon. This enables more efficient inventory management and reduced food waste, and enables an efficient shopping experience by providing consumers with optimized discount information and customized recipe suggestions.

[0775] "Inventory information" refers to data about the quantity, storage location, expiration date, etc. of products held by a retailer.

[0776] "Sales information" refers to data obtained by retailers regarding the past sales history and sales amount of each product.

[0777] "IoT technology" is a technology that allows physical devices and sensors to communicate with each other via the Internet and collect and exchange data.

[0778] "Environmental information" refers to data about the environment collected using IoT technology, such as the temperature and humidity inside the store and the location of shopping carts.

[0779] "Consumer information" refers to data relating to consumer purchasing behavior, preferences, evaluations, etc.

[0780] "Analysis means" refers to the function of analyzing data using collected inventory information, sales information, IoT information, and consumer information.

[0781] "Discount information" is data relating to discount coupons and discount rates that are applied to products that are close to their expiration date.

[0782] An "electronic notification" is a notification message sent from a server to a consumer's terminal.

[0783] "Customized recipes" are personalized recipes that are suggested based on the ingredients a consumer has on hand.

[0784] The "additional ingredient list" is data listing additional ingredients and their amounts required to realize a customized recipe.

[0785] "Evaluation information" refers to data regarding evaluations and feedback that consumers have given regarding products or services they have actually purchased.

[0786] This invention builds a system that allows for efficient inventory management, reduces food waste, and provides consumers with an optimal shopping experience through collaboration between servers, terminals, and users.

[0787] Hardware and software used

[0788] The server utilizes a high-performance cloud infrastructure, using MySQL for database management and TensorFlow for data analysis. The MQTT protocol is also used to collect data from IoT sensors. A mobile app is installed on user devices, and the app communicates with the server via a Node.js server and REST API.

[0789] Data collection

[0790] The server periodically queries the retailer's database to obtain inventory and sales information. This data includes product quantities, storage locations, expiration dates, and past sales history. It also collects environmental information such as temperature, humidity, and shopping cart locations from IoT sensors in the store. Consumer information, such as purchasing behavior, preferences, and feedback, is obtained through the consumer database.

[0791] Data analysis

[0792] The server passes all collected data to a generative AI model for data analysis. This model identifies products that are close to their expiration date and generates discount information for them. For example, if there are tomatoes in stock that are close to their expiration date, the generative AI model will set a 20% discount coupon for the tomatoes. The model also creates individually optimized discount offers based on the consumer's purchasing history and preferences.

[0793] Delivery of bargain information

[0794] The server customizes the generated discount information for each consumer and sends an electronic notification to each consumer's device. For example, a notification may be sent saying, "Tomatoes 20% off, expiration date in 2 days." The device receives this notification and displays it as an app notification on the user's smartphone.

[0795] Collecting User Input

[0796] The user opens the smartphone app and inputs the ingredients they have and their quantities. For example, they might input "300g chicken breast, 2 cloves of garlic." The device then sends this information in JSON format to the server.

[0797] Creating a customized recipe

[0798] The server uses the received user data to generate a customized recipe using a generative AI model. For example, it suggests a recipe for "pasta with chicken breast and tomatoes." It also calculates the additional ingredients needed and their quantities, and generates a shopping list. For example, it determines that "200g of pasta and 2 tablespoons of olive oil" are needed.

[0799] Shopping list and discount coupons provided

[0800] The server generates a shopping list of the necessary ingredients and provides corresponding discount coupons. For example, "Pasta 200g - Discount coupon 10% off" or "Tomato - Discount coupon 20% off." The device receives this information and displays the shopping list and discount coupons on the user's smartphone.

[0801] Purchasing data feedback

[0802] After shopping, users enter the items they actually purchased and their ratings through the app. For example, they might enter, "I bought five tomatoes, and the chicken breast and tomato pasta was delicious." The device receives this feedback data and sends it to the server. The server updates the database based on the feedback, and reflects it in future analyses and discount information.

[0803] This allows retailers to efficiently improve inventory management and food waste, while providing consumers with an optimal shopping experience.

[0804] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0805] Step 1:

[0806] Data collection

[0807] The server accesses the retailer's database and periodically retrieves inventory and sales information.

[0808] Specific actions

[0809] The server queries a MySQL database to retrieve product quantities, storage locations, expiration dates, and past sales history.

[0810] Input: The server uses the connection information to the database and the query as input.

[0811] Output: The server stores the acquired inventory and sales information in its internal data storage.

[0812] Step 2:

[0813] Collecting environmental data using IoT sensors

[0814] The server collects environmental information such as temperature, humidity, and shopping cart location inside the store from IoT sensors.

[0815] Specific actions

[0816] The server uses the MQTT protocol to collect data from each sensor.

[0817] Input: Real-time data sent from IoT sensors.

[0818] Output: The server stores the collected environmental information in its internal data storage.

[0819] Step 3:

[0820] Collection of Consumer Information

[0821] The server accesses a consumer database to obtain purchasing behavior, preferences, and feedback information.

[0822] Specific actions

[0823] The server sends a request to the consumer database via an API to retrieve the required information.

[0824] Input: A request to a consumer database via an API.

[0825] Output: Store consumer purchase history, preferences, and feedback information in the server's internal data storage.

[0826] Step 4:

[0827] Data analysis

[0828] The server passes the collected inventory information, sales information, IoT information, and consumer information to the generative AI model for data analysis.

[0829] Specific actions

[0830] The server uses TensorFlow to identify products that are nearing their expiration date and generate discount offers.

[0831] Input: Inventory information, sales information, IoT information, consumer information.

[0832] Output: Products with upcoming expiration dates and discount information.

[0833] Step 5:

[0834] Discount information generation and notification

[0835] The server customizes the discount information for each consumer and sends an electronic notification to the consumer's terminal.

[0836] Specific actions

[0837] Use a Node.js server to send notifications using FCM (Firebase Cloud Messaging).

[0838] Input: Generated discount information, consumer's device information.

[0839] Output: A customized discount notification for each consumer.

[0840] Step 6:

[0841] Consumer food ingredient data entry

[0842] Users input the information about ingredients they have into the smartphone app.

[0843] Specific actions

[0844] The user enters the names and quantities of ingredients into the app's input form and clicks the submit button.

[0845] Input: Ingredient information entered by the consumer.

[0846] Output: The entered data is sent from the terminal to the server.

[0847] Step 7:

[0848] Creating a customized recipe

[0849] The server generates a customized recipe using a generative AI model based on the received user data.

[0850] Specific actions

[0851] The server uses a Python script to generate recipes that take into account the ingredients the user has and create additional ingredient lists.

[0852] Input: Ingredient information entered by the consumer.

[0853] Output: A customized recipe with an additional ingredients list.

[0854] Step 8:

[0855] View recipes and ingredient lists

[0856] The device displays the suggested recipe and a list of additional ingredients to the user.

[0857] Specific actions

[0858] The smartphone app displays the recipe information and ingredient list received from the server on its interface.

[0859] Input: Recipe information received from the server and additional ingredient list.

[0860] Output: Detailed information displayed for the user to see.

[0861] Step 9:

[0862] Shopping list and discount coupons provided

[0863] The server generates a shopping list of the necessary ingredients and provides corresponding discount coupons.

[0864] Specific actions

[0865] The server generates discount coupons for additional ingredients calculated by the generative AI model and adds them to the shopping list.

[0866] Input: Customized recipe information, additional ingredient information.

[0867] Output: Additional ingredients list and corresponding discount coupon.

[0868] Step 10:

[0869] View shopping list and coupons

[0870] The terminal displays a shopping list and discount coupons on the user's smartphone.

[0871] Specific actions

[0872] The app displays the shopping list and discount coupons received from the server on the screen for easy access by the user.

[0873] Input: Shopping list and discount coupons received from the server.

[0874] Output: Detailed information displayed for the user to see.

[0875] Step 11:

[0876] Collecting consumer evaluation data

[0877] After shopping, users enter the items they purchased and their ratings into the app.

[0878] Specific actions

[0879] Users fill out the evaluation form through the app interface and click the submit button.

[0880] Input: Rating information entered by the consumer.

[0881] Output: The entered rating information is sent from the terminal to the server.

[0882] Step 12:

[0883] Submitting evaluation data

[0884] The terminal transmits the collected evaluation data to the server.

[0885] Specific actions

[0886] The app sends rating data in JSON format to the server, communicating via HTTP POST requests.

[0887] Input: Rating information entered by the consumer.

[0888] Output: The rating data is sent to the server.

[0889] Step 13:

[0890] Database Update

[0891] The server updates the database based on the received evaluation information and reflects it in the next analysis and the provision of discount information.

[0892] Specific actions

[0893] The server analyzes the rating data and performs a process to update its internal database.

[0894] Input: Evaluation data.

[0895] Output: The updated database.

[0896] (Application example 1)

[0897] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0898] Conventional inventory management systems have difficulty efficiently identifying products with approaching expiration dates and providing appropriate discount information to consumers. Furthermore, few systems offer customized recipes based on the ingredients a consumer has, failing to improve the consumer's purchasing experience. Furthermore, there is a lack of a way to visually present product discount information, creating technical challenges for improving the work efficiency of store staff.

[0899] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0900] In this invention, the server includes: means for collecting inventory data of retailers; means for collecting sales data; means for collecting environmental data using IoT sensors; means for collecting data such as consumer purchasing history and preferences; means for analyzing the inventory data, sales data, IoT sensor data, and consumer data using a generative AI model to identify products with upcoming expiration dates; means for generating discount information for the products with upcoming expiration dates; means for transmitting the discount information to each consumer; means for receiving data on ingredients owned by the consumer and generating customized recipes based thereon; means for generating an additional ingredient list and its discount information based on the recipes; means for providing the additional ingredient list and its discount information to the consumer; means for visually presenting the discount information on smart glasses or a head-mounted display; and means for collecting consumer feedback data and improving the analysis means based thereon. This enables efficient inventory management, providing appropriate discount information to consumers, generating customized recipes, and improving the work efficiency of store staff.

[0901] A "retailer" is a business that sells products directly to general consumers.

[0902] "Inventory data" refers to information relating to the quantity, storage location, expiration date, etc. of products in stores and warehouses.

[0903] "Sales data" refers to information relating to past sales history and sales.

[0904] An "IoT sensor" is a sensor device for collecting physical environmental data (e.g., temperature, humidity, location data, etc.).

[0905] "Environmental data" refers to data such as the temperature and humidity inside the store, and the location of shopping carts.

[0906] "Consumer data" refers to data such as consumer purchasing history, preferences, and feedback information.

[0907] A "generative AI model" is an artificial intelligence model that analyzes collected data and is generated to achieve a specific purpose.

[0908] "Discount information" is information such as the discount rate for a product.

[0909] A "customized recipe" is an individually optimized recipe generated based on the ingredients the consumer has on hand.

[0910] The "additional ingredient list" is a list showing the additional ingredients required and their quantities based on the generated recipe.

[0911] "Smart glasses or head-mounted display" refers to a wearable device for visually displaying information.

[0912] "Visually presented" means displaying information directly in the user's field of vision.

[0913] "Feedback data" refers to data such as ratings and post-purchase opinions provided by consumers.

[0914] This invention provides a system that allows retailers to improve the efficiency of inventory management in brick-and-mortar stores and provide appropriate discount information to consumers. The overall configuration and specific operation of the system are described below.

[0915] Overall system overview

[0916] The system mainly consists of a server, smart glasses or head-mounted displays (HMDs), and IoT sensors. The server collects and analyzes various data, generates the necessary information, and sends it to the smart devices. The smart devices then visually present the information to the user.

[0917] Data collection and analysis

[0918] The server accesses the retailer's database to periodically collect inventory and sales data. It also collects environmental data (temperature, humidity, location information) from IoT sensors installed in the store. It also accesses the consumer database to collect consumer purchasing history, preferences, and feedback information.

[0919] All collected data is passed to a generative AI model for analysis. The model identifies products that are close to expiring and generates discount offers for those products. For example, a 20% off coupon for tomatoes that are close to expiring will be set. The model also creates discount offers optimized for each consumer.

[0920] Discount information and feedback

[0921] Discount and stock information is visually presented to users using smart glasses or HMDs, such as Microsoft HoloLens or Google Glass. When users stand in front of a shelf, discount information for products is displayed on the device's transparent display.

[0922] It also generates recipes: users input the ingredients they have on hand, and the generative AI model generates the optimal recipe, calculating any additional ingredients needed and discount information, allowing consumers to shop efficiently.

[0923] After shopping, users can provide feedback through the app. The feedback data is sent to the server and reflected in the database. This will be used for future analysis and to provide discount information.

[0924] Specific examples

[0925] The server analyzes the store's inventory data and generates discount information such as a 20% discount on tomatoes with a best-by date of two days. This information is visually communicated to the user via smart glasses or an HMD. When the user inputs 300g of chicken breast and two cloves of garlic as ingredients on hand, the generative AI model suggests a recipe for "chicken breast and tomato pasta," and determines that "200g of pasta and two tablespoons of olive oil" are also required, generating discount information for this.

[0926] Prompt Sentence Examples

[0927] Consider a specific example of an AR application that assists in inventory management in a physical store. When approaching a shelf, the system displays discount information for products with an approaching expiration date in the field of view of smart glasses. Please explain in detail what kind of interface and notification method you could use.

[0928] In this way, the system of the present invention can improve the work efficiency of store staff and provide consumers with appropriate information in real time, thereby providing an efficient shopping experience.

[0929] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0930] Step 1:

[0931] The server accesses the retailer's database and collects inventory and sales data. This includes information such as product quantity, storage location, and expiration date. The inventory and sales data is periodically retrieved using an API. The input is the database query results, and the output is organized inventory and sales data stored in the server's memory.

[0932] Step 2:

[0933] The server collects environmental data from IoT sensors. Data such as temperature, humidity, and shopping cart location information is obtained from the IoT sensors via an API. The input is real-time data sent from the sensors, and the output is organized environmental data that is stored in the server's memory. Specifically, the server receives data sent from sensors in the store and converts it into an analyzable format.

[0934] Step 3:

[0935] The server accesses the consumer database to collect consumer purchasing history, preferences, and feedback information. The input is the query results from the consumer database, and the output is organized consumer data stored in the server's memory. Specifically, it obtains each consumer's past purchasing history and provided feedback in an analyzable format.

[0936] Step 4:

[0937] The server passes the collected inventory data, sales data, IoT sensor data, and consumer data to the generative AI model for analysis. All collected data is input, and the output is a list of products with upcoming expiration dates and their discount information. Specifically, the data is input into the AI ​​model, and discount information for specific products is generated as an analysis result. For example, a 20% off coupon is set for tomatoes with an expiration date in two days.

[0938] Step 5:

[0939] The server sends the generated discount information to each consumer's device. The input is the generated discount information, and the output is a notification sent to the consumer's device. Specifically, the discount coupon information is sent to the consumer's smartphone as an electronic notification.

[0940] Step 6:

[0941] The user opens the smartphone app and inputs the ingredients they have and the amount. The input is the ingredient data the user has, and the output is the ingredient data sent to the server. Specifically, the user enters the ingredient data into the app's input form and presses the send button, which sends the data to the server.

[0942] Step 7:

[0943] The server uses a generative AI model to generate customized recipes based on the received user data. The inputs are the user's ingredient data and inventory data, and the output is the generated recipe and a list of additional ingredients. Specifically, the ingredient data is input into the AI ​​model, which generates the optimal recipe and a list of required additional ingredients. For example, it proposes a recipe for "pasta with chicken breast and tomatoes" and determines that "200g of pasta and 2 tablespoons of olive oil" are required.

[0944] Step 8:

[0945] The server generates a shopping list of the necessary ingredients and assigns corresponding discount coupons. The input is a customized recipe and a list of additional ingredients, and the output is a shopping list with discount coupons. Specifically, the server sets discount information for the additional ingredients and generates the entire list.

[0946] Step 9:

[0947] The user can check the suggested recipe and additional ingredient list on their smartphone. The input is the recipe information sent from the server, and the output is the information displayed on the user's device. Specifically, the user opens the smartphone app to check the recipe and shopping list.

[0948] Step 10:

[0949] Users enter their post-shopping experience and product evaluation as feedback through a smartphone app. The input is the user's feedback data, and the output is feedback information sent to the server. Specifically, users enter their thoughts in the app's feedback form and press the send button.

[0950] Step 11:

[0951] The server analyzes the received feedback data and updates the database to reflect it in the next analysis and discount information provision. The input is the user's feedback data, and the output is an updated database. Specifically, the feedback information is input into the analysis model to improve the accuracy of future analysis algorithms.

[0952] The above processing steps enable efficient inventory management, appropriate discount information to consumers, and the creation of customized recipes.

[0953] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0954] Overall system configuration

[0955] This system collects and analyzes retailers' inventory data, sales data, IoT sensor data, and consumer data, presents discount information for products with approaching expiration dates, and generates customized recipes based on the ingredients the consumer has and the user's emotional data. It also collects actual consumer purchasing data and feedback, and optimizes the system based on that data.

[0956] Program processing

[0957] Data collection

[0958] 1. Server

[0959] Access the retailer's database to collect inventory data. This includes information such as product quantity, storage location, and expiration date. For example, this includes data such as "Tomatoes: 10 units in stock, expiration date in 2 days."

[0960] Sales data is also collected in the same way. Past sales history, prices, purchaser information, etc. are obtained. For example, data such as "Tomatoes: 60 units sold in the past 30 days, an average of 2 units per day" is collected.

[0961] IoT sensors are used to collect in-store environmental data (temperature, humidity, shopping cart location, etc.). Specifically, data is collected for "in-store temperature: 20°C, humidity: 50%."

[0962] Access consumer databases to collect consumer purchasing history, preferences, and feedback information, such as "Consumer A: Purchased tomatoes 5 times."

[0963] Data analysis

[0964] 2. Server

[0965] All collected data is passed to a generative AI model for analysis. Here, products with an approaching expiration date are identified and discount information for those products is generated. For example, if "five tomatoes with an expiration date in two days" are identified, a "20% off discount coupon for tomatoes" is created.

[0966] Delivery of bargain information

[0967] 3. Server

[0968] Based on the analysis results, the system generates bargain information tailored to each individual consumer and sends an electronic notification to their device. For example, a notification will be sent to "Consumer A" that "Tomatoes are 20% off, with the expiration date in two days."

[0969] 4. Terminal

[0970] A notification will appear on the consumer's smartphone, for example, "Tomatoes 20% off, expiration date in 2 days."

[0971] User input collection and sentiment data collection

[0972] 5. Users

[0973] Open the smartphone app and enter the ingredients you have and the quantities. For example, enter "300g chicken breast, 2 cloves of garlic" into the app.

[0974] The app uses a built-in emotion engine to recognize the user's emotions (e.g., happy, tired, etc.).

[0975] 6. Terminal

[0976] The input food ingredient data and emotion data are sent to the server. For example, the data "300g chicken breast, 2 cloves of garlic" and "Emotion: fun" are sent to the server.

[0977] Creating a customized recipe

[0978] 7. Server

[0979] Based on the received user data and emotion data, the generative AI model generates customized recipes. For example, it determines that "it's okay if the cooking time is long because the user seems to enjoy it," and suggests a recipe such as "chicken breast and tomato pasta."

[0980] It calculates the additional ingredients needed and their quantities and generates a shopping list. For example, the list might include "200g pasta, 2 tablespoons olive oil."

[0981] 8. Terminal

[0982] The suggested recipe and a list of additional ingredients are displayed to the user, for example, "Chicken breast and tomato pasta, plus 200g pasta and 2 tablespoons olive oil."

[0983] Shopping list and discount coupons provided

[0984] 9. Server

[0985] Add discount coupons to a list of required additional ingredients, for example, "Pasta 200g - 10% off discount coupon, Tomato - 20% off discount coupon".

[0986] 10. Terminal

[0987] A shopping list and discount coupons are displayed on the user's smartphone, allowing them to shop efficiently.

[0988] Purchasing data feedback

[0989] 11. Users

[0990] After shopping, users enter their purchases and their ratings through the app. For example, they can provide feedback such as, "I bought five tomatoes, and the chicken breast and tomato pasta was delicious."

[0991] 12. Terminal

[0992] Send feedback data to the server. For example, "I bought 5 tomatoes and the pasta was delicious."

[0993] 13. Server

[0994] We use your feedback to update our database, which can be reflected in our next analysis and offer.

[0995] Specific examples

[0996] server

[0997] Collect inventory data for tomatoes from a retailer and identify that five of them have a best-by date two days later. This will qualify for a discount, so set up a 20% off coupon for the tomatoes.

[0998] Terminal

[0999] A notification appears on the user's smartphone saying, "Tomatoes 20% off, expiration date in 2 days."

[1000] User

[1001] You log in to the app and enter the information you have, such as "300g chicken breast and 2 cloves of garlic," and the emotion engine recognizes that you are in a happy state.

[1002] server

[1003] The received data is analyzed and a recipe for "chicken breast and tomato pasta" is suggested, with the additional recommendation of "200g of pasta and 2 tablespoons of olive oil."

[1004] Terminal

[1005] It displays suggested recipes and ingredient lists to users and allows them to take advantage of discount coupons.

[1006] User

[1007] After shopping, customers provide feedback, which is received by the server and updated in the database.

[1008] In this way, the system of the present invention can also utilize user emotional data to optimize inventory management for retailers and the consumer purchasing experience.

[1009] The processing flow will be explained below.

[1010] Step 1:

[1011] server

[1012] Access the retailer's database and collect inventory data. Specifically, obtain information such as product quantity, storage location, expiration date, etc. For example, obtain data such as "Tomatoes: 10 units in stock, expiration date in 2 days."

[1013] Step 2:

[1014] server

[1015] Collect sales data, such as past sales history, prices, and buyer information. For example, obtain data such as "Tomatoes: 60 units sold in the past 30 days, an average of 2 units per day."

[1016] Step 3:

[1017] server

[1018] IoT sensors are used to collect environmental data. Specifically, sensor data such as the temperature, humidity, and location of shopping carts in the store is collected. For example, data such as "Store temperature: 20°C, humidity: 50%" is acquired.

[1019] Step 4:

[1020] server

[1021] Access consumer databases to collect consumer purchasing history, preferences, and feedback information. For example, obtain data such as "Consumer A: Purchased tomatoes five times in the past."

[1022] Step 5:

[1023] server

[1024] The collected inventory data, sales data, IoT sensor data, and consumer data are passed to a generative AI model for analysis. The analysis identifies products with an approaching expiration date. For example, "identify five tomatoes with an expiration date in two days."

[1025] Step 6:

[1026] server

[1027] Generate discount information for identified products that are close to their expiration date. For example, "Set a 20% discount coupon for tomatoes."

[1028] Step 7:

[1029] server

[1030] Customize the discount information generated for each consumer and prepare an electronic notification to send to each consumer. For example, send a notification to "Consumer A" that "Tomatoes are 20% off, expiration date is 2 days away."

[1031] Step 8:

[1032] Terminal

[1033] A notification will be displayed on the consumer's smartphone, for example, "Tomatoes 20% off, expiration date in 2 days."

[1034] Step 9:

[1035] User

[1036] Open the smartphone app and enter the ingredients you have and the quantities. For example, enter "300g chicken breast, 2 cloves of garlic."

[1037] Step 10:

[1038] Terminal

[1039] The app's emotion engine recognizes the user's emotions, for example, detecting emotions such as "fun" using the camera and microphone.

[1040] Step 11:

[1041] Terminal

[1042] The input food ingredient data and emotion data are sent to the server. For example, the data "300g chicken breast, 2 cloves of garlic" and "Emotion: happy" are sent to the server.

[1043] Step 12:

[1044] server

[1045] Based on the received user data and emotion data, the generative AI model generates customized recipes. For example, it determines that the user will enjoy cooking, so it may be okay to wait a long time and suggests a recipe for "chicken breast and tomato pasta."

[1046] Step 13:

[1047] server

[1048] Generate a shopping list by calculating additional ingredients and their quantities, for example, "200g pasta, 2 tablespoons olive oil"

[1049] Step 14:

[1050] Terminal

[1051] Presents the user with a customized recipe and a list of additional ingredients, such as "Chicken breast and tomato pasta, plus 200g pasta and 2 tablespoons olive oil."

[1052] Step 15:

[1053] server

[1054] Add discount coupons to a list of required additional ingredients, for example, "Pasta 200g - 10% off discount coupon, Tomato - 20% off discount coupon".

[1055] Step 16:

[1056] Terminal

[1057] It displays shopping lists and discount coupons on the user's smartphone, allowing them to shop efficiently.

[1058] Step 17:

[1059] User

[1060] After shopping, users enter their purchases and their ratings through the app. For example, they can provide feedback such as, "I bought five tomatoes, and the chicken breast and tomato pasta was delicious."

[1061] Step 18:

[1062] Terminal

[1063] Send feedback data to the server. For example, "I bought 5 tomatoes, and the chicken breast and tomato pasta was delicious."

[1064] Step 19:

[1065] server

[1066] We use the feedback we receive to update our database, which can be used to analyze data and provide deals to you next time.

[1067] In this way, by linking the server, terminal, and user, a system can be created that utilizes user emotional data and provides a more personalized purchasing experience.

[1068] Example 2

[1069] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1070] Modern retailers are faced with the complex task of managing a wide variety of products, making inventory management and expiration date management cumbersome. Consumers also struggle to find new recipes that make the most of the ingredients they already have. Providing recipes that match consumers' emotions and current desires is particularly challenging, posing many challenges for providing an optimal shopping experience.

[1071] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1072] In this invention, the server includes means for collecting inventory data, means for collecting sales data, means for collecting environmental data, means for collecting data such as consumer purchasing history and preferences, means for analyzing the inventory data, sales data, environmental data, and consumer data to identify products with upcoming expiration dates, means for generating discount information for the products with upcoming expiration dates, means for transmitting the discount information to each consumer, means for receiving data on ingredients owned by the consumer and generating customized recipes based thereon, means for generating an additional ingredient list and its discount information based on the recipe, means for providing the additional ingredient list and discount information to the consumer, means for collecting consumer feedback data and improving the analysis means based thereon, and means for collecting emotion data and suggesting optimal recipes to the consumer based thereon. This makes it possible to improve inventory management efficiency and provide optimal recipes and discount information to individual consumers.

[1073] "Inventory data" refers to information about inventory, such as product quantities, storage locations, and expiration dates.

[1074] "Sales data" refers to data including transaction records, prices, and purchaser information when a retailer sells a product.

[1075] "Environmental data" refers to information about the environment, such as temperature, humidity, and the location of shopping carts within a store, collected through IoT sensors.

[1076] "Consumer data" refers to information about consumer behavior and trends, such as consumer purchasing history, preferences, and feedback information.

[1077] "Best before" refers to the period during which food or consumable products can maintain their quality, after which the quality may deteriorate.

[1078] "Discount Information" refers to information about price discounts and coupons offered for specific products.

[1079] "Customized recipes" refer to cooking instructions that are individually generated based on the consumer's ingredients, preferences, and emotional data.

[1080] "Emotional data" refers to data that indicates a consumer's emotional state (e.g., happy, tired, etc.).

[1081] "Additional Ingredient List" means a list of additional ingredients and their quantities required to prepare a customized recipe.

[1082] "Feedback data" refers to data containing reactions and impressions about products and services, such as ratings and opinions provided by consumers.

[1083] "Generative AI model" refers to an artificial intelligence model that analyzes collected data and performs a specific task (e.g., generating a recipe or presenting a discount coupon).

[1084] This system collects and analyzes inventory data, sales data, environmental data, and consumer data from retailers, generates discount information for products approaching their expiration date, and provides customized recipes based on the consumer's ingredients and emotional data. The system consists of the following main components:

[1085] 1. Data Collection

[1086] server:

[1087] The server accesses the retailer's database to collect inventory data. Specific techniques include using SQL queries. For example, data is retrieved using "SELECT FROM inventory WHERE expiration_date < '2023-10-20'". Sales data is also collected, including transaction records, prices, and customer information. Furthermore, IoT sensors are used to collect environmental data such as in-store temperature, humidity, and shopping cart location. Finally, a consumer database is accessed to retrieve consumer purchasing history, preferences, and feedback information.

[1088] 2. Data Analysis

[1089] server:

[1090] The server inputs the collected inventory data, sales data, environmental data, and consumer data into a generative AI model for analysis. The generative AI model uses libraries such as TensorFlow and PyTorch. Here, it identifies products with an approaching expiration date and generates discount information for those products. For example, it creates a 20% discount coupon for "five tomatoes with an expiration date in two days."

[1091] 3. Delivery of bargain information

[1092] server:

[1093] Based on the analysis results, the system generates bargain information tailored to each individual consumer and sends an electronic notification to each consumer's device. For example, the notification might read, "Consumer A receives 20% off tomatoes with a best-by date in two days."

[1094] Device:

[1095] The device displays a notification on the consumer's smartphone, for example, "Tomatoes 20% off, expiration date in 2 days."

[1096] 4. Collecting user input and emotion data

[1097] User:

[1098] The user opens the smartphone app and inputs the ingredients they have and the amounts. Specifically, they input "300g chicken breast, 2 cloves of garlic" into the app. The app also uses its built-in emotion engine to recognize the user's emotions.

[1099] Device:

[1100] The device sends the input food ingredient data and emotion data to the server. For example, data such as "300g chicken breast, 2 cloves of garlic" and "emotion: fun" are sent to the server.

[1101] 5. Creating a customized recipe

[1102] server:

[1103] The server uses the received user data and emotional data to generate a customized recipe using a generative AI model. For example, it determines that "it seems like the user will enjoy cooking it, so it's okay if it takes a long time to cook," and suggests a recipe such as "chicken breast and tomato pasta." It also calculates the additional ingredients needed and their quantities, and generates a shopping list. For example, the list might include "200g of pasta and 2 tablespoons of olive oil."

[1104] Device:

[1105] The device will then display a suggested recipe and a list of additional ingredients to the user, such as "Chicken breast and tomato pasta, plus 200g pasta and 2 tablespoons olive oil."

[1106] 6. Providing shopping lists and discount coupons

[1107] server:

[1108] The server will add discount coupons to the list of required additional ingredients, for example "Pasta 200g - 10% off discount coupon, Tomato - 20% off discount coupon".

[1109] Device:

[1110] The device displays a shopping list and discount coupons on the user's smartphone, allowing them to shop efficiently.

[1111] 7. Feedback of purchasing data

[1112] User:

[1113] After shopping, users can enter their purchases and their ratings through the app, for example, "I bought five tomatoes, and the chicken breast and tomato pasta was delicious."

[1114] Device:

[1115] The device sends feedback data to the server, for example, "I bought five tomatoes and the pasta was delicious."

[1116] server:

[1117] The server can update the database based on the feedback and reflect it in the next analysis or offer of bargains.

[1118] This allows the system to utilize user emotion data to improve the efficiency of inventory management for retailers and optimize the consumer purchasing experience. For example, the following prompt can be used: "User emotion: Fun, Ingredients on hand: 300g chicken breast, 2 cloves garlic."

[1119] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1120] Step 1: Data collection

[1121] Server: The server accesses the retailer's database to collect inventory data. This data includes product quantities, storage locations, expiration dates, and so on. For example, it retrieves data using an SQL query like "SELECT FROM inventory WHERE expiration_date < '2023-10-20'". Sales data includes transaction records, prices, and purchaser information, and is collected in a similar manner. Furthermore, it retrieves environmental data such as in-store temperature, humidity, and shopping cart location through IoT sensors. An example of environmental data is "store temperature: 20°C, humidity: 50%". Finally, it retrieves consumer purchasing history, preferences, and feedback information from the consumer database. These data are collected as input and stored in the database as output.

[1122] Step 2: Data analysis

[1123] Server: Collected inventory data, sales data, environmental data, and consumer data are input into the generative AI model and data analysis is performed. For example, a generative AI model written in Python uses libraries such as TensorFlow and PyTorch. Based on the input data, products with an approaching expiration date are identified. As a result of this analysis, for example, if "five tomatoes with an expiration date in two days" are identified, a "20% off discount coupon for tomatoes" is created. Data input is information from various databases, and discount information is obtained as output.

[1124] Step 3: Generate and distribute deals

[1125] Server: Based on the analysis results, it generates bargain information suitable for each individual consumer and sends this information to each consumer's device in an electronic notification. For example, it sends a notification to consumer A offering tomatoes at 20% off with the expiration date in two days. The input is the analysis results and individual consumer information, and the output is the delivery of an electronic notification.

[1126] Terminal: The terminal displays a notification on the consumer's smartphone. For example, it displays a notification saying, "Tomatoes are 20% off, expiration date is in 2 days." The input is the notification information from the server, and the output is the display on the smartphone screen.

[1127] Step 4: Collecting user input and emotion data

[1128] User: The user opens the smartphone app and inputs the ingredients they have and the quantities. For example, they input "300g chicken breast, 2 cloves of garlic" into the app. The app also uses its built-in emotion engine to recognize the user's emotion data. The input is the user's actions, and the output is the input data.

[1129] Terminal: The terminal sends the input food ingredient data and emotion data to the server. For example, the data "300g chicken breast, 2 cloves of garlic" and "Emotion: fun" are sent to the server. The input is the data entered by the user, and the output is the data sent to the server.

[1130] Step 5: Generate a customized recipe

[1131] Server: The server uses the generative AI model to generate a customized recipe based on the received user data and emotional data. For example, it determines that "it's okay if the cooking time is long because the user seems to enjoy it," and suggests a recipe such as "chicken breast and tomato pasta." It also calculates the additional ingredients needed and their quantities, and generates a shopping list. For example, the list might include "200g pasta and 2 tablespoons olive oil." The input is user data and emotional data, and the output is a customized recipe and a shopping list.

[1132] Terminal: The terminal displays the suggested recipe and a list of additional ingredients to the user. For example, "Chicken breast and tomato pasta, plus 200g of pasta and 2 tablespoons of olive oil." The input is the recipe information from the server, and the output is the display on the smartphone screen.

[1133] Step 6: Provide a shopping list and discount coupons

[1134] Server: The server adds discount coupons to a list of required additional ingredients. For example, it generates "Pasta 200g - 10% off discount coupon, Tomato - 20% off discount coupon". The input is a shopping list, and the output is a list with discount coupons.

[1135] Terminal: The terminal displays a shopping list and discount coupons on the user's smartphone, allowing for efficient shopping. The input is discount coupon information from the server, and the output is the display on the smartphone screen.

[1136] Step 7: Feedback on purchasing data

[1137] User: After shopping, the user enters the items they actually purchased and their evaluation through the app. For example, they provide feedback such as, "I bought five tomatoes, and the chicken breast and tomato pasta was delicious." The input is the evaluation after the purchase, and the output is the feedback data.

[1138] Terminal: The terminal sends feedback data to the server. For example, the terminal sends data such as "I bought five tomatoes and the pasta was delicious." The input is the user's feedback data, and the output is the data sent to the server.

[1139] Server: The server updates the database based on the feedback and reflects it in the next analysis and the provision of bargain information. The input is the feedback data, and the output is the updated database information.

[1140] (Application example 2)

[1141] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1142] In today's retail industry, there is a demand for more efficient inventory management and improved consumer satisfaction. Unsold and discarded products approaching their expiration date present significant challenges for retailers. There is also a need to improve the purchasing experience through services such as personalized recommendations and recipe provision. However, simultaneously addressing these challenges requires the collection and analysis of various data and real-time notifications to consumers, which existing systems are not yet able to adequately address. Further advances are needed, particularly in real-time notifications utilizing the latest technologies, including smart glasses, and the provision of customized recipes that take emotional data into account.

[1143] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1144] In this invention, the server includes: means for collecting inventory data of retailers; means for collecting sales data; means for collecting environmental data using IoT sensors; means for collecting data such as consumer purchasing history and preferences; means for analyzing the inventory data, sales data, IoT sensor data, and consumer data to identify products with upcoming expiration dates; means for generating discount information for the products with upcoming expiration dates; means for transmitting the discount information to each consumer; means for receiving data on ingredients owned by the consumer and generating customized recipes based thereon; means for generating an additional ingredient list and its discount information based on the recipes; means for providing the additional ingredient list and the discount information to the consumer; means for collecting consumer feedback data and improving the analysis means based thereon; means for notifying discount information for products with upcoming expiration dates in real time using smart glasses; and means for collecting consumer emotion data and taking the emotion data into consideration when generating the recipes. This improves the accuracy of inventory management and enables personalized suggestions and recipes to be provided to consumers in real time, thereby improving the purchasing experience.

[1145] "Inventory data" is a collection of information such as the quantity, type, expiration date, and storage location of products held by a retailer.

[1146] "Sales data" is a collection of data including the history of past sales of products, prices, purchaser information, and so on.

[1147] "IoT sensor data" refers to environmental data such as temperature, humidity, and shopping cart location collected using IoT sensors.

[1148] "Consumer data" is a collection of information such as a consumer's purchasing history, preferences, and feedback.

[1149] "Analysis means" refers to a technical means for analyzing collected data and identifying products that are close to their expiration date.

[1150] "Discount information" refers to information about price reductions offered for products that are close to their expiration date.

[1151] "Notification means" refers to a technical means for sending discount information and recipe information to each consumer.

[1152] A "customized recipe" is a personalized cooking procedure generated based on the consumer's ingredient and emotional data.

[1153] The "additional ingredient list" is a list showing the additional ingredients and their quantities required for cooking based on the customized recipe.

[1154] "Smart glasses" are eyeglass-type devices that have the ability to display information in real time.

[1155] "Emotional data" refers to information used to recognize a consumer's emotional state and analyze that data.

[1156] "Feedback data" refers to data such as post-purchase ratings and impressions provided by consumers.

[1157] Overall system overview

[1158] This invention is a system that collects and analyzes retailers' inventory data, sales data, IoT sensor data, and consumer data, presents discount information for products approaching their expiration date, and generates customized recipes based on the ingredients and emotional data of consumers. It also collects actual consumer purchasing data and feedback, and optimizes the system based on that data.

[1159] Hardware and software used

[1160] The main hardware used is as follows:

[1161] Smart glasses (e.g., general smart wearable devices)

[1162] IoT sensors (e.g. temperature sensors, humidity sensors)

[1163] Server (analyzes and stores data)

[1164] The main software used is as follows:

[1165] Generative AI models (e.g., GPT-4)

[1166] Database management system (e.g. MySQL)

[1167] Emotion Recognition Engine

[1168] Detailed process description

[1169] The server performs the following steps:

[1170] 1. Data Collection

[1171] The server accesses the retailer's database and collects inventory data, sales data, IoT sensor data, and consumer data. For example, inventory data includes information such as product quantity, storage location, and expiration date. IoT sensors are also used to collect environmental data such as temperature, humidity, and shopping cart location within the store. Consumer data includes purchasing history, preferences, and feedback information.

[1172] 2. Data Analysis

[1173] The collected data is analyzed by a generative AI model (e.g., GPT-4). As a result of the analysis, products with an approaching expiration date are identified, and discount information for those products is generated. For example, if there are "five tomatoes with an expiration date in two days," a "20% off discount coupon for tomatoes" is generated.

[1174] 3. Notification function

[1175] The server sends the generated discount information to the consumer's smart glasses in real time. For example, a notification such as "Tomatoes 20% off, expiration date in 2 days" is displayed. This information is displayed on the smart glasses' display, allowing the consumer to understand it immediately.

[1176] 4. Collecting User Input

[1177] The user inputs the information about the ingredients they have through the smart glasses, and the emotion engine collects emotional data. For example, if the user inputs "300g chicken breast, 2 cloves of garlic," the emotion recognition engine will recognize the user's emotion as "fun." This data is then sent to the server.

[1178] 5. Creating a customized recipe

[1179] The server uses a generative AI model (e.g., GPT-4) to generate a customized recipe based on the received user's ingredient and emotion data. For example, a recipe such as "chicken breast and tomato pasta" is suggested. Additionally, additional ingredients and their amounts are calculated, resulting in a list of "200g pasta and 2 tablespoons olive oil."

[1180] 6. Providing shopping lists and discount coupons

[1181] The generated recipe, additional ingredients list, and discount coupons are displayed on the smart glasses, allowing users to shop efficiently.

[1182] Examples of concrete examples and prompts

[1183] Examples:

[1184] A user is shopping in a physical store while wearing smart glasses. Data collected by the server includes inventory information for tomatoes (10 in stock, expiration date in 2 days). The user inputs the ingredients they have (300g chicken breast, 2 cloves of garlic) and recognizes that their emotion is "happy." Based on this, the server suggests "pasta with chicken breast and tomatoes," lists "200g pasta, 2 tablespoons olive oil" as additional ingredients, and provides a discount coupon.

[1185] Example prompt sentence:

[1186] prompt:

[1187] Analyze the following data and suggest suitable recipes for the user.

[1188] Stock Data: Tomatoes - 10 in stock, expiration date in 2 days

[1189] Emotional data: Fun

[1190] Ingredients I have: 300g chicken breast, 2 cloves of garlic

[1191] Required steps:

[1192] 1. Identify products that are nearing their expiration date and generate discount information.

[1193] 2. Suggest recipes that take into account the ingredients and emotional data of the user.

[1194] 3. Make a list of any additional ingredients you need.

[1195] Example output: Chicken breast and tomato pasta, 200g extra pasta, 2 tablespoons olive oil

[1196] This invention improves the accuracy of inventory management and makes it possible to provide individual suggestions and recipes to consumers in real time, thereby improving the purchasing experience.

[1197] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1198] Step 1:

[1199] The server accesses the retailer's database and collects inventory data. Specifically, it obtains information such as product quantity, expiration date, and storage location. For example, it collects data such as "Tomatoes: 10 in stock, expiration date 2 days away."

[1200] Input: Inventory data from the database

[1201] Output: Collected inventory data

[1202] What it does: Retrieves inventory information from a database using an SQL query.

[1203] Step 2:

[1204] The server collects sales data, including past sales history, prices, and buyer information. For example, data such as "Tomatoes: 60 units sold in the past 30 days, an average of 2 units per day" is collected.

[1205] Input: Sales data from the database

[1206] Output: Collected sales data

[1207] What it does: Retrieves sales information from a database using an SQL query.

[1208] Step 3:

[1209] The server uses IoT sensors to collect environmental data, such as the temperature and humidity inside the store and the location of shopping carts. For example, it obtains data such as "Store temperature: 20°C, humidity: 50%."

[1210] Input: Environmental data from IoT sensors

[1211] Output: Collected environmental data

[1212] Specific operation: Call the IoT sensor's data collection API to obtain environmental information.

[1213] Step 4:

[1214] The server collects data such as consumer purchasing history and preferences. For example, it collects information such as "Consumer A: purchased tomatoes 5 times."

[1215] Input: Consumer data from a database

[1216] Output: Collected consumer data

[1217] What it does: Retrieves consumer information using an SQL query.

[1218] Step 5:

[1219] The server analyzes the collected inventory data, sales data, IoT sensor data, and consumer data to identify products with an approaching expiration date. For example, it identifies "five tomatoes with an expiration date in two days."

[1220] Input: Inventory data, sales data, IoT sensor data, consumer data

[1221] Output: Identified products with an approaching expiration date

[1222] Specific operation: Using a generative AI model, data is analyzed and target products are identified.

[1223] Step 6:

[1224] The server generates discount information for products that are close to their expiration date. For example, it generates a "20% off discount coupon for tomatoes."

[1225] Input: Identified products with an approaching expiration date

[1226] Output: Generated discount coupon

[1227] Specific operation: Executes the discount information generation algorithm to create a coupon.

[1228] Step 7:

[1229] The server sends the generated discount information to the consumer, who then sends a notification to the smart glasses, displaying information such as "20% off tomatoes, expiration date in 2 days."

[1230] Input: Generated discount coupon

[1231] Output: Notification sent to smart glasses

[1232] What it does: Sends information to consumer devices using the Notification API.

[1233] Step 8:

[1234] The user inputs the information about the ingredients they have through the smart glasses, for example, "300g chicken breast, 2 cloves of garlic."

[1235] Input: User's ingredient information

[1236] Output: Input food ingredients data

[1237] Specific operation: Collect food ingredient data through the input interface of smart glasses.

[1238] Step 9:

[1239] The emotion engine of the device recognizes the user's emotion data and sends it to the server. For example, the user's emotion is recognized as "happy."

[1240] Input: User's emotional state

[1241] Output: Recognized emotion data

[1242] Specific operation: Analyzes input data using an emotion recognition engine and recognizes the emotional state.

[1243] Step 10:

[1244] The server generates a customized recipe based on the received user's ingredients and emotion data. For example, it might suggest "chicken breast and tomato pasta" and add "200g pasta, 2 tablespoons olive oil."

[1245] Input: food data, emotion data

[1246] Output: Customized recipe, additional ingredients list

[1247] How it works: Uses a generative AI model to generate a customized recipe and calculate any additional ingredients needed.

[1248] Step 11:

[1249] The server displays the generated recipe, additional ingredients list, and discount coupons on the smart glasses, allowing users to shop efficiently.

[1250] Input: Customized recipes, additional ingredient lists, discount coupons

[1251] Output: Information displayed on the smart glasses

[1252] Specific operation: Send information to the smart glasses through the display interface.

[1253] Step 12:

[1254] After shopping, the user provides feedback through the smart glasses, for example, "I bought five tomatoes and the pasta was delicious."

[1255] Input: User feedback data

[1256] Output: Input feedback data

[1257] Specific operation: Collect feedback data through the input interface of smart glasses.

[1258] Step 13:

[1259] The server analyzes the collected feedback data and updates the database to reflect it in the next analysis and offer of deals.

[1260] Input: Feedback data

[1261] Output: Updated database

[1262] Specific behavior: Parse the feedback data and execute SQL queries to update the database.

[1263] 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.

[1264] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1265] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1266] [Third embodiment]

[1267] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1268] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1269] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[1270] 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.

[1271] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1272] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1273] 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.

[1274] 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.

[1275] 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 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.

[1276] 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.

[1277] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1278] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1279] Overall system configuration

[1280] This invention is a system that collects and analyzes retailers' inventory data, sales data, IoT sensor data, and consumer data, presents discount information for products with approaching expiration dates, and generates customized recipes based on the ingredients the consumer has on hand.

[1281] Program processing

[1282] Data collection

[1283] 1. Server

[1284] Accessing retailer databases to periodically collect inventory and sales data, including product quantities, storage locations, expiration dates, and past sales history.

[1285] In addition, IoT sensors will be used to collect environmental data within the store (temperature, humidity, shopping cart position, etc.).

[1286] Access consumer databases to collect consumer purchasing history, preferences, and feedback information.

[1287] Data analysis

[1288] 2. Server

[1289] All collected data is passed to a generative AI model for analysis, which identifies products with an approaching expiration date and generates discount information for those products.

[1290] For example, if you have tomatoes in stock with a best-by date of two days, set a 20% discount coupon for tomatoes.

[1291] It also takes into account the consumer's purchasing history and preferences to create individually optimized discount offers.

[1292] Delivery of bargain information

[1293] 3. Server

[1294] The generated discount information is customized for each consumer, and an electronic notification is sent to each consumer's terminal.

[1295] For example, a notification may be sent to the consumer saying, "Tomatoes are 20% off, expiration date is in 2 days."

[1296] 4. Terminal

[1297] App notifications are displayed on the user's smartphone, visually presenting bargain information to consumers.

[1298] Collecting User Input

[1299] 5. Users

[1300] Open the smartphone app and enter the ingredients you have and the amounts. For example, enter "300g chicken breast, 2 cloves of garlic."

[1301] 6. Terminal

[1302] The entered information is sent to the server.

[1303] Creating a customized recipe

[1304] 7. Server

[1305] Based on the received user data, the generative AI model generates customized recipes, such as suggesting "pasta with chicken breast and tomatoes."

[1306] Calculates the additional ingredients needed and their quantities and generates a shopping list. For example, it determines that you need 200g of pasta and 2 tablespoons of olive oil.

[1307] 8. Terminal

[1308] Display a suggested recipe and additional ingredient list to the user.

[1309] Shopping list and discount coupons provided

[1310] 9. Server

[1311] Generate a shopping list of the ingredients you need and add corresponding discount coupons, for example, "Pasta 200g - Discount coupon 10% off", "Tomato - Discount coupon 20% off".

[1312] 10. Terminal

[1313] A shopping list and discount coupons are displayed on the user's smartphone, allowing them to shop efficiently.

[1314] Purchasing data feedback

[1315] 11. Users

[1316] After shopping, users enter their purchases and their ratings through the app. For example, "I bought five tomatoes, and the chicken breast and tomato pasta was delicious."

[1317] 12. Terminal

[1318] Send the feedback data to the server.

[1319] 13. Server

[1320] Based on your feedback, we will update our database and reflect it in our next analysis and offer of bargains.

[1321] Specific examples

[1322] server

[1323] Collect inventory data for tomatoes from a retailer and identify that five of them have a best-by date two days later. This will qualify for a discount, so set up a 20% off coupon for the tomatoes.

[1324] Terminal

[1325] A notification appears on the user's smartphone saying, "Tomatoes 20% off, expiration date in 2 days."

[1326] User

[1327] Log in to the app and enter the information you have: 300g chicken breast, 2 cloves of garlic.

[1328] server

[1329] The received data is analyzed and a recipe for "chicken breast and tomato pasta" is suggested, with the additional recommendation of "200g of pasta and 2 tablespoons of olive oil."

[1330] Terminal

[1331] It displays suggested recipes and ingredient lists to users and allows them to apply discount coupons.

[1332] User

[1333] After shopping, customers provide feedback, which is received by the server and updated in the database.

[1334] In this way, the system of the present invention allows retailers to efficiently improve inventory management and food waste, and also allows consumers to shop more efficiently.

[1335] The processing flow will be explained below.

[1336] Step 1:

[1337] server

[1338] Access the retailer's database and collect inventory data. Here, obtain information such as product quantity, storage location, and expiration date. For example, obtain data such as "Tomatoes: 10 units in stock, expiration date in 2 days."

[1339] Step 2:

[1340] server

[1341] Collect sales data. Obtain data including past sales history, prices, and buyer information. For example, collect data such as "Tomatoes: 60 units sold in the past 30 days, an average of 2 units per day."

[1342] Step 3:

[1343] server

[1344] IoT sensors are used to collect environmental data. For example, sensor data such as the temperature, humidity, and location of shopping carts in the store is acquired. Specifically, data such as "Store temperature: 20°C, humidity: 50%" is collected.

[1345] Step 4:

[1346] server

[1347] Collect data on consumer purchasing history and preferences. For example, obtain a list of products purchased by Consumer A in the past and their preferences, such as "Consumer A: has purchased tomatoes five times."

[1348] Step 5:

[1349] server

[1350] It analyzes collected inventory data, sales data, IoT sensor data, and consumer data, and uses a generative AI model to identify products that are approaching their expiration date. For example, it detects five tomatoes that have two days left until their expiration date.

[1351] Step 6:

[1352] server

[1353] Based on the analyzed data, discount information is generated for products that are close to their expiration date. For example, a 20% discount coupon is created for tomatoes.

[1354] Step 7:

[1355] server

[1356] The generated discount information is sent to individual consumers. For example, a notification of 20% off tomatoes with an expiration date of 2 days is sent to "Consumer A."

[1357] Step 8:

[1358] Terminal

[1359] Display a notification on the user's smartphone. For example, a notification like "Tomatoes 20% off, expiration date in 2 days" will be displayed on the smartphone.

[1360] Step 9:

[1361] User

[1362] Open the app and enter the ingredients you have and the quantities. For example, enter "300g chicken breast, 2 cloves of garlic" into the app.

[1363] Step 10:

[1364] Terminal

[1365] The entered data is sent to the server. For example, the data "300g chicken breast, 2 cloves of garlic" is sent to the server.

[1366] Step 11:

[1367] server

[1368] Based on the received user data, the generative AI model generates customized recipes, such as suggesting a recipe for "pasta with chicken breast and tomatoes."

[1369] Step 12:

[1370] server

[1371] Calculate the amount of additional ingredients needed for the recipe. For example, include "200g pasta, 2 tablespoons olive oil" in your list.

[1372] Step 13:

[1373] Terminal

[1374] Presents the user with a customized recipe and a list of additional ingredients, for example, "Chicken breast and tomato pasta, plus 200g pasta and 2 tablespoons olive oil."

[1375] Step 14:

[1376] server

[1377] Add discount coupons to a list of required additional ingredients, for example, "Pasta 200g - 10% off discount coupon, Tomato - 20% off discount coupon".

[1378] Step 15:

[1379] Terminal

[1380] It displays shopping lists and discount coupons on the user's smartphone, allowing them to shop efficiently.

[1381] Step 16:

[1382] User

[1383] After shopping, users enter their purchases and their ratings through the app. For example, they can provide feedback such as, "I bought five tomatoes, and the chicken breast and tomato pasta was delicious."

[1384] Step 17:

[1385] Terminal

[1386] The provided feedback data is sent to the server. For example, data such as "I bought 5 tomatoes and the pasta was delicious" is sent to the server.

[1387] Step 18:

[1388] server

[1389] We use the feedback we receive to update our database so that it can be reflected in our next analysis and offer.

[1390] Example 1

[1391] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1392] Traditional retail inventory management has been problematic due to the difficulty of efficient inventory management and expiration date management, resulting in food waste. It has also been difficult to provide appropriate discount information to consumers, resulting in insufficient motivation to purchase. Furthermore, it has been difficult to provide customized recipe suggestions that take into account the ingredients that consumers have on hand, making it difficult to provide an efficient shopping experience.

[1393] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1394] In this invention, the server includes: means for collecting inventory information from retailers; means for collecting sales information; means for collecting environmental information using IoT technology; means for collecting information on consumer purchasing behavior and preferences; means for analyzing the inventory information, sales information, IoT information, and consumer information to identify products with upcoming expiration dates; means for generating discount information for the products with upcoming expiration dates; means for transmitting the discount information to each consumer; means for receiving information on ingredients owned by the consumer and generating customized recipes based thereon; means for generating an additional ingredient list and its discount information based on the recipe; means for providing the additional ingredient list and discount information to consumers; and means for collecting consumer evaluation information and improving the analysis means based thereon. This enables more efficient inventory management and reduced food waste, and enables an efficient shopping experience by providing consumers with optimized discount information and customized recipe suggestions.

[1395] "Inventory information" refers to data about the quantity, storage location, expiration date, etc. of products held by a retailer.

[1396] "Sales information" refers to data obtained by retailers regarding the past sales history and sales amount of each product.

[1397] "IoT technology" is a technology that allows physical devices and sensors to communicate with each other via the Internet and collect and exchange data.

[1398] "Environmental information" refers to data about the environment collected using IoT technology, such as the temperature and humidity inside the store and the location of shopping carts.

[1399] "Consumer information" refers to data relating to consumer purchasing behavior, preferences, evaluations, etc.

[1400] "Analysis means" refers to the function of analyzing data using collected inventory information, sales information, IoT information, and consumer information.

[1401] "Discount information" is data relating to discount coupons and discount rates that are applied to products that are close to their expiration date.

[1402] An "electronic notification" is a notification message sent from a server to a consumer's terminal.

[1403] "Customized recipes" are personalized recipes that are suggested based on the ingredients a consumer has on hand.

[1404] The "additional ingredient list" is data listing additional ingredients and their amounts required to realize a customized recipe.

[1405] "Evaluation information" refers to data regarding evaluations and feedback that consumers have given regarding products or services they have actually purchased.

[1406] This invention builds a system that allows for efficient inventory management, reduces food waste, and provides consumers with an optimal shopping experience through collaboration between servers, terminals, and users.

[1407] Hardware and software used

[1408] The server utilizes a high-performance cloud infrastructure, using MySQL for database management and TensorFlow for data analysis. The MQTT protocol is also used to collect data from IoT sensors. A mobile app is installed on user devices, and the app communicates with the server via a Node.js server and REST API.

[1409] Data collection

[1410] The server periodically queries the retailer's database to obtain inventory and sales information. This data includes product quantities, storage locations, expiration dates, and past sales history. It also collects environmental information such as temperature, humidity, and shopping cart locations from IoT sensors in the store. Consumer information, such as purchasing behavior, preferences, and feedback, is obtained through the consumer database.

[1411] Data analysis

[1412] The server passes all collected data to a generative AI model for data analysis. This model identifies products that are close to their expiration date and generates discount information for them. For example, if there are tomatoes in stock that are close to their expiration date, the generative AI model will set a 20% discount coupon for the tomatoes. The model also creates individually optimized discount offers based on the consumer's purchasing history and preferences.

[1413] Delivery of bargain information

[1414] The server customizes the generated discount information for each consumer and sends an electronic notification to each consumer's device. For example, a notification may be sent saying, "Tomatoes 20% off, expiration date in 2 days." The device receives this notification and displays it as an app notification on the user's smartphone.

[1415] Collecting User Input

[1416] The user opens the smartphone app and inputs the ingredients they have and their quantities. For example, they might input "300g chicken breast, 2 cloves of garlic." The device then sends this information in JSON format to the server.

[1417] Creating a customized recipe

[1418] The server uses the received user data to generate a customized recipe using a generative AI model. For example, it suggests a recipe for "pasta with chicken breast and tomatoes." It also calculates the additional ingredients needed and their quantities, and generates a shopping list. For example, it determines that "200g of pasta and 2 tablespoons of olive oil" are needed.

[1419] Shopping list and discount coupons provided

[1420] The server generates a shopping list of the necessary ingredients and provides corresponding discount coupons. For example, "Pasta 200g - Discount coupon 10% off" or "Tomato - Discount coupon 20% off." The device receives this information and displays the shopping list and discount coupons on the user's smartphone.

[1421] Purchasing data feedback

[1422] After shopping, users enter the items they actually purchased and their ratings through the app. For example, they might enter, "I bought five tomatoes, and the chicken breast and tomato pasta was delicious." The device receives this feedback data and sends it to the server. The server updates the database based on the feedback, and reflects it in future analyses and discount information.

[1423] This allows retailers to efficiently improve inventory management and food waste, while providing consumers with an optimal shopping experience.

[1424] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1425] Step 1:

[1426] Data collection

[1427] The server accesses the retailer's database and periodically retrieves inventory and sales information.

[1428] Specific actions

[1429] The server queries a MySQL database to retrieve product quantities, storage locations, expiration dates, and past sales history.

[1430] Input: The server uses the connection information to the database and the query as input.

[1431] Output: The server stores the acquired inventory and sales information in its internal data storage.

[1432] Step 2:

[1433] Collecting environmental data using IoT sensors

[1434] The server collects environmental information such as temperature, humidity, and shopping cart location inside the store from IoT sensors.

[1435] Specific actions

[1436] The server uses the MQTT protocol to collect data from each sensor.

[1437] Input: Real-time data sent from IoT sensors.

[1438] Output: The server stores the collected environmental information in its internal data storage.

[1439] Step 3:

[1440] Collection of Consumer Information

[1441] The server accesses a consumer database to obtain purchasing behavior, preferences, and feedback information.

[1442] Specific actions

[1443] The server sends a request to the consumer database via an API to retrieve the required information.

[1444] Input: A request to a consumer database via an API.

[1445] Output: Store consumer purchase history, preferences, and feedback information in the server's internal data storage.

[1446] Step 4:

[1447] Data analysis

[1448] The server passes the collected inventory information, sales information, IoT information, and consumer information to the generative AI model for data analysis.

[1449] Specific actions

[1450] The server uses TensorFlow to identify products that are nearing their expiration date and generate discount offers.

[1451] Input: Inventory information, sales information, IoT information, consumer information.

[1452] Output: Products with upcoming expiration dates and discount information.

[1453] Step 5:

[1454] Discount information generation and notification

[1455] The server customizes the discount information for each consumer and sends an electronic notification to the consumer's terminal.

[1456] Specific actions

[1457] Use a Node.js server to send notifications using FCM (Firebase Cloud Messaging).

[1458] Input: Generated discount information, consumer's device information.

[1459] Output: A customized discount notification for each consumer.

[1460] Step 6:

[1461] Consumer food ingredient data entry

[1462] Users input the information about ingredients they have into the smartphone app.

[1463] Specific actions

[1464] The user enters the names and quantities of ingredients into the app's input form and clicks the submit button.

[1465] Input: Ingredient information entered by the consumer.

[1466] Output: The entered data is sent from the terminal to the server.

[1467] Step 7:

[1468] Creating a customized recipe

[1469] The server generates a customized recipe using a generative AI model based on the received user data.

[1470] Specific actions

[1471] The server uses a Python script to generate recipes that take into account the ingredients the user has and create additional ingredient lists.

[1472] Input: Ingredient information entered by the consumer.

[1473] Output: A customized recipe with an additional ingredients list.

[1474] Step 8:

[1475] View recipes and ingredient lists

[1476] The device displays the suggested recipe and a list of additional ingredients to the user.

[1477] Specific actions

[1478] The smartphone app displays the recipe information and ingredient list received from the server on its interface.

[1479] Input: Recipe information received from the server and additional ingredient list.

[1480] Output: Detailed information displayed for the user to see.

[1481] Step 9:

[1482] Shopping list and discount coupons provided

[1483] The server generates a shopping list of the necessary ingredients and provides corresponding discount coupons.

[1484] Specific actions

[1485] The server generates discount coupons for additional ingredients calculated by the generative AI model and adds them to the shopping list.

[1486] Input: Customized recipe information, additional ingredient information.

[1487] Output: Additional ingredients list and corresponding discount coupon.

[1488] Step 10:

[1489] View shopping list and coupons

[1490] The terminal displays a shopping list and discount coupons on the user's smartphone.

[1491] Specific actions

[1492] The app displays the shopping list and discount coupons received from the server on the screen for easy access by the user.

[1493] Input: Shopping list and discount coupons received from the server.

[1494] Output: Detailed information displayed for the user to see.

[1495] Step 11:

[1496] Collecting consumer evaluation data

[1497] After shopping, users enter the items they purchased and their ratings into the app.

[1498] Specific actions

[1499] Users fill out the evaluation form through the app interface and click the submit button.

[1500] Input: Rating information entered by the consumer.

[1501] Output: The entered rating information is sent from the terminal to the server.

[1502] Step 12:

[1503] Submitting evaluation data

[1504] The terminal transmits the collected evaluation data to the server.

[1505] Specific actions

[1506] The app sends rating data in JSON format to the server, communicating via HTTP POST requests.

[1507] Input: Rating information entered by the consumer.

[1508] Output: The rating data is sent to the server.

[1509] Step 13:

[1510] Database Update

[1511] The server updates the database based on the received evaluation information and reflects it in the next analysis and the provision of discount information.

[1512] Specific actions

[1513] The server analyzes the rating data and performs a process to update its internal database.

[1514] Input: Evaluation data.

[1515] Output: The updated database.

[1516] (Application example 1)

[1517] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1518] Conventional inventory management systems have difficulty efficiently identifying products with approaching expiration dates and providing appropriate discount information to consumers. Furthermore, few systems offer customized recipes based on the ingredients a consumer has, failing to improve the consumer's purchasing experience. Furthermore, there is a lack of a way to visually present product discount information, creating technical challenges for improving the work efficiency of store staff.

[1519] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1520] In this invention, the server includes: means for collecting inventory data of retailers; means for collecting sales data; means for collecting environmental data using IoT sensors; means for collecting data such as consumer purchasing history and preferences; means for analyzing the inventory data, sales data, IoT sensor data, and consumer data using a generative AI model to identify products with upcoming expiration dates; means for generating discount information for the products with upcoming expiration dates; means for transmitting the discount information to each consumer; means for receiving data on ingredients owned by the consumer and generating customized recipes based thereon; means for generating an additional ingredient list and its discount information based on the recipes; means for providing the additional ingredient list and its discount information to the consumer; means for visually presenting the discount information on smart glasses or a head-mounted display; and means for collecting consumer feedback data and improving the analysis means based thereon. This enables efficient inventory management, providing appropriate discount information to consumers, generating customized recipes, and improving the work efficiency of store staff.

[1521] A "retailer" is a business that sells products directly to general consumers.

[1522] "Inventory data" refers to information relating to the quantity, storage location, expiration date, etc. of products in stores and warehouses.

[1523] "Sales data" refers to information relating to past sales history and sales.

[1524] An "IoT sensor" is a sensor device for collecting physical environmental data (e.g., temperature, humidity, location data, etc.).

[1525] "Environmental data" refers to data such as the temperature and humidity inside the store, and the location of shopping carts.

[1526] "Consumer data" refers to data such as consumer purchasing history, preferences, and feedback information.

[1527] A "generative AI model" is an artificial intelligence model that analyzes collected data and is generated to achieve a specific purpose.

[1528] "Discount information" is information such as the discount rate for a product.

[1529] A "customized recipe" is an individually optimized recipe generated based on the ingredients the consumer has on hand.

[1530] The "additional ingredient list" is a list showing the additional ingredients required and their quantities based on the generated recipe.

[1531] "Smart glasses or head-mounted display" refers to a wearable device for visually displaying information.

[1532] "Visually presented" means displaying information directly in the user's field of vision.

[1533] "Feedback data" refers to data such as ratings and post-purchase opinions provided by consumers.

[1534] This invention provides a system that allows retailers to improve the efficiency of inventory management in brick-and-mortar stores and provide appropriate discount information to consumers. The overall configuration and specific operation of the system are described below.

[1535] Overall system overview

[1536] The system mainly consists of a server, smart glasses or head-mounted displays (HMDs), and IoT sensors. The server collects and analyzes various data, generates the necessary information, and sends it to the smart devices. The smart devices then visually present the information to the user.

[1537] Data collection and analysis

[1538] The server accesses the retailer's database to periodically collect inventory and sales data. It also collects environmental data (temperature, humidity, location information) from IoT sensors installed in the store. It also accesses the consumer database to collect consumer purchasing history, preferences, and feedback information.

[1539] All collected data is passed to a generative AI model for analysis. The model identifies products that are close to expiring and generates discount offers for those products. For example, a 20% off coupon for tomatoes that are close to expiring will be set. The model also creates discount offers optimized for each consumer.

[1540] Discount information and feedback

[1541] Discount and stock information is visually presented to users using smart glasses or HMDs, such as Microsoft HoloLens or Google Glass. When users stand in front of a shelf, discount information for products is displayed on the device's transparent display.

[1542] It also generates recipes: users input the ingredients they have on hand, and the generative AI model generates the optimal recipe, calculating any additional ingredients needed and discount information, allowing consumers to shop efficiently.

[1543] After shopping, users can provide feedback through the app. The feedback data is sent to the server and reflected in the database. This will be used for future analysis and to provide discount information.

[1544] Specific examples

[1545] The server analyzes the store's inventory data and generates discount information such as a 20% discount on tomatoes with a best-by date of two days. This information is visually communicated to the user via smart glasses or an HMD. When the user inputs 300g of chicken breast and two cloves of garlic as ingredients on hand, the generative AI model suggests a recipe for "chicken breast and tomato pasta," and determines that "200g of pasta and two tablespoons of olive oil" are also required, generating discount information for this.

[1546] Prompt Sentence Examples

[1547] Consider a specific example of an AR application that assists in inventory management in a physical store. When approaching a shelf, the system displays discount information for products with an approaching expiration date in the field of view of smart glasses. Please explain in detail what kind of interface and notification method you could use.

[1548] In this way, the system of the present invention can improve the work efficiency of store staff and provide consumers with appropriate information in real time, thereby providing an efficient shopping experience.

[1549] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1550] Step 1:

[1551] The server accesses the retailer's database and collects inventory and sales data. This includes information such as product quantity, storage location, and expiration date. The inventory and sales data is periodically retrieved using an API. The input is the database query results, and the output is organized inventory and sales data stored in the server's memory.

[1552] Step 2:

[1553] The server collects environmental data from IoT sensors. Data such as temperature, humidity, and shopping cart location information is obtained from the IoT sensors via an API. The input is real-time data sent from the sensors, and the output is organized environmental data that is stored in the server's memory. Specifically, the server receives data sent from sensors in the store and converts it into an analyzable format.

[1554] Step 3:

[1555] The server accesses the consumer database to collect consumer purchasing history, preferences, and feedback information. The input is the query results from the consumer database, and the output is organized consumer data stored in the server's memory. Specifically, it obtains each consumer's past purchasing history and provided feedback in an analyzable format.

[1556] Step 4:

[1557] The server passes the collected inventory data, sales data, IoT sensor data, and consumer data to the generative AI model for analysis. All collected data is input, and the output is a list of products with upcoming expiration dates and their discount information. Specifically, the data is input into the AI ​​model, and discount information for specific products is generated as an analysis result. For example, a 20% off coupon is set for tomatoes with an expiration date in two days.

[1558] Step 5:

[1559] The server sends the generated discount information to each consumer's device. The input is the generated discount information, and the output is a notification sent to the consumer's device. Specifically, the discount coupon information is sent to the consumer's smartphone as an electronic notification.

[1560] Step 6:

[1561] The user opens the smartphone app and inputs the ingredients they have and the amount. The input is the ingredient data the user has, and the output is the ingredient data sent to the server. Specifically, the user enters the ingredient data into the app's input form and presses the send button, which sends the data to the server.

[1562] Step 7:

[1563] The server uses a generative AI model to generate customized recipes based on the received user data. The inputs are the user's ingredient data and inventory data, and the output is the generated recipe and a list of additional ingredients. Specifically, the ingredient data is input into the AI ​​model, which generates the optimal recipe and a list of required additional ingredients. For example, it proposes a recipe for "pasta with chicken breast and tomatoes" and determines that "200g of pasta and 2 tablespoons of olive oil" are required.

[1564] Step 8:

[1565] The server generates a shopping list of the necessary ingredients and assigns corresponding discount coupons. The input is a customized recipe and a list of additional ingredients, and the output is a shopping list with discount coupons. Specifically, the server sets discount information for the additional ingredients and generates the entire list.

[1566] Step 9:

[1567] The user can check the suggested recipe and additional ingredient list on their smartphone. The input is the recipe information sent from the server, and the output is the information displayed on the user's device. Specifically, the user opens the smartphone app to check the recipe and shopping list.

[1568] Step 10:

[1569] Users enter their post-shopping experience and product evaluation as feedback through a smartphone app. The input is the user's feedback data, and the output is feedback information sent to the server. Specifically, users enter their thoughts in the app's feedback form and press the send button.

[1570] Step 11:

[1571] The server analyzes the received feedback data and updates the database to reflect it in the next analysis and discount information provision. The input is the user's feedback data, and the output is an updated database. Specifically, the feedback information is input into the analysis model to improve the accuracy of future analysis algorithms.

[1572] The above processing steps enable efficient inventory management, appropriate discount information to consumers, and the creation of customized recipes.

[1573] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1574] Overall system configuration

[1575] This system collects and analyzes retailers' inventory data, sales data, IoT sensor data, and consumer data, presents discount information for products with approaching expiration dates, and generates customized recipes based on the ingredients the consumer has and the user's emotional data. It also collects actual consumer purchasing data and feedback, and optimizes the system based on that data.

[1576] Program processing

[1577] Data collection

[1578] 1. Server

[1579] Access the retailer's database to collect inventory data. This includes information such as product quantity, storage location, and expiration date. For example, this includes data such as "Tomatoes: 10 units in stock, expiration date in 2 days."

[1580] Sales data is also collected in the same way. Past sales history, prices, purchaser information, etc. are obtained. For example, data such as "Tomatoes: 60 units sold in the past 30 days, an average of 2 units per day" is collected.

[1581] IoT sensors are used to collect in-store environmental data (temperature, humidity, shopping cart location, etc.). Specifically, data is collected for "in-store temperature: 20°C, humidity: 50%."

[1582] Access consumer databases to collect consumer purchasing history, preferences, and feedback information, such as "Consumer A: Purchased tomatoes 5 times."

[1583] Data analysis

[1584] 2. Server

[1585] All collected data is passed to a generative AI model for analysis. Here, products with an approaching expiration date are identified and discount information for those products is generated. For example, if "five tomatoes with an expiration date in two days" are identified, a "20% off discount coupon for tomatoes" is created.

[1586] Delivery of bargain information

[1587] 3. Server

[1588] Based on the analysis results, the system generates bargain information tailored to each individual consumer and sends an electronic notification to their device. For example, a notification will be sent to "Consumer A" that "Tomatoes are 20% off, with the expiration date in two days."

[1589] 4. Terminal

[1590] A notification will appear on the consumer's smartphone, for example, "Tomatoes 20% off, expiration date in 2 days."

[1591] User input collection and sentiment data collection

[1592] 5. Users

[1593] Open the smartphone app and enter the ingredients you have and the quantities. For example, enter "300g chicken breast, 2 cloves of garlic" into the app.

[1594] The app uses a built-in emotion engine to recognize the user's emotions (e.g., happy, tired, etc.).

[1595] 6. Terminal

[1596] The input food ingredient data and emotion data are sent to the server. For example, the data "300g chicken breast, 2 cloves of garlic" and "Emotion: fun" are sent to the server.

[1597] Creating a customized recipe

[1598] 7. Server

[1599] Based on the received user data and emotion data, the generative AI model generates customized recipes. For example, it determines that "it's okay if the cooking time is long because the user seems to enjoy it," and suggests a recipe such as "chicken breast and tomato pasta."

[1600] It calculates the additional ingredients needed and their quantities and generates a shopping list. For example, the list might include "200g pasta, 2 tablespoons olive oil."

[1601] 8. Terminal

[1602] The suggested recipe and a list of additional ingredients are displayed to the user, for example, "Chicken breast and tomato pasta, plus 200g pasta and 2 tablespoons olive oil."

[1603] Shopping list and discount coupons provided

[1604] 9. Server

[1605] Add discount coupons to a list of required additional ingredients, for example, "Pasta 200g - 10% off discount coupon, Tomato - 20% off discount coupon".

[1606] 10. Terminal

[1607] A shopping list and discount coupons are displayed on the user's smartphone, allowing them to shop efficiently.

[1608] Purchasing data feedback

[1609] 11. Users

[1610] After shopping, users enter their purchases and their ratings through the app. For example, they can provide feedback such as, "I bought five tomatoes, and the chicken breast and tomato pasta was delicious."

[1611] 12. Terminal

[1612] Send feedback data to the server. For example, "I bought 5 tomatoes and the pasta was delicious."

[1613] 13. Server

[1614] We use your feedback to update our database, which can be reflected in our next analysis and offer.

[1615] Specific examples

[1616] server

[1617] Collect inventory data for tomatoes from a retailer and identify that five of them have a best-by date two days later. This will qualify for a discount, so set up a 20% off coupon for the tomatoes.

[1618] Terminal

[1619] A notification appears on the user's smartphone saying, "Tomatoes 20% off, expiration date in 2 days."

[1620] User

[1621] You log in to the app and enter the information you have, such as "300g chicken breast and 2 cloves of garlic," and the emotion engine recognizes that you are in a happy state.

[1622] server

[1623] The received data is analyzed and a recipe for "chicken breast and tomato pasta" is suggested, with the additional recommendation of "200g of pasta and 2 tablespoons of olive oil."

[1624] Terminal

[1625] It displays suggested recipes and ingredient lists to users and allows them to take advantage of discount coupons.

[1626] User

[1627] After shopping, customers provide feedback, which is received by the server and updated in the database.

[1628] In this way, the system of the present invention can also utilize user emotional data to optimize inventory management for retailers and the consumer purchasing experience.

[1629] The processing flow will be explained below.

[1630] Step 1:

[1631] server

[1632] Access the retailer's database and collect inventory data. Specifically, obtain information such as product quantity, storage location, expiration date, etc. For example, obtain data such as "Tomatoes: 10 units in stock, expiration date in 2 days."

[1633] Step 2:

[1634] server

[1635] Collect sales data, such as past sales history, prices, and buyer information. For example, obtain data such as "Tomatoes: 60 units sold in the past 30 days, an average of 2 units per day."

[1636] Step 3:

[1637] server

[1638] IoT sensors are used to collect environmental data. Specifically, sensor data such as the temperature, humidity, and location of shopping carts in the store is collected. For example, data such as "Store temperature: 20°C, humidity: 50%" is acquired.

[1639] Step 4:

[1640] server

[1641] Access consumer databases to collect consumer purchasing history, preferences, and feedback information. For example, obtain data such as "Consumer A: Purchased tomatoes five times in the past."

[1642] Step 5:

[1643] server

[1644] The collected inventory data, sales data, IoT sensor data, and consumer data are passed to a generative AI model for analysis. The analysis identifies products with an approaching expiration date. For example, "identify five tomatoes with an expiration date in two days."

[1645] Step 6:

[1646] server

[1647] Generate discount information for identified products that are close to their expiration date. For example, "Set a 20% discount coupon for tomatoes."

[1648] Step 7:

[1649] server

[1650] Customize the discount information generated for each consumer and prepare an electronic notification to send to each consumer. For example, send a notification to "Consumer A" that "Tomatoes are 20% off, expiration date is 2 days away."

[1651] Step 8:

[1652] Terminal

[1653] A notification will be displayed on the consumer's smartphone, for example, "Tomatoes 20% off, expiration date in 2 days."

[1654] Step 9:

[1655] User

[1656] Open the smartphone app and enter the ingredients you have and the quantities. For example, enter "300g chicken breast, 2 cloves of garlic."

[1657] Step 10:

[1658] Terminal

[1659] The app's emotion engine recognizes the user's emotions, for example, detecting emotions such as "fun" using the camera and microphone.

[1660] Step 11:

[1661] Terminal

[1662] The input food ingredient data and emotion data are sent to the server. For example, the data "300g chicken breast, 2 cloves of garlic" and "Emotion: happy" are sent to the server.

[1663] Step 12:

[1664] server

[1665] Based on the received user data and emotion data, the generative AI model generates customized recipes. For example, it determines that the user will enjoy cooking, so it may be okay to wait a long time and suggests a recipe for "chicken breast and tomato pasta."

[1666] Step 13:

[1667] server

[1668] Generate a shopping list by calculating additional ingredients and their quantities, for example, "200g pasta, 2 tablespoons olive oil"

[1669] Step 14:

[1670] Terminal

[1671] Presents the user with a customized recipe and a list of additional ingredients, such as "Chicken breast and tomato pasta, plus 200g pasta and 2 tablespoons olive oil."

[1672] Step 15:

[1673] server

[1674] Add discount coupons to a list of required additional ingredients, for example, "Pasta 200g - 10% off discount coupon, Tomato - 20% off discount coupon".

[1675] Step 16:

[1676] Terminal

[1677] It displays shopping lists and discount coupons on the user's smartphone, allowing them to shop efficiently.

[1678] Step 17:

[1679] User

[1680] After shopping, users enter their purchases and their ratings through the app. For example, they can provide feedback such as, "I bought five tomatoes, and the chicken breast and tomato pasta was delicious."

[1681] Step 18:

[1682] Terminal

[1683] Send feedback data to the server. For example, "I bought 5 tomatoes, and the chicken breast and tomato pasta was delicious."

[1684] Step 19:

[1685] server

[1686] We use the feedback we receive to update our database, which can be used to analyze data and provide deals to you next time.

[1687] In this way, by linking the server, terminal, and user, a system can be created that utilizes user emotional data and provides a more personalized purchasing experience.

[1688] Example 2

[1689] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1690] Modern retailers are faced with the complex task of managing a wide variety of products, making inventory management and expiration date management cumbersome. Consumers also struggle to find new recipes that make the most of the ingredients they already have. Providing recipes that match consumers' emotions and current desires is particularly challenging, posing many challenges for providing an optimal shopping experience.

[1691] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1692] In this invention, the server includes means for collecting inventory data, means for collecting sales data, means for collecting environmental data, means for collecting data such as consumer purchasing history and preferences, means for analyzing the inventory data, sales data, environmental data, and consumer data to identify products with upcoming expiration dates, means for generating discount information for the products with upcoming expiration dates, means for transmitting the discount information to each consumer, means for receiving data on ingredients owned by the consumer and generating customized recipes based thereon, means for generating an additional ingredient list and its discount information based on the recipe, means for providing the additional ingredient list and discount information to the consumer, means for collecting consumer feedback data and improving the analysis means based thereon, and means for collecting emotion data and suggesting optimal recipes to the consumer based thereon. This makes it possible to improve inventory management efficiency and provide optimal recipes and discount information to individual consumers.

[1693] "Inventory data" refers to information about inventory, such as product quantities, storage locations, and expiration dates.

[1694] "Sales data" refers to data including transaction records, prices, and purchaser information when a retailer sells a product.

[1695] "Environmental data" refers to information about the environment, such as temperature, humidity, and the location of shopping carts within a store, collected through IoT sensors.

[1696] "Consumer data" refers to information about consumer behavior and trends, such as consumer purchasing history, preferences, and feedback information.

[1697] "Best before" refers to the period during which food or consumable products can maintain their quality, after which the quality may deteriorate.

[1698] "Discount Information" refers to information about price discounts and coupons offered for specific products.

[1699] "Customized recipes" refer to cooking instructions that are individually generated based on the consumer's ingredients, preferences, and emotional data.

[1700] "Emotional data" refers to data that indicates a consumer's emotional state (e.g., happy, tired, etc.).

[1701] "Additional Ingredient List" means a list of additional ingredients and their quantities required to prepare a customized recipe.

[1702] "Feedback data" refers to data containing reactions and impressions about products and services, such as ratings and opinions provided by consumers.

[1703] "Generative AI model" refers to an artificial intelligence model that analyzes collected data and performs a specific task (e.g., generating a recipe or presenting a discount coupon).

[1704] This system collects and analyzes inventory data, sales data, environmental data, and consumer data from retailers, generates discount information for products approaching their expiration date, and provides customized recipes based on the consumer's ingredients and emotional data. The system consists of the following main components:

[1705] 1. Data Collection

[1706] server:

[1707] The server accesses the retailer's database to collect inventory data. Specific techniques include using SQL queries. For example, data is retrieved using "SELECT FROM inventory WHERE expiration_date < '2023-10-20'". Sales data is also collected, including transaction records, prices, and customer information. Furthermore, IoT sensors are used to collect environmental data such as in-store temperature, humidity, and shopping cart location. Finally, a consumer database is accessed to retrieve consumer purchasing history, preferences, and feedback information.

[1708] 2. Data Analysis

[1709] server:

[1710] The server inputs the collected inventory data, sales data, environmental data, and consumer data into a generative AI model for analysis. The generative AI model uses libraries such as TensorFlow and PyTorch. Here, it identifies products with an approaching expiration date and generates discount information for those products. For example, it creates a 20% discount coupon for "five tomatoes with an expiration date in two days."

[1711] 3. Delivery of bargain information

[1712] server:

[1713] Based on the analysis results, the system generates bargain information tailored to each individual consumer and sends an electronic notification to each consumer's device. For example, the notification might read, "Consumer A receives 20% off tomatoes with a best-by date in two days."

[1714] Device:

[1715] The device displays a notification on the consumer's smartphone, for example, "Tomatoes 20% off, expiration date in 2 days."

[1716] 4. Collecting user input and emotion data

[1717] User:

[1718] The user opens the smartphone app and inputs the ingredients they have and the amounts. Specifically, they input "300g chicken breast, 2 cloves of garlic" into the app. The app also uses its built-in emotion engine to recognize the user's emotions.

[1719] Device:

[1720] The device sends the input food ingredient data and emotion data to the server. For example, data such as "300g chicken breast, 2 cloves of garlic" and "emotion: fun" are sent to the server.

[1721] 5. Creating a customized recipe

[1722] server:

[1723] The server uses the received user data and emotional data to generate a customized recipe using a generative AI model. For example, it determines that "it seems like the user will enjoy cooking it, so it's okay if it takes a long time to cook," and suggests a recipe such as "chicken breast and tomato pasta." It also calculates the additional ingredients needed and their quantities, and generates a shopping list. For example, the list might include "200g of pasta and 2 tablespoons of olive oil."

[1724] Device:

[1725] The device will then display a suggested recipe and a list of additional ingredients to the user, such as "Chicken breast and tomato pasta, plus 200g pasta and 2 tablespoons olive oil."

[1726] 6. Providing shopping lists and discount coupons

[1727] server:

[1728] The server will add discount coupons to the list of required additional ingredients, for example "Pasta 200g - 10% off discount coupon, Tomato - 20% off discount coupon".

[1729] Device:

[1730] The device displays a shopping list and discount coupons on the user's smartphone, allowing them to shop efficiently.

[1731] 7. Feedback of purchasing data

[1732] User:

[1733] After shopping, users can enter their purchases and their ratings through the app, for example, "I bought five tomatoes, and the chicken breast and tomato pasta was delicious."

[1734] Device:

[1735] The device sends feedback data to the server, for example, "I bought five tomatoes and the pasta was delicious."

[1736] server:

[1737] The server can update the database based on the feedback and reflect it in the next analysis or offer of bargains.

[1738] This allows the system to utilize user emotion data to improve the efficiency of inventory management for retailers and optimize the consumer purchasing experience. For example, the following prompt can be used: "User emotion: Fun, Ingredients on hand: 300g chicken breast, 2 cloves garlic."

[1739] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1740] Step 1: Data collection

[1741] Server: The server accesses the retailer's database to collect inventory data. This data includes product quantities, storage locations, expiration dates, and so on. For example, it retrieves data using an SQL query like "SELECT FROM inventory WHERE expiration_date < '2023-10-20'". Sales data includes transaction records, prices, and purchaser information, and is collected in a similar manner. Furthermore, it retrieves environmental data such as in-store temperature, humidity, and shopping cart location through IoT sensors. An example of environmental data is "store temperature: 20°C, humidity: 50%". Finally, it retrieves consumer purchasing history, preferences, and feedback information from the consumer database. These data are collected as input and stored in the database as output.

[1742] Step 2: Data analysis

[1743] Server: Collected inventory data, sales data, environmental data, and consumer data are input into the generative AI model and data analysis is performed. For example, a generative AI model written in Python uses libraries such as TensorFlow and PyTorch. Based on the input data, products with an approaching expiration date are identified. As a result of this analysis, for example, if "five tomatoes with an expiration date in two days" are identified, a "20% off discount coupon for tomatoes" is created. Data input is information from various databases, and discount information is obtained as output.

[1744] Step 3: Generate and distribute deals

[1745] Server: Based on the analysis results, it generates bargain information suitable for each individual consumer and sends this information to each consumer's device in an electronic notification. For example, it sends a notification to consumer A offering tomatoes at 20% off with the expiration date in two days. The input is the analysis results and individual consumer information, and the output is the delivery of an electronic notification.

[1746] Terminal: The terminal displays a notification on the consumer's smartphone. For example, it displays a notification saying, "Tomatoes are 20% off, expiration date is in 2 days." The input is the notification information from the server, and the output is the display on the smartphone screen.

[1747] Step 4: Collecting user input and emotion data

[1748] User: The user opens the smartphone app and inputs the ingredients they have and the quantities. For example, they input "300g chicken breast, 2 cloves of garlic" into the app. The app also uses its built-in emotion engine to recognize the user's emotion data. The input is the user's actions, and the output is the input data.

[1749] Terminal: The terminal sends the input food ingredient data and emotion data to the server. For example, the data "300g chicken breast, 2 cloves of garlic" and "Emotion: fun" are sent to the server. The input is the data entered by the user, and the output is the data sent to the server.

[1750] Step 5: Generate a customized recipe

[1751] Server: The server uses the generative AI model to generate a customized recipe based on the received user data and emotional data. For example, it determines that "it's okay if the cooking time is long because the user seems to enjoy it," and suggests a recipe such as "chicken breast and tomato pasta." It also calculates the additional ingredients needed and their quantities, and generates a shopping list. For example, the list might include "200g pasta and 2 tablespoons olive oil." The input is user data and emotional data, and the output is a customized recipe and a shopping list.

[1752] Terminal: The terminal displays the suggested recipe and a list of additional ingredients to the user. For example, "Chicken breast and tomato pasta, plus 200g of pasta and 2 tablespoons of olive oil." The input is the recipe information from the server, and the output is the display on the smartphone screen.

[1753] Step 6: Provide a shopping list and discount coupons

[1754] Server: The server adds discount coupons to a list of required additional ingredients. For example, it generates "Pasta 200g - 10% off discount coupon, Tomato - 20% off discount coupon". The input is a shopping list, and the output is a list with discount coupons.

[1755] Terminal: The terminal displays a shopping list and discount coupons on the user's smartphone, allowing for efficient shopping. The input is discount coupon information from the server, and the output is the display on the smartphone screen.

[1756] Step 7: Feedback on purchasing data

[1757] User: After shopping, the user enters the items they actually purchased and their evaluation through the app. For example, they provide feedback such as, "I bought five tomatoes, and the chicken breast and tomato pasta was delicious." The input is the evaluation after the purchase, and the output is the feedback data.

[1758] Terminal: The terminal sends feedback data to the server. For example, the terminal sends data such as "I bought five tomatoes and the pasta was delicious." The input is the user's feedback data, and the output is the data sent to the server.

[1759] Server: The server updates the database based on the feedback and reflects it in the next analysis and the provision of bargain information. The input is the feedback data, and the output is the updated database information.

[1760] (Application example 2)

[1761] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1762] In today's retail industry, there is a demand for more efficient inventory management and improved consumer satisfaction. Unsold and discarded products approaching their expiration date present significant challenges for retailers. There is also a need to improve the purchasing experience through services such as personalized recommendations and recipe provision. However, simultaneously addressing these challenges requires the collection and analysis of various data and real-time notifications to consumers, which existing systems are not yet able to adequately address. Further advances are needed, particularly in real-time notifications utilizing the latest technologies, including smart glasses, and the provision of customized recipes that take emotional data into account.

[1763] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1764] In this invention, the server includes: means for collecting inventory data of retailers; means for collecting sales data; means for collecting environmental data using IoT sensors; means for collecting data such as consumer purchasing history and preferences; means for analyzing the inventory data, sales data, IoT sensor data, and consumer data to identify products with upcoming expiration dates; means for generating discount information for the products with upcoming expiration dates; means for transmitting the discount information to each consumer; means for receiving data on ingredients owned by the consumer and generating customized recipes based thereon; means for generating an additional ingredient list and its discount information based on the recipes; means for providing the additional ingredient list and the discount information to the consumer; means for collecting consumer feedback data and improving the analysis means based thereon; means for notifying discount information for products with upcoming expiration dates in real time using smart glasses; and means for collecting consumer emotion data and taking the emotion data into consideration when generating the recipes. This improves the accuracy of inventory management and enables personalized suggestions and recipes to be provided to consumers in real time, thereby improving the purchasing experience.

[1765] "Inventory data" is a collection of information such as the quantity, type, expiration date, and storage location of products held by a retailer.

[1766] "Sales data" is a collection of data including the history of past sales of products, prices, purchaser information, and so on.

[1767] "IoT sensor data" refers to environmental data such as temperature, humidity, and shopping cart location collected using IoT sensors.

[1768] "Consumer data" is a collection of information such as a consumer's purchasing history, preferences, and feedback.

[1769] "Analysis means" refers to a technical means for analyzing collected data and identifying products that are close to their expiration date.

[1770] "Discount information" refers to information about price reductions offered for products that are close to their expiration date.

[1771] "Notification means" refers to a technical means for sending discount information and recipe information to each consumer.

[1772] A "customized recipe" is a personalized cooking procedure generated based on the consumer's ingredient and emotional data.

[1773] The "additional ingredient list" is a list showing the additional ingredients and their quantities required for cooking based on the customized recipe.

[1774] "Smart glasses" are eyeglass-type devices that have the ability to display information in real time.

[1775] "Emotional data" refers to information used to recognize a consumer's emotional state and analyze that data.

[1776] "Feedback data" refers to data such as post-purchase ratings and impressions provided by consumers.

[1777] Overall system overview

[1778] This invention is a system that collects and analyzes retailers' inventory data, sales data, IoT sensor data, and consumer data, presents discount information for products approaching their expiration date, and generates customized recipes based on the ingredients and emotional data of consumers. It also collects actual consumer purchasing data and feedback, and optimizes the system based on that data.

[1779] Hardware and software used

[1780] The main hardware used is as follows:

[1781] Smart glasses (e.g., general smart wearable devices)

[1782] IoT sensors (e.g. temperature sensors, humidity sensors)

[1783] Server (analyzes and stores data)

[1784] The main software used is as follows:

[1785] Generative AI models (e.g., GPT-4)

[1786] Database management system (e.g. MySQL)

[1787] Emotion Recognition Engine

[1788] Detailed process description

[1789] The server performs the following steps:

[1790] 1. Data Collection

[1791] The server accesses the retailer's database and collects inventory data, sales data, IoT sensor data, and consumer data. For example, inventory data includes information such as product quantity, storage location, and expiration date. IoT sensors are also used to collect environmental data such as temperature, humidity, and shopping cart location within the store. Consumer data includes purchasing history, preferences, and feedback information.

[1792] 2. Data Analysis

[1793] The collected data is analyzed by a generative AI model (e.g., GPT-4). As a result of the analysis, products with an approaching expiration date are identified, and discount information for those products is generated. For example, if there are "five tomatoes with an expiration date in two days," a "20% off discount coupon for tomatoes" is generated.

[1794] 3. Notification function

[1795] The server sends the generated discount information to the consumer's smart glasses in real time. For example, a notification such as "Tomatoes 20% off, expiration date in 2 days" is displayed. This information is displayed on the smart glasses' display, allowing the consumer to understand it immediately.

[1796] 4. Collecting User Input

[1797] The user inputs the information about the ingredients they have through the smart glasses, and the emotion engine collects emotional data. For example, if the user inputs "300g chicken breast, 2 cloves of garlic," the emotion recognition engine will recognize the user's emotion as "fun." This data is then sent to the server.

[1798] 5. Creating a customized recipe

[1799] The server uses a generative AI model (e.g., GPT-4) to generate a customized recipe based on the received user's ingredient and emotion data. For example, a recipe such as "chicken breast and tomato pasta" is suggested. Additionally, additional ingredients and their amounts are calculated, resulting in a list of "200g pasta and 2 tablespoons olive oil."

[1800] 6. Providing shopping lists and discount coupons

[1801] The generated recipe, additional ingredients list, and discount coupons are displayed on the smart glasses, allowing users to shop efficiently.

[1802] Examples of concrete examples and prompts

[1803] Examples:

[1804] A user is shopping in a physical store while wearing smart glasses. Data collected by the server includes inventory information for tomatoes (10 in stock, expiration date in 2 days). The user inputs the ingredients they have (300g chicken breast, 2 cloves of garlic) and recognizes that their emotion is "happy." Based on this, the server suggests "pasta with chicken breast and tomatoes," lists "200g pasta, 2 tablespoons olive oil" as additional ingredients, and provides a discount coupon.

[1805] Example prompt sentence:

[1806] prompt:

[1807] Analyze the following data and suggest suitable recipes for the user.

[1808] Stock Data: Tomatoes - 10 in stock, expiration date in 2 days

[1809] Emotional data: Fun

[1810] Ingredients I have: 300g chicken breast, 2 cloves of garlic

[1811] Required steps:

[1812] 1. Identify products that are nearing their expiration date and generate discount information.

[1813] 2. Suggest recipes that take into account the ingredients and emotional data of the user.

[1814] 3. Make a list of any additional ingredients you need.

[1815] Example output: Chicken breast and tomato pasta, 200g extra pasta, 2 tablespoons olive oil

[1816] This invention improves the accuracy of inventory management and makes it possible to provide individual suggestions and recipes to consumers in real time, thereby improving the purchasing experience.

[1817] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1818] Step 1:

[1819] The server accesses the retailer's database and collects inventory data. Specifically, it obtains information such as product quantity, expiration date, and storage location. For example, it collects data such as "Tomatoes: 10 in stock, expiration date 2 days away."

[1820] Input: Inventory data from the database

[1821] Output: Collected inventory data

[1822] What it does: Retrieves inventory information from a database using an SQL query.

[1823] Step 2:

[1824] The server collects sales data, including past sales history, prices, and buyer information. For example, data such as "Tomatoes: 60 units sold in the past 30 days, an average of 2 units per day" is collected.

[1825] Input: Sales data from the database

[1826] Output: Collected sales data

[1827] What it does: Retrieves sales information from a database using an SQL query.

[1828] Step 3:

[1829] The server uses IoT sensors to collect environmental data, such as the temperature and humidity inside the store and the location of shopping carts. For example, it obtains data such as "Store temperature: 20°C, humidity: 50%."

[1830] Input: Environmental data from IoT sensors

[1831] Output: Collected environmental data

[1832] Specific operation: Call the IoT sensor's data collection API to obtain environmental information.

[1833] Step 4:

[1834] The server collects data such as consumer purchasing history and preferences. For example, it collects information such as "Consumer A: purchased tomatoes 5 times."

[1835] Input: Consumer data from a database

[1836] Output: Collected consumer data

[1837] What it does: Retrieves consumer information using an SQL query.

[1838] Step 5:

[1839] The server analyzes the collected inventory data, sales data, IoT sensor data, and consumer data to identify products with an approaching expiration date. For example, it identifies "five tomatoes with an expiration date in two days."

[1840] Input: Inventory data, sales data, IoT sensor data, consumer data

[1841] Output: Identified products with an approaching expiration date

[1842] Specific operation: Using a generative AI model, data is analyzed and target products are identified.

[1843] Step 6:

[1844] The server generates discount information for products that are close to their expiration date. For example, it generates a "20% off discount coupon for tomatoes."

[1845] Input: Identified products with an approaching expiration date

[1846] Output: Generated discount coupon

[1847] Specific operation: Executes the discount information generation algorithm to create a coupon.

[1848] Step 7:

[1849] The server sends the generated discount information to the consumer, who then sends a notification to the smart glasses, displaying information such as "20% off tomatoes, expiration date in 2 days."

[1850] Input: Generated discount coupon

[1851] Output: Notification sent to smart glasses

[1852] What it does: Sends information to consumer devices using the Notification API.

[1853] Step 8:

[1854] The user inputs the information about the ingredients they have through the smart glasses, for example, "300g chicken breast, 2 cloves of garlic."

[1855] Input: User's ingredient information

[1856] Output: Input food ingredients data

[1857] Specific operation: Collect food ingredient data through the input interface of smart glasses.

[1858] Step 9:

[1859] The emotion engine of the device recognizes the user's emotion data and sends it to the server. For example, the user's emotion is recognized as "happy."

[1860] Input: User's emotional state

[1861] Output: Recognized emotion data

[1862] Specific operation: Analyzes input data using an emotion recognition engine and recognizes the emotional state.

[1863] Step 10:

[1864] The server generates a customized recipe based on the received user's ingredients and emotion data. For example, it might suggest "chicken breast and tomato pasta" and add "200g pasta, 2 tablespoons olive oil."

[1865] Input: food data, emotion data

[1866] Output: Customized recipe, additional ingredients list

[1867] How it works: Uses a generative AI model to generate a customized recipe and calculate any additional ingredients needed.

[1868] Step 11:

[1869] The server displays the generated recipe, additional ingredients list, and discount coupons on the smart glasses, allowing users to shop efficiently.

[1870] Input: Customized recipes, additional ingredient lists, discount coupons

[1871] Output: Information displayed on the smart glasses

[1872] Specific operation: Send information to the smart glasses through the display interface.

[1873] Step 12:

[1874] After shopping, the user provides feedback through the smart glasses, for example, "I bought five tomatoes and the pasta was delicious."

[1875] Input: User feedback data

[1876] Output: Input feedback data

[1877] Specific operation: Collect feedback data through the input interface of smart glasses.

[1878] Step 13:

[1879] The server analyzes the collected feedback data and updates the database to reflect it in the next analysis and offer of deals.

[1880] Input: Feedback data

[1881] Output: Updated database

[1882] Specific behavior: Parse the feedback data and execute SQL queries to update the database.

[1883] 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.

[1884] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1885] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1886] [Fourth embodiment]

[1887] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1888] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1889] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[1890] 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.

[1891] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1892] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1893] 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.

[1894] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1895] 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.

[1896] 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 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.

[1897] 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.

[1898] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1899] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1900] Overall system configuration

[1901] This invention is a system that collects and analyzes retailers' inventory data, sales data, IoT sensor data, and consumer data, presents discount information for products with approaching expiration dates, and generates customized recipes based on the ingredients the consumer has on hand.

[1902] Program processing

[1903] Data collection

[1904] 1. Server

[1905] Accessing retailer databases to periodically collect inventory and sales data, including product quantities, storage locations, expiration dates, and past sales history.

[1906] In addition, IoT sensors will be used to collect environmental data within the store (temperature, humidity, shopping cart position, etc.).

[1907] Access consumer databases to collect consumer purchasing history, preferences, and feedback information.

[1908] Data analysis

[1909] 2. Server

[1910] All collected data is passed to a generative AI model for analysis, which identifies products with an approaching expiration date and generates discount information for those products.

[1911] For example, if you have tomatoes in stock with a best-by date of two days, set a 20% discount coupon for tomatoes.

[1912] It also takes into account the consumer's purchasing history and preferences to create individually optimized discount offers.

[1913] Delivery of bargain information

[1914] 3. Server

[1915] The generated discount information is customized for each consumer, and an electronic notification is sent to each consumer's terminal.

[1916] For example, a notification may be sent to the consumer saying, "Tomatoes are 20% off, expiration date is in 2 days."

[1917] 4. Terminal

[1918] App notifications are displayed on the user's smartphone, visually presenting bargain information to consumers.

[1919] Collecting User Input

[1920] 5. Users

[1921] Open the smartphone app and enter the ingredients you have and the amounts. For example, enter "300g chicken breast, 2 cloves of garlic."

[1922] 6. Terminal

[1923] The entered information is sent to the server.

[1924] Creating a customized recipe

[1925] 7. Server

[1926] Based on the received user data, the generative AI model generates customized recipes, such as suggesting "pasta with chicken breast and tomatoes."

[1927] Calculates the additional ingredients needed and their quantities and generates a shopping list. For example, it determines that you need 200g of pasta and 2 tablespoons of olive oil.

[1928] 8. Terminal

[1929] Display a suggested recipe and additional ingredient list to the user.

[1930] Shopping list and discount coupons provided

[1931] 9. Server

[1932] Generate a shopping list of the ingredients you need and add corresponding discount coupons, for example, "Pasta 200g - Discount coupon 10% off", "Tomato - Discount coupon 20% off".

[1933] 10. Terminal

[1934] A shopping list and discount coupons are displayed on the user's smartphone, allowing them to shop efficiently.

[1935] Purchasing data feedback

[1936] 11. Users

[1937] After shopping, users enter their purchases and their ratings through the app. For example, "I bought five tomatoes, and the chicken breast and tomato pasta was delicious."

[1938] 12. Terminal

[1939] Send the feedback data to the server.

[1940] 13. Server

[1941] Based on your feedback, we will update our database and reflect it in our next analysis and offer of bargains.

[1942] Specific examples

[1943] server

[1944] Collect inventory data for tomatoes from a retailer and identify that five of them have a best-by date two days later. This will qualify for a discount, so set up a 20% off coupon for the tomatoes.

[1945] Terminal

[1946] A notification appears on the user's smartphone saying, "Tomatoes 20% off, expiration date in 2 days."

[1947] User

[1948] Log in to the app and enter the information you have: 300g chicken breast, 2 cloves of garlic.

[1949] server

[1950] The received data is analyzed and a recipe for "chicken breast and tomato pasta" is suggested, with the additional recommendation of "200g of pasta and 2 tablespoons of olive oil."

[1951] Terminal

[1952] It displays suggested recipes and ingredient lists to users and allows them to apply discount coupons.

[1953] User

[1954] After shopping, customers provide feedback, which is received by the server and updated in the database.

[1955] In this way, the system of the present invention allows retailers to efficiently improve inventory management and food waste, and also allows consumers to shop more efficiently.

[1956] The processing flow will be explained below.

[1957] Step 1:

[1958] server

[1959] Access the retailer's database and collect inventory data. Here, obtain information such as product quantity, storage location, and expiration date. For example, obtain data such as "Tomatoes: 10 units in stock, expiration date in 2 days."

[1960] Step 2:

[1961] server

[1962] Collect sales data. Obtain data including past sales history, prices, and buyer information. For example, collect data such as "Tomatoes: 60 units sold in the past 30 days, an average of 2 units per day."

[1963] Step 3:

[1964] server

[1965] IoT sensors are used to collect environmental data. For example, sensor data such as the temperature, humidity, and location of shopping carts in the store is acquired. Specifically, data such as "Store temperature: 20°C, humidity: 50%" is collected.

[1966] Step 4:

[1967] server

[1968] Collect data on consumer purchasing history and preferences. For example, obtain a list of products purchased by Consumer A in the past and their preferences, such as "Consumer A: has purchased tomatoes five times."

[1969] Step 5:

[1970] server

[1971] It analyzes collected inventory data, sales data, IoT sensor data, and consumer data, and uses a generative AI model to identify products that are approaching their expiration date. For example, it detects five tomatoes that have two days left until their expiration date.

[1972] Step 6:

[1973] server

[1974] Based on the analyzed data, discount information is generated for products that are close to their expiration date. For example, a 20% discount coupon is created for tomatoes.

[1975] Step 7:

[1976] server

[1977] The generated discount information is sent to individual consumers. For example, a notification of 20% off tomatoes with an expiration date of 2 days is sent to "Consumer A."

[1978] Step 8:

[1979] Terminal

[1980] Display a notification on the user's smartphone. For example, a notification like "Tomatoes 20% off, expiration date in 2 days" will be displayed on the smartphone.

[1981] Step 9:

[1982] User

[1983] Open the app and enter the ingredients you have and the quantities. For example, enter "300g chicken breast, 2 cloves of garlic" into the app.

[1984] Step 10:

[1985] Terminal

[1986] The entered data is sent to the server. For example, the data "300g chicken breast, 2 cloves of garlic" is sent to the server.

[1987] Step 11:

[1988] server

[1989] Based on the received user data, the generative AI model generates customized recipes, such as suggesting a recipe for "pasta with chicken breast and tomatoes."

[1990] Step 12:

[1991] server

[1992] Calculate the amount of additional ingredients needed for the recipe. For example, include "200g pasta, 2 tablespoons olive oil" in your list.

[1993] Step 13:

[1994] Terminal

[1995] Presents the user with a customized recipe and a list of additional ingredients, for example, "Chicken breast and tomato pasta, plus 200g pasta and 2 tablespoons olive oil."

[1996] Step 14:

[1997] server

[1998] Add discount coupons to a list of required additional ingredients, for example, "Pasta 200g - 10% off discount coupon, Tomato - 20% off discount coupon".

[1999] Step 15:

[2000] Terminal

[2001] It displays shopping lists and discount coupons on the user's smartphone, allowing them to shop efficiently.

[2002] Step 16:

[2003] User

[2004] After shopping, users enter their purchases and their ratings through the app. For example, they can provide feedback such as, "I bought five tomatoes, and the chicken breast and tomato pasta was delicious."

[2005] Step 17:

[2006] Terminal

[2007] The provided feedback data is sent to the server. For example, data such as "I bought 5 tomatoes and the pasta was delicious" is sent to the server.

[2008] Step 18:

[2009] server

[2010] We use the feedback we receive to update our database so that it can be reflected in our next analysis and offer.

[2011] Example 1

[2012] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2013] Traditional retail inventory management has been problematic due to the difficulty of efficient inventory management and expiration date management, resulting in food waste. It has also been difficult to provide appropriate discount information to consumers, resulting in insufficient motivation to purchase. Furthermore, it has been difficult to provide customized recipe suggestions that take into account the ingredients that consumers have on hand, making it difficult to provide an efficient shopping experience.

[2014] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[2015] In this invention, the server includes: means for collecting inventory information from retailers; means for collecting sales information; means for collecting environmental information using IoT technology; means for collecting information on consumer purchasing behavior and preferences; means for analyzing the inventory information, sales information, IoT information, and consumer information to identify products with upcoming expiration dates; means for generating discount information for the products with upcoming expiration dates; means for transmitting the discount information to each consumer; means for receiving information on ingredients owned by the consumer and generating customized recipes based thereon; means for generating an additional ingredient list and its discount information based on the recipe; means for providing the additional ingredient list and discount information to consumers; and means for collecting consumer evaluation information and improving the analysis means based thereon. This enables more efficient inventory management and reduced food waste, and enables an efficient shopping experience by providing consumers with optimized discount information and customized recipe suggestions.

[2016] "Inventory information" refers to data about the quantity, storage location, expiration date, etc. of products held by a retailer.

[2017] "Sales information" refers to data obtained by retailers regarding the past sales history and sales amount of each product.

[2018] "IoT technology" is a technology that allows physical devices and sensors to communicate with each other via the Internet and collect and exchange data.

[2019] "Environmental information" refers to data about the environment collected using IoT technology, such as the temperature and humidity inside the store and the location of shopping carts.

[2020] "Consumer information" refers to data relating to consumer purchasing behavior, preferences, evaluations, etc.

[2021] "Analysis means" refers to the function of analyzing data using collected inventory information, sales information, IoT information, and consumer information.

[2022] "Discount information" is data relating to discount coupons and discount rates that are applied to products that are close to their expiration date.

[2023] An "electronic notification" is a notification message sent from a server to a consumer's terminal.

[2024] "Customized recipes" are personalized recipes that are suggested based on the ingredients a consumer has on hand.

[2025] The "additional ingredient list" is data listing additional ingredients and their amounts required to realize a customized recipe.

[2026] "Evaluation information" refers to data regarding evaluations and feedback that consumers have given regarding products or services they have actually purchased.

[2027] This invention builds a system that allows for efficient inventory management, reduces food waste, and provides consumers with an optimal shopping experience through collaboration between servers, terminals, and users.

[2028] Hardware and software used

[2029] The server utilizes a high-performance cloud infrastructure, using MySQL for database management and TensorFlow for data analysis. The MQTT protocol is also used to collect data from IoT sensors. A mobile app is installed on user devices, and the app communicates with the server via a Node.js server and REST API.

[2030] Data collection

[2031] The server periodically queries the retailer's database to obtain inventory and sales information. This data includes product quantities, storage locations, expiration dates, and past sales history. It also collects environmental information such as temperature, humidity, and shopping cart locations from IoT sensors in the store. Consumer information, such as purchasing behavior, preferences, and feedback, is obtained through the consumer database.

[2032] Data analysis

[2033] The server passes all collected data to a generative AI model for data analysis. This model identifies products that are close to their expiration date and generates discount information for them. For example, if there are tomatoes in stock that are close to their expiration date, the generative AI model will set a 20% discount coupon for the tomatoes. The model also creates individually optimized discount offers based on the consumer's purchasing history and preferences.

[2034] Delivery of bargain information

[2035] The server customizes the generated discount information for each consumer and sends an electronic notification to each consumer's device. For example, a notification may be sent saying, "Tomatoes 20% off, expiration date in 2 days." The device receives this notification and displays it as an app notification on the user's smartphone.

[2036] Collecting User Input

[2037] The user opens the smartphone app and inputs the ingredients they have and their quantities. For example, they might input "300g chicken breast, 2 cloves of garlic." The device then sends this information in JSON format to the server.

[2038] Creating a customized recipe

[2039] The server uses the received user data to generate a customized recipe using a generative AI model. For example, it suggests a recipe for "pasta with chicken breast and tomatoes." It also calculates the additional ingredients needed and their quantities, and generates a shopping list. For example, it determines that "200g of pasta and 2 tablespoons of olive oil" are needed.

[2040] Shopping list and discount coupons provided

[2041] The server generates a shopping list of the necessary ingredients and provides corresponding discount coupons. For example, "Pasta 200g - Discount coupon 10% off" or "Tomato - Discount coupon 20% off." The device receives this information and displays the shopping list and discount coupons on the user's smartphone.

[2042] Purchasing data feedback

[2043] After shopping, users enter the items they actually purchased and their ratings through the app. For example, they might enter, "I bought five tomatoes, and the chicken breast and tomato pasta was delicious." The device receives this feedback data and sends it to the server. The server updates the database based on the feedback, and reflects it in future analyses and discount information.

[2044] This allows retailers to efficiently improve inventory management and food waste, while providing consumers with an optimal shopping experience.

[2045] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2046] Step 1:

[2047] Data collection

[2048] The server accesses the retailer's database and periodically retrieves inventory and sales information.

[2049] Specific actions

[2050] The server queries a MySQL database to retrieve product quantities, storage locations, expiration dates, and past sales history.

[2051] Input: The server uses the connection information to the database and the query as input.

[2052] Output: The server stores the acquired inventory and sales information in its internal data storage.

[2053] Step 2:

[2054] Collecting environmental data using IoT sensors

[2055] The server collects environmental information such as temperature, humidity, and shopping cart location inside the store from IoT sensors.

[2056] Specific actions

[2057] The server uses the MQTT protocol to collect data from each sensor.

[2058] Input: Real-time data sent from IoT sensors.

[2059] Output: The server stores the collected environmental information in its internal data storage.

[2060] Step 3:

[2061] Collection of Consumer Information

[2062] The server accesses a consumer database to obtain purchasing behavior, preferences, and feedback information.

[2063] Specific actions

[2064] The server sends a request to the consumer database via an API to retrieve the required information.

[2065] Input: A request to a consumer database via an API.

[2066] Output: Store consumer purchase history, preferences, and feedback information in the server's internal data storage.

[2067] Step 4:

[2068] Data analysis

[2069] The server passes the collected inventory information, sales information, IoT information, and consumer information to the generative AI model for data analysis.

[2070] Specific actions

[2071] The server uses TensorFlow to identify products that are nearing their expiration date and generate discount offers.

[2072] Input: Inventory information, sales information, IoT information, consumer information.

[2073] Output: Products with upcoming expiration dates and discount information.

[2074] Step 5:

[2075] Discount information generation and notification

[2076] The server customizes the discount information for each consumer and sends an electronic notification to the consumer's terminal.

[2077] Specific actions

[2078] Use a Node.js server to send notifications using FCM (Firebase Cloud Messaging).

[2079] Input: Generated discount information, consumer's device information.

[2080] Output: A customized discount notification for each consumer.

[2081] Step 6:

[2082] Consumer food ingredient data entry

[2083] Users input the information about ingredients they have into the smartphone app.

[2084] Specific actions

[2085] The user enters the names and quantities of ingredients into the app's input form and clicks the submit button.

[2086] Input: Ingredient information entered by the consumer.

[2087] Output: The entered data is sent from the terminal to the server.

[2088] Step 7:

[2089] Creating a customized recipe

[2090] The server generates a customized recipe using a generative AI model based on the received user data.

[2091] Specific actions

[2092] The server uses a Python script to generate recipes that take into account the ingredients the user has and create additional ingredient lists.

[2093] Input: Ingredient information entered by the consumer.

[2094] Output: A customized recipe with an additional ingredients list.

[2095] Step 8:

[2096] View recipes and ingredient lists

[2097] The device displays the suggested recipe and a list of additional ingredients to the user.

[2098] Specific actions

[2099] The smartphone app displays the recipe information and ingredient list received from the server on its interface.

[2100] Input: Recipe information received from the server and additional ingredient list.

[2101] Output: Detailed information displayed for the user to see.

[2102] Step 9:

[2103] Shopping list and discount coupons provided

[2104] The server generates a shopping list of the necessary ingredients and provides corresponding discount coupons.

[2105] Specific actions

[2106] The server generates discount coupons for additional ingredients calculated by the generative AI model and adds them to the shopping list.

[2107] Input: Customized recipe information, additional ingredient information.

[2108] Output: Additional ingredients list and corresponding discount coupon.

[2109] Step 10:

[2110] View shopping list and coupons

[2111] The terminal displays a shopping list and discount coupons on the user's smartphone.

[2112] Specific actions

[2113] The app displays the shopping list and discount coupons received from the server on the screen for easy access by the user.

[2114] Input: Shopping list and discount coupons received from the server.

[2115] Output: Detailed information displayed for the user to see.

[2116] Step 11:

[2117] Collecting consumer evaluation data

[2118] After shopping, users enter the items they purchased and their ratings into the app.

[2119] Specific actions

[2120] Users fill out the evaluation form through the app interface and click the submit button.

[2121] Input: Rating information entered by the consumer.

[2122] Output: The entered rating information is sent from the terminal to the server.

[2123] Step 12:

[2124] Submitting evaluation data

[2125] The terminal transmits the collected evaluation data to the server.

[2126] Specific actions

[2127] The app sends rating data in JSON format to the server, communicating via HTTP POST requests.

[2128] Input: Rating information entered by the consumer.

[2129] Output: The rating data is sent to the server.

[2130] Step 13:

[2131] Database Update

[2132] The server updates the database based on the received evaluation information and reflects it in the next analysis and the provision of discount information.

[2133] Specific actions

[2134] The server analyzes the rating data and performs a process to update its internal database.

[2135] Input: Evaluation data.

[2136] Output: The updated database.

[2137] (Application example 1)

[2138] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2139] Conventional inventory management systems have difficulty efficiently identifying products with approaching expiration dates and providing appropriate discount information to consumers. Furthermore, few systems offer customized recipes based on the ingredients a consumer has, failing to improve the consumer's purchasing experience. Furthermore, there is a lack of a way to visually present product discount information, creating technical challenges for improving the work efficiency of store staff.

[2140] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[2141] In this invention, the server includes: means for collecting inventory data of retailers; means for collecting sales data; means for collecting environmental data using IoT sensors; means for collecting data such as consumer purchasing history and preferences; means for analyzing the inventory data, sales data, IoT sensor data, and consumer data using a generative AI model to identify products with upcoming expiration dates; means for generating discount information for the products with upcoming expiration dates; means for transmitting the discount information to each consumer; means for receiving data on ingredients owned by the consumer and generating customized recipes based thereon; means for generating an additional ingredient list and its discount information based on the recipes; means for providing the additional ingredient list and its discount information to the consumer; means for visually presenting the discount information on smart glasses or a head-mounted display; and means for collecting consumer feedback data and improving the analysis means based thereon. This enables efficient inventory management, providing appropriate discount information to consumers, generating customized recipes, and improving the work efficiency of store staff.

[2142] A "retailer" is a business that sells products directly to general consumers.

[2143] "Inventory data" refers to information relating to the quantity, storage location, expiration date, etc. of products in stores and warehouses.

[2144] "Sales data" refers to information relating to past sales history and sales.

[2145] An "IoT sensor" is a sensor device for collecting physical environmental data (e.g., temperature, humidity, location data, etc.).

[2146] "Environmental data" refers to data such as the temperature and humidity inside the store, and the location of shopping carts.

[2147] "Consumer data" refers to data such as consumer purchasing history, preferences, and feedback information.

[2148] A "generative AI model" is an artificial intelligence model that analyzes collected data and is generated to achieve a specific purpose.

[2149] "Discount information" is information such as the discount rate for a product.

[2150] A "customized recipe" is an individually optimized recipe generated based on the ingredients the consumer has on hand.

[2151] The "additional ingredient list" is a list showing the additional ingredients required and their quantities based on the generated recipe.

[2152] "Smart glasses or head-mounted display" refers to a wearable device for visually displaying information.

[2153] "Visually presented" means displaying information directly in the user's field of vision.

[2154] "Feedback data" refers to data such as ratings and post-purchase opinions provided by consumers.

[2155] This invention provides a system that allows retailers to improve the efficiency of inventory management in brick-and-mortar stores and provide appropriate discount information to consumers. The overall configuration and specific operation of the system are described below.

[2156] Overall system overview

[2157] The system mainly consists of a server, smart glasses or head-mounted displays (HMDs), and IoT sensors. The server collects and analyzes various data, generates the necessary information, and sends it to the smart devices. The smart devices then visually present the information to the user.

[2158] Data collection and analysis

[2159] The server accesses the retailer's database to periodically collect inventory and sales data. It also collects environmental data (temperature, humidity, location information) from IoT sensors installed in the store. It also accesses the consumer database to collect consumer purchasing history, preferences, and feedback information.

[2160] All collected data is passed to a generative AI model for analysis. The model identifies products that are close to expiring and generates discount offers for those products. For example, a 20% off coupon for tomatoes that are close to expiring will be set. The model also creates discount offers optimized for each consumer.

[2161] Discount information and feedback

[2162] Discount and stock information is visually presented to users using smart glasses or HMDs, such as Microsoft HoloLens or Google Glass. When users stand in front of a shelf, discount information for products is displayed on the device's transparent display.

[2163] It also generates recipes: users input the ingredients they have on hand, and the generative AI model generates the optimal recipe, calculating any additional ingredients needed and discount information, allowing consumers to shop efficiently.

[2164] After shopping, users can provide feedback through the app. The feedback data is sent to the server and reflected in the database. This will be used for future analysis and to provide discount information.

[2165] Specific examples

[2166] The server analyzes the store's inventory data and generates discount information such as a 20% discount on tomatoes with a best-by date of two days. This information is visually communicated to the user via smart glasses or an HMD. When the user inputs 300g of chicken breast and two cloves of garlic as ingredients on hand, the generative AI model suggests a recipe for "chicken breast and tomato pasta," and determines that "200g of pasta and two tablespoons of olive oil" are also required, generating discount information for this.

[2167] Prompt Sentence Examples

[2168] Consider a specific example of an AR application that assists in inventory management in a physical store. When approaching a shelf, the system displays discount information for products with an approaching expiration date in the field of view of smart glasses. Please explain in detail what kind of interface and notification method you could use.

[2169] In this way, the system of the present invention can improve the work efficiency of store staff and provide consumers with appropriate information in real time, thereby providing an efficient shopping experience.

[2170] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2171] Step 1:

[2172] The server accesses the retailer's database and collects inventory and sales data. This includes information such as product quantity, storage location, and expiration date. The inventory and sales data is periodically retrieved using an API. The input is the database query results, and the output is organized inventory and sales data stored in the server's memory.

[2173] Step 2:

[2174] The server collects environmental data from IoT sensors. Data such as temperature, humidity, and shopping cart location information is obtained from the IoT sensors via an API. The input is real-time data sent from the sensors, and the output is organized environmental data that is stored in the server's memory. Specifically, the server receives data sent from sensors in the store and converts it into an analyzable format.

[2175] Step 3:

[2176] The server accesses the consumer database to collect consumer purchasing history, preferences, and feedback information. The input is the query results from the consumer database, and the output is organized consumer data stored in the server's memory. Specifically, it obtains each consumer's past purchasing history and provided feedback in an analyzable format.

[2177] Step 4:

[2178] The server passes the collected inventory data, sales data, IoT sensor data, and consumer data to the generative AI model for analysis. All collected data is input, and the output is a list of products with upcoming expiration dates and their discount information. Specifically, the data is input into the AI ​​model, and discount information for specific products is generated as an analysis result. For example, a 20% off coupon is set for tomatoes with an expiration date in two days.

[2179] Step 5:

[2180] The server sends the generated discount information to each consumer's device. The input is the generated discount information, and the output is a notification sent to the consumer's device. Specifically, the discount coupon information is sent to the consumer's smartphone as an electronic notification.

[2181] Step 6:

[2182] The user opens the smartphone app and inputs the ingredients they have and the amount. The input is the ingredient data the user has, and the output is the ingredient data sent to the server. Specifically, the user enters the ingredient data into the app's input form and presses the send button, which sends the data to the server.

[2183] Step 7:

[2184] The server uses a generative AI model to generate customized recipes based on the received user data. The inputs are the user's ingredient data and inventory data, and the output is the generated recipe and a list of additional ingredients. Specifically, the ingredient data is input into the AI ​​model, which generates the optimal recipe and a list of required additional ingredients. For example, it proposes a recipe for "pasta with chicken breast and tomatoes" and determines that "200g of pasta and 2 tablespoons of olive oil" are required.

[2185] Step 8:

[2186] The server generates a shopping list of the necessary ingredients and assigns corresponding discount coupons. The input is a customized recipe and a list of additional ingredients, and the output is a shopping list with discount coupons. Specifically, the server sets discount information for the additional ingredients and generates the entire list.

[2187] Step 9:

[2188] The user can check the suggested recipe and additional ingredient list on their smartphone. The input is the recipe information sent from the server, and the output is the information displayed on the user's device. Specifically, the user opens the smartphone app to check the recipe and shopping list.

[2189] Step 10:

[2190] Users enter their post-shopping experience and product evaluation as feedback through a smartphone app. The input is the user's feedback data, and the output is feedback information sent to the server. Specifically, users enter their thoughts in the app's feedback form and press the send button.

[2191] Step 11:

[2192] The server analyzes the received feedback data and updates the database to reflect it in the next analysis and discount information provision. The input is the user's feedback data, and the output is an updated database. Specifically, the feedback information is input into the analysis model to improve the accuracy of future analysis algorithms.

[2193] The above processing steps enable efficient inventory management, appropriate discount information to consumers, and the creation of customized recipes.

[2194] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[2195] Overall system configuration

[2196] This system collects and analyzes retailers' inventory data, sales data, IoT sensor data, and consumer data, presents discount information for products with approaching expiration dates, and generates customized recipes based on the ingredients the consumer has and the user's emotional data. It also collects actual consumer purchasing data and feedback, and optimizes the system based on that data.

[2197] Program processing

[2198] Data collection

[2199] 1. Server

[2200] Access the retailer's database to collect inventory data. This includes information such as product quantity, storage location, and expiration date. For example, this includes data such as "Tomatoes: 10 units in stock, expiration date in 2 days."

[2201] Sales data is also collected in the same way. Past sales history, prices, purchaser information, etc. are obtained. For example, data such as "Tomatoes: 60 units sold in the past 30 days, an average of 2 units per day" is collected.

[2202] IoT sensors are used to collect in-store environmental data (temperature, humidity, shopping cart location, etc.). Specifically, data is collected for "in-store temperature: 20°C, humidity: 50%."

[2203] Access consumer databases to collect consumer purchasing history, preferences, and feedback information, such as "Consumer A: Purchased tomatoes 5 times."

[2204] Data analysis

[2205] 2. Server

[2206] All collected data is passed to a generative AI model for analysis. Here, products with an approaching expiration date are identified and discount information for those products is generated. For example, if "five tomatoes with an expiration date in two days" are identified, a "20% off discount coupon for tomatoes" is created.

[2207] Delivery of bargain information

[2208] 3. Server

[2209] Based on the analysis results, the system generates bargain information tailored to each individual consumer and sends an electronic notification to their device. For example, a notification will be sent to "Consumer A" that "Tomatoes are 20% off, with the expiration date in two days."

[2210] 4. Terminal

[2211] A notification will appear on the consumer's smartphone, for example, "Tomatoes 20% off, expiration date in 2 days."

[2212] User input collection and sentiment data collection

[2213] 5. Users

[2214] Open the smartphone app and enter the ingredients you have and the quantities. For example, enter "300g chicken breast, 2 cloves of garlic" into the app.

[2215] The app uses a built-in emotion engine to recognize the user's emotions (e.g., happy, tired, etc.).

[2216] 6. Terminal

[2217] The input food ingredient data and emotion data are sent to the server. For example, the data "300g chicken breast, 2 cloves of garlic" and "Emotion: fun" are sent to the server.

[2218] Creating a customized recipe

[2219] 7. Server

[2220] Based on the received user data and emotion data, the generative AI model generates customized recipes. For example, it determines that "it's okay if the cooking time is long because the user seems to enjoy it," and suggests a recipe such as "chicken breast and tomato pasta."

[2221] It calculates the additional ingredients needed and their quantities and generates a shopping list. For example, the list might include "200g pasta, 2 tablespoons olive oil."

[2222] 8. Terminal

[2223] The suggested recipe and a list of additional ingredients are displayed to the user, for example, "Chicken breast and tomato pasta, plus 200g pasta and 2 tablespoons olive oil."

[2224] Shopping list and discount coupons provided

[2225] 9. Server

[2226] Add discount coupons to a list of required additional ingredients, for example, "Pasta 200g - 10% off discount coupon, Tomato - 20% off discount coupon".

[2227] 10. Terminal

[2228] A shopping list and discount coupons are displayed on the user's smartphone, allowing them to shop efficiently.

[2229] Purchasing data feedback

[2230] 11. Users

[2231] After shopping, users enter their purchases and their ratings through the app. For example, they can provide feedback such as, "I bought five tomatoes, and the chicken breast and tomato pasta was delicious."

[2232] 12. Terminal

[2233] Send feedback data to the server. For example, "I bought 5 tomatoes and the pasta was delicious."

[2234] 13. Server

[2235] We use your feedback to update our database, which can be reflected in our next analysis and offer.

[2236] Specific examples

[2237] server

[2238] Collect inventory data for tomatoes from a retailer and identify that five of them have a best-by date two days later. This will qualify for a discount, so set up a 20% off coupon for the tomatoes.

[2239] Terminal

[2240] A notification appears on the user's smartphone saying, "Tomatoes 20% off, expiration date in 2 days."

[2241] User

[2242] You log in to the app and enter the information you have, such as "300g chicken breast and 2 cloves of garlic," and the emotion engine recognizes that you are in a happy state.

[2243] server

[2244] The received data is analyzed and a recipe for "chicken breast and tomato pasta" is suggested, with the additional recommendation of "200g of pasta and 2 tablespoons of olive oil."

[2245] Terminal

[2246] It displays suggested recipes and ingredient lists to users and allows them to take advantage of discount coupons.

[2247] User

[2248] After shopping, customers provide feedback, which is received by the server and updated in the database.

[2249] In this way, the system of the present invention can also utilize user emotional data to optimize inventory management for retailers and the consumer purchasing experience.

[2250] The processing flow will be explained below.

[2251] Step 1:

[2252] server

[2253] Access the retailer's database and collect inventory data. Specifically, obtain information such as product quantity, storage location, expiration date, etc. For example, obtain data such as "Tomatoes: 10 units in stock, expiration date in 2 days."

[2254] Step 2:

[2255] server

[2256] Collect sales data, such as past sales history, prices, and buyer information. For example, obtain data such as "Tomatoes: 60 units sold in the pa...

Claims

1. a means for collecting inventory data for retailers; a means of collecting sales data; A means of collecting environmental data using IoT sensors; A means of collecting data such as consumer purchasing history and preferences, A means for analyzing the inventory data, sales data, IoT sensor data, and consumer data to identify products that are close to their expiration date; means for generating discount information for the products whose expiration date is approaching; means for transmitting the discount information to each consumer; A means of receiving data on ingredients that consumers have and generating customized recipes based on that data; means for generating an additional ingredient list and discount information therefor based on the recipe; means for providing said additional ingredient list and discount information to consumers; means for collecting consumer feedback data and improving said analysis means based thereon; A system including:

2. 10. The system of claim 1, further comprising means for automatically generating discount coupons for products approaching their expiration date and sending electronic notifications to consumers.

3. 2. The system according to claim 1, further comprising means for calculating the necessary additional ingredients and their quantities based on the ingredient data input by the consumer, generating a shopping list, and providing discount information based on the list.

Images