system

Abstract

Provide a system. 【Solution means】 Means for obtaining and analyzing the purchase history of users, Means for obtaining and analyzing the location information of users, Means for generating individualized product recommendations based on the purchase history and the location information, Means for presenting the individualized product recommendations to users in real time, Means for obtaining price information of different providers regarding a product when the product is scanned or searched, Means for comparing the obtained price information and presenting the optimal purchase destination to users, Means for generating and presenting individualized coupons based on the purchase history and the location information, A system including the above.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] Consumers often spend a great deal of time when making product selections and price comparisons in their daily purchasing activities. Furthermore, it is difficult to grasp price differences and benefits between online and offline, and it is difficult to make product selections that suit one's preferences and needs. In addition, overspending due to impulse buying is a problem that many consumers have. There is a need to solve such inefficiencies and economic losses and provide a smarter and more efficient purchasing experience for consumers.

Means for Solving the Problems

[0005] This invention provides a system that generates and delivers personalized product recommendations to consumers in real time by analyzing electronic purchase history and location information. It also has a function to present consumers with the optimal purchase choice by acquiring and comparing price information from various sellers when scanning or searching for products. Furthermore, it supports economical and efficient shopping by generating and providing personalized coupons based on consumers' purchase history and location information. Thus, this invention comprehensively solves diverse consumer purchasing challenges by utilizing analysis technology based on AI models.

[0006] "User" refers to an individual or group that uses the system to obtain product information or engage in purchasing activities.

[0007] "Purchase history" refers to a detailed record of purchases made by the user in the past, and includes information such as product name, purchase date and time, store name, and payment amount.

[0008] "Location information" refers to data that indicates the geographical location where a user is currently or has been in the past, and is expressed in forms such as GPS coordinates or addresses.

[0009] "Personalized product recommendations" refer to a list of products and services suggested to a specific user based on their purchase history and preferences.

[0010] "Real-time" means that data generation and processing occur the moment they happen, and results are output almost instantly with virtually no delay.

[0011] "Provider" refers to companies or organizations that offer goods or services to the market, including online stores and physical stores.

[0012] A "coupon" is an electronic or paper voucher used to offer a discount on the price of a specific product or service.

[0013] A "generative artificial intelligence model" refers to a computer model formed by combining advanced machine learning algorithms primarily used for data analysis and prediction. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the 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.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0028] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention is a system for improving the user's purchasing experience and is realized through three main components: a server, a terminal, and the user. The server plays a central role in analyzing purchase history and location information collected from the user and generating personalized product recommendations and coupons.

[0036] The server maintains users' past purchase history in a database and periodically analyzes this data using an artificial intelligence model. Based on the analysis results, it understands users' consumption trends and recommends products best suited to their individual preferences and needs. Furthermore, by knowing the user's current location, the server can generate and provide geographically appropriate coupons in real time.

[0037] The terminal serves to display product recommendations and coupon information sent from the server to the user. Through the terminal, users can view products of interest and utilize offered coupons. When a user scans or searches for a specific product, the terminal instantly sends that information to the server, which then retrieves price information and suggests the best place to purchase it.

[0038] As a concrete example, when a user scans an item in a supermarket, the terminal sends the item information to a server. The server instantly retrieves price information from multiple providers, compares it, and then presents the most economical option for the user. Furthermore, any coupons that can be used at that time are also presented, allowing the user to shop more efficiently by utilizing those coupons.

[0039] The system according to the present invention can save users time and costs and provide a more satisfying purchasing experience.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The server collects each user's past purchase history data and stores it in a database. This includes information such as the date and time, purchased items, amount, and location of purchase.

[0043] Step 2:

[0044] The device obtains permission from the user to share location information and sends that information to the server.

[0045] Step 3:

[0046] The server uses the acquired purchase history and location information to perform data analysis using an AI model. This analysis identifies the user's purchasing patterns and preferences.

[0047] Step 4:

[0048] The server generates a product list tailored to the user based on the analysis results. This list reflects the user's past behavior and current trends.

[0049] Step 5:

[0050] The server sends the generated product list to the terminal and presents it to the user.

[0051] Step 6:

[0052] The user selects an item of interest from the presented product list and checks its details.

[0053] Step 7:

[0054] The device scans or searches for products the user is interested in and sends that information to the server.

[0055] Step 8:

[0056] The server instantly retrieves prices from multiple providers for the scanned or searched product and performs a comparative analysis.

[0057] Step 9:

[0058] The server returns the price analysis results to the terminal and presents the user with the best place to buy and its details.

[0059] Step 10:

[0060] The server generates relevant coupons based on the user's current location and purchase history, and provides them to the user via the terminal.

[0061] Step 11:

[0062] Based on the presented purchase information and coupons, the user selects and executes the most suitable purchase.

[0063] (Example 1)

[0064] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0065] While modern consumers have access to a wide variety of products, they spend a lot of time and effort finding products that suit their individual preferences. Furthermore, opportunities to identify the best place to buy in real time and to receive effective discounts and benefits to encourage purchases are limited. Therefore, there is a need to provide an efficient and personalized shopping experience.

[0066] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0067] In this invention, the server includes processing means for acquiring and analyzing the user's purchase history, processing means for acquiring and analyzing the user's geographical information, and processing means for generating personalized product suggestions based on the purchase history and geographical information. This makes it possible to efficiently recommend products based on the user's preferences and provide an optimal purchasing experience.

[0068] "Purchase history" refers to records of products that a user has purchased in the past, including the date and time of purchase, product name, price, and store information.

[0069] "Geographic information" refers to location data used to identify the user's current location, and is data obtained from GPS information and Wi-Fi location information.

[0070] "Product recommendations" refer to information that recommends products suitable for the user based on their purchase history and geographical information.

[0071] "Price information" refers to information about the price of a specific product, and includes multiple price data obtained from different suppliers.

[0072] "Supplier" refers to a business or store that provides goods or services.

[0073] A "discount service" is a monetary benefit that users can receive by meeting certain conditions, and includes coupons and promotions.

[0074] A "generative AI model" is an artificial intelligence method or system used to analyze user data and generate personalized product recommendations and other information.

[0075] This invention provides a system comprising a server, terminals, and users to improve the user's purchasing experience. The server collects and analyzes the user's purchase history and geographical information using specific hardware and software. The hardware used includes a high-performance database server, and the software includes a machine learning platform for generative AI models. The server continuously analyzes purchase history data and utilizes generative AI models to understand the user's consumption trends.

[0076] The server acquires users' geographical information in real time, for example, through GPS and location services, and uses this data to generate region-specific discount services. At the same time, it uses a generative AI model to provide personalized product recommendations. This makes it possible to recommend products based on the user's specific preferences in real time.

[0077] The terminals, configured as smartphones or dedicated devices, are responsible for presenting information from the server to the user. When a user scans a product using the terminal, they can instantly obtain detailed price information about that product. The server collects price information from different suppliers for each product and presents the best option.

[0078] For example, when a user scans for coffee at a supermarket, the server instantly retrieves price information from multiple suppliers and presents the user with the most economical option. It also displays discount coupons for cafes that can be used immediately, allowing the user to shop efficiently.

[0079] Examples of prompt messages include, "Recommend the best products for this user based on their most recent purchase history," and "Generate coupons available nearby based on their location."

[0080] This entire system utilizes generative artificial intelligence models and advanced analytical techniques to enable effective data processing and personalized services. This results in time and cost savings for users, and provides a more satisfying purchasing experience.

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] The server collects purchase history each time a user makes a purchase. Inputs include purchase data such as the date and time of purchase, product name, price, and store information. The server stores this data in a database and organizes it for each user. The purpose of properly storing the data is to make it available for later analysis.

[0084] Step 2:

[0085] The server acquires the user's location information. The input consists of GPS data and Wi-Fi location information provided by the user's smartphone or device. Based on this, the server identifies geographical patterns. The output is location data used to determine services and products suitable for the user's current location. This geographical data is updated in real time and forms the basis for providing optimal suggestions to the user.

[0086] Step 3:

[0087] The server uses a generative artificial intelligence (AI) model to analyze collected purchase history. Past purchase history from the database is used as input. The AI ​​model analyzes this data to understand the user's consumption trends and preferences. The output is a personalized product list tailored to the user. Based on the analysis results, more accurate product recommendations become possible.

[0088] Step 4:

[0089] The server generates personalized product recommendations and coupons based on the user's location information and product analysis results. The input consists of product recommendation data and location data generated by an AI model. The server integrates this data to create coupons and product recommendations tailored to the user. The output is product recommendation information and coupon data sent to the user's device.

[0090] Step 5:

[0091] The terminal displays product recommendations and coupon information sent from the server to the user. Input includes recommendation information and coupon data from the server. The terminal visually presents this information through a user interface, making it immediately available to the user. Output is a screen display designed to encourage the user's purchasing behavior.

[0092] Step 6:

[0093] When a user scans an item in a store, the terminal sends the item's barcode information to the server. The input is the barcode scan data generated by the user interaction. The server collects price information from multiple suppliers and presents the user with the most economical option. The output is the price comparison results and associated coupon information. This allows the user to purchase at the best possible price.

[0094] (Application Example 1)

[0095] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0096] Modern consumers demand efficient and personalized information when shopping in physical stores. However, they face the challenge of difficulty obtaining the most relevant product information, price comparisons, and discount information in real time at the time of purchase. Furthermore, the insufficient use of smart devices for purchasing support is also a problem.

[0097] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0098] In this invention, the server includes means for acquiring and analyzing the user's purchase history, means for acquiring and analyzing the user's current location information, and means for generating and immediately presenting personalized product suggestions. This enables consumers to efficiently select products in physical stores and make the most economical purchases.

[0099] "Purchase history" refers to records of goods and services that consumers have purchased in the past, and is data used to understand consumption patterns and preferences.

[0100] "Current location information" refers to data about the consumer's current location and is used to provide geographically relevant information.

[0101] "Personalized product recommendations" refer to product recommendations selected based on a consumer's individual purchase history and interests, providing the optimal choices to meet individual needs.

[0102] "Price information" refers to data on the prices offered by different suppliers for a particular item, and serves as a basis for consumers to make informed decisions about the best deals.

[0103] A "discount coupon" is a type of voucher that offers a discount when purchasing designated goods or services, thereby providing consumers with an economic benefit.

[0104] A "smartphone or mobile device" is a portable device with advanced computing capabilities and communication functions, enabling the use of various applications.

[0105] "Real-time" refers to a state where information processing and communication occur instantly without delay, allowing consumers to obtain the information they need at that moment.

[0106] A "generative model" is an algorithm or computational method used to generate new information or options based on data, and is a technology that is attracting particular attention in the field of artificial intelligence.

[0107] "Contrast" is the act of comparing multiple options or data to reveal their differences and characteristics, and is a technique that helps in making the optimal choice.

[0108] The system for carrying out this invention comprises a server, a terminal, and a user. The server acquires the user's purchase history and current location information and analyzes it using a generative AI model. This generates personalized product suggestions suitable for the user. Specifically, the server is built using Python or Node.js, and data is stored in MySQL® or MongoDB. For analysis, the generative model is implemented using TENSORFLOW® or PyTorch.

[0109] The terminal uses a smartphone or other portable device and presents necessary information to the user using front-end technologies such as React Native. On the terminal, the user scans a product to obtain barcode information and sends that information to the server. The server retrieves product prices from different suppliers and presents the user with the best option in real time.

[0110] For example, when a user scans milk in a store, the server collects price information and finds the most economical provider. The user can then use this information to make the best purchase. Furthermore, using discount coupons provided simultaneously allows for even more efficient shopping.

[0111] An example of a prompt message is, "Analyze user ID: 12345's purchase data for the past three months to generate new milk product recommendations." Such a system allows users to enjoy an efficient and personalized shopping experience in physical stores.

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The user scans the product using the camera on their device. The input is a barcode image captured by the camera, and the output is a recognized product ID. This product ID is matched against the product database, and the corresponding product information is sent to the server.

[0115] Step 2:

[0116] The server retrieves price information for the corresponding product from the database based on the received product ID. The input is the product ID, and price information from different suppliers in the database is retrieved. The output is a list of price information from each supplier.

[0117] Step 3:

[0118] The server analyzes the acquired price information using a generating AI model to calculate the optimal price. The input is a list of price information, and the output is information on the best place to buy. This calculation identifies the most economical option that meets the user's needs.

[0119] Step 4:

[0120] The server obtains the user's current location information and generates discount coupons usable at nearby stores. The input consists of the user's current location information and the results of an AI model analysis; the output is appropriate discount coupon information. The generated discount coupon information is then sent to the user's device.

[0121] Step 5:

[0122] The terminal displays optimal price information and discount coupon information received from the server to the user. Input is data from the server, and output is visually presented to the user through the terminal's user interface. This presentation allows the user to shop efficiently and effectively.

[0123] Step 6:

[0124] The user makes a purchase decision based on the information presented and applies a discount coupon using the terminal. The input is the user's selection, and the output is the final purchase information. This process allows the user to have a personalized shopping experience in a physical store.

[0125] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0126] This invention is a system that combines an emotion engine to improve the user's purchasing experience. The system consists of three main components: a server, a terminal, and an emotion engine. The server has the basic functions of collecting and analyzing the user's purchase history and location information, while also utilizing the user's emotional information obtained by the emotion engine.

[0127] The server collects users' past purchase history and location information as numerous data points and analyzes them in real time. In addition, the emotion engine estimates the user's emotions from voice input and biometric information and provides this information to the server. This emotion information is used to make personalized product recommendations and coupon offers more appropriate.

[0128] The device presents product recommendations and coupon information sent from the server to the user, reflecting their changing emotions. The device works in conjunction with an emotion engine, enabling it to offer emotionally appropriate product selections and benefits when the user shows interest in a product or when signs of hesitation are detected.

[0129] For example, if the emotion engine detects a user's stress while they are searching for products through their device, the server will enhance its recommendations for relaxing products based on this information. For instance, if a user shows signs of tension while browsing expensive items, the server will recommend lower-priced alternatives within the same category to make the shopping experience more comfortable. Furthermore, if excitement or joy is detected during the purchasing process, the server will offer immediately available discount coupons to encourage purchases.

[0130] Thus, the system of the present invention aims to enhance user satisfaction by incorporating user emotions into the feedback, providing a more refined and personalized purchasing experience.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] Users use a terminal to search for products or scan products within the store. The terminal then sends the scanned product information to the server.

[0134] Step 2:

[0135] The device inputs voice data and biometric information from the user into an emotion engine to analyze the user's emotions. The results of this analysis are sent to the server as information indicating the user's current emotional state.

[0136] Step 3:

[0137] Based on the received product information and the user's emotional state, the server generates a personalized product recommendation list, referencing past purchase history and location information. During this process, the recommendations are adjusted according to the user's emotional state.

[0138] Step 4:

[0139] The server sends the generated product recommendation list to the terminal, which then displays it to the user in real time.

[0140] Step 5:

[0141] If a user expresses interest in a presented product, the device requests the server for available pricing information about that product.

[0142] Step 6:

[0143] The server collects pricing information from different providers for a product and determines the best place to buy it. It also generates special discounts and coupons, taking into account the user's emotional state, as needed.

[0144] Step 7:

[0145] The server sends the best purchase source and special discount information to the terminal, which then presents this information to the user.

[0146] Step 8:

[0147] If the user decides to proceed with a purchase, the device will use the selected retailer to support the purchase process.

[0148] This entire process allows users to receive a customized purchasing experience tailored to their emotions and specific needs, leading to increased satisfaction.

[0149] (Example 2)

[0150] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0151] To provide a personalized purchasing experience, it is necessary to offer information that takes into account not only the user's past behavior but also their current emotional state. However, conventional systems lack product recommendations and coupon offers that reflect the user's emotions, which limits the potential for improving user satisfaction.

[0152] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0153] In this invention, the server includes means for analyzing the user's past purchase history and location information, means for estimating and acquiring emotional information from the user's voice and biometric information, and means for generating personalized product recommendations based on the said data. This makes it possible to provide a more precise and personalized purchasing experience.

[0154] "Purchase history" refers to data about products and services that a user has purchased in the past.

[0155] "Location information" refers to data about a user's current location or places they have visited in the past.

[0156] "Emotional information" refers to data on the emotional state estimated based on the user's voice and biometric information.

[0157] "Personalized product recommendations" refer to recommendations for products and services that are optimized for a specific user, generated based on the user's purchase history, location information, and sentiment information.

[0158] A "terminal" is an electronic device used by a user to receive information, and includes smartphones, tablets, and other similar devices.

[0159] A "supplier" refers to a company or business that provides specific goods or services.

[0160] "Generative AI technology" is an artificial intelligence technology that generates user-optimized recommendations and information based on diverse input data.

[0161] This system is built around a server, terminals, and an emotion engine to enhance the user's purchasing experience. The server collects and analyzes user purchase history and location information. It utilizes Python and SQL as data processing languages, and general-purpose MySQL or PostgreSQL as its database management system. To process data in real time, the server uses a widely used cloud service platform.

[0162] The device has the functionality to receive and display personalized product recommendations and coupons to the user. The device utilizes a mobile phone with the ANDROID® or iOS operating system and works in conjunction with an emotion engine. The emotion engine collects and analyzes voice and biometric information through input devices such as microphones and cameras. This allows it to estimate the user's emotions. AI technology is used in this process. For example, voice recognition software is used for voice processing, and an emotion analysis API is used for emotion estimation.

[0163] As a concrete example, suppose a user is considering purchasing an expensive item using their device, and the emotion engine detects the user's tension. In this case, the server recommends a cheaper item in the same category and also enhances the presentation with information on items that have a relaxing effect. Furthermore, if a situation is detected where the user's purchase intent is heightened, an immediate discount coupon is provided to encourage the user's purchasing behavior.

[0164] An example of a prompt message is, "Please describe the procedure for detecting changes in the user's emotions and recommending appropriate products based on their situation while searching for products online." In this way, the present invention is a system that can improve the user's purchasing experience by providing sophisticated product recommendations that take emotions into consideration.

[0165] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0166] Step 1:

[0167] The server retrieves users' purchase history and location information from a database. The retrieved data is then input into an analysis tool to extract characteristics such as purchase patterns and popular visited locations. This data processing uses Python for data cleaning and aggregation, resulting in organized information for each user.

[0168] Step 2:

[0169] The device inputs the user's voice and biometric information through sensors and transmits it to the emotion engine. The emotion engine estimates the user's current emotional state based on this information. Specifically, a machine learning model analyzes emotions from voice tone and facial expressions, and classifies the results into categories such as "relaxed" and "stressed."

[0170] Step 3:

[0171] The server combines the analysis results from Step 1 with the emotional information obtained in Step 2 and inputs it into the AI ​​model that generates product recommendations. This model creates a list of products that are most suitable for the user. For example, for a user who is experiencing an emotional state of tension, the model will generate recommendations that emphasize products that are expected to have a relaxing effect.

[0172] Step 4:

[0173] The server sends the generated product recommendation list to the terminal. The terminal uses its user interface to display customized product recommendations and coupons on the screen. This output includes promotional messages composed of text and images, designed to be easily understood visually by the user.

[0174] Step 5:

[0175] Users review product recommendations displayed on their devices and either select items they are interested in or use offered coupons. This selection information is sent back from the device to the server and updated as part of the user's behavior history. This feedback data is then used to improve the quality of future recommendations.

[0176] (Application Example 2)

[0177] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0178] Modern consumers demand diverse and personalized shopping experiences, but existing systems fail to adequately provide flexible product recommendations and coupons tailored to consumers' emotions and circumstances. Furthermore, physical stores struggle to provide real-time information that meets customer needs, resulting in a lack of efficient means to optimally stimulate purchasing intent.

[0179] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0180] In this invention, the server includes means for acquiring biometric information and estimating the user's emotions, means for generating personalized product recommendations based on the user's purchase history, location information, and emotional information, and means for adaptively displaying product recommendations and coupons via a smart device according to the emotions. This makes it possible to provide a flexible and personalized purchasing experience that is tailored to the user's emotions and circumstances.

[0181] "Users" refer to individuals who use the system and are the entities that provide purchase history and location information.

[0182] "Purchase history" refers to information that shows a record of products a user has purchased in the past and functions as an important data point for recommending products.

[0183] "Location information" refers to data that indicates the user's current location or a specific point, and is used to provide personalized services.

[0184] "Biometric information" refers to data that indicates the user's physical state and reactions, and is used for emotion estimation.

[0185] "Emotional information" refers to data that indicates the emotional state of users and is used for product recommendations and coupon provision.

[0186] "Product recommendations" are product options presented to users, and are personalized based on purchase history, location information, and sentiment information.

[0187] A "coupon" is a voucher or code used to offer discounts or benefits to users, and it plays a role in stimulating purchasing intent.

[0188] A "smart device" is a device capable of processing and displaying electronic data, and functions as an interface within this system.

[0189] "Product recognition" is the process of identifying a specific product and obtaining information based on its characteristics.

[0190] A "supplier" is an entity that provides or sells goods and is the source of price information.

[0191] The system implementing this invention mainly consists of three elements: a server, a terminal, and an emotion engine. This system collects the user's purchase history, location information, and biometric information through a smart device worn by the user. The server uses a generative AI model based on this data to estimate the user's emotional information.

[0192] The server analyzes the data by linking it with acquired purchase history and location information to generate personalized product recommendations and coupons. This analysis utilizes machine learning frameworks such as TensorFlow and PyTorch, which run on Google Cloud or Amazon Web Services. The generated product recommendations and coupons are sent to the user's smart device in real time, and are displayed adaptively according to their emotions.

[0193] The device functions as an interface to provide the user with this received information visually and audibly. Smart glasses and smartphones fulfill this role, displaying information at appropriate times in response to changes in the user's emotions. Augmented reality (AR) technology may also be used for this display.

[0194] As a concrete example, if tension is detected when a customer picks up a specific product in a physical store, the system will recommend another product within that category that offers better value for money. The AI ​​model generated in this case operates using the following prompt messages.

[0195] Example prompt: "Generate a list of products that can help you relax, based on your recent purchase history and current emotional state, 'Tension'."

[0196] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0197] Step 1:

[0198] The server receives biometric information, purchase history, and location information from smart devices. This information is used as input. A data collection module acquires this data in real time and stores it directly in the database.

[0199] Step 2:

[0200] The server analyzes biometric information to estimate the user's emotional state. Based on the input biometric information, an emotion engine operates and outputs the current emotion through voice and image analysis. For example, emotions such as tension and joy are identified by the AI ​​model.

[0201] Step 3:

[0202] The server generates personalized product recommendations based on purchase history, location information, and estimated sentiment information. The AI ​​recommendation engine receives this data as input, analyzes it using TensorFlow, and outputs a list of products best suited to the user. In this process, the generating AI model uses the prompt statement "Generate recommended products based on recent purchase history and current sentiment 'XX'."

[0203] Step 4:

[0204] The server generates product recommendations and coupons, then sends them to the device. The transmitted data arrives at the device and is then provided to the user visually or audibly. For example, product information might be displayed on smart glasses using augmented reality (AR) technology.

[0205] Step 5:

[0206] Users make purchasing decisions based on the presented product information and coupons. The system confirms the user's output action of selecting a product via their smart device, and the device resends that selection to the server, recording it as purchase data.

[0207] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0208] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0209] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0210] [Second Embodiment]

[0211] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0212] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0213] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0214] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0215] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0216] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0217] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0218] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0219] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0220] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0221] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0222] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0223] This invention is a system for improving the user's purchasing experience and is realized through three main components: a server, a terminal, and the user. The server plays a central role in analyzing purchase history and location information collected from the user and generating personalized product recommendations and coupons.

[0224] The server maintains users' past purchase history in a database and periodically analyzes this data using an artificial intelligence model. Based on the analysis results, it understands users' consumption trends and recommends products best suited to their individual preferences and needs. Furthermore, by knowing the user's current location, the server can generate and provide geographically appropriate coupons in real time.

[0225] The terminal serves to display product recommendations and coupon information sent from the server to the user. Through the terminal, users can view products of interest and utilize offered coupons. When a user scans or searches for a specific product, the terminal instantly sends that information to the server, which then retrieves price information and suggests the best place to purchase it.

[0226] As a concrete example, when a user scans an item in a supermarket, the terminal sends the item information to a server. The server instantly retrieves price information from multiple providers, compares it, and then presents the most economical option for the user. Furthermore, any coupons that can be used at that time are also presented, allowing the user to shop more efficiently by utilizing those coupons.

[0227] The system according to the present invention can save users time and costs and provide a more satisfying purchasing experience.

[0228] The following describes the processing flow.

[0229] Step 1:

[0230] The server collects each user's past purchase history data and stores it in a database. This includes information such as the date and time, purchased items, amount, and location of purchase.

[0231] Step 2:

[0232] The device obtains permission from the user to share location information and sends that information to the server.

[0233] Step 3:

[0234] The server uses the acquired purchase history and location information to perform data analysis using an AI model. This analysis identifies the user's purchasing patterns and preferences.

[0235] Step 4:

[0236] The server generates a product list tailored to the user based on the analysis results. This list reflects the user's past behavior and current trends.

[0237] Step 5:

[0238] The server sends the generated product list to the terminal and presents it to the user.

[0239] Step 6:

[0240] The user selects an item of interest from the presented product list and checks its details.

[0241] Step 7:

[0242] The device scans or searches for products the user is interested in and sends that information to the server.

[0243] Step 8:

[0244] The server instantly retrieves prices from multiple providers for the scanned or searched product and performs a comparative analysis.

[0245] Step 9:

[0246] The server returns the price analysis results to the terminal and presents the user with the best place to buy and its details.

[0247] Step 10:

[0248] The server generates relevant coupons based on the user's current location and purchase history, and provides them to the user via the terminal.

[0249] Step 11:

[0250] Based on the presented purchase information and coupons, the user selects and executes the most suitable purchase.

[0251] (Example 1)

[0252] Next, we will describe Example 1. 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."

[0253] While modern consumers have access to a wide variety of products, they spend a lot of time and effort finding products that suit their individual preferences. Furthermore, opportunities to identify the best place to buy in real time and to receive effective discounts and benefits to encourage purchases are limited. Therefore, there is a need to provide an efficient and personalized shopping experience.

[0254] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0255] In this invention, the server includes processing means for acquiring and analyzing the user's purchase history, processing means for acquiring and analyzing the user's geographical information, and processing means for generating personalized product suggestions based on the purchase history and geographical information. This makes it possible to efficiently recommend products based on the user's preferences and provide an optimal purchasing experience.

[0256] "Purchase history" refers to records of products that a user has purchased in the past, including the date and time of purchase, product name, price, and store information.

[0257] "Geographic information" refers to location data used to identify the user's current location, and is data obtained from GPS information and Wi-Fi location information.

[0258] "Product recommendations" refer to information that recommends products suitable for the user based on their purchase history and geographical information.

[0259] "Price information" refers to information about the price of a specific product, and includes multiple price data obtained from different suppliers.

[0260] "Supplier" refers to a business or store that provides goods or services.

[0261] A "discount service" is a monetary benefit that users can receive by meeting certain conditions, and includes coupons and promotions.

[0262] A "generative AI model" is an artificial intelligence method or system used to analyze user data and generate personalized product recommendations and other information.

[0263] This invention provides a system comprising a server, terminals, and users to improve the user's purchasing experience. The server collects and analyzes the user's purchase history and geographical information using specific hardware and software. The hardware used includes a high-performance database server, and the software includes a machine learning platform for generative AI models. The server continuously analyzes purchase history data and utilizes generative AI models to understand the user's consumption trends.

[0264] The server acquires users' geographical information in real time, for example, through GPS and location services, and uses this data to generate region-specific discount services. At the same time, it uses a generative AI model to provide personalized product recommendations. This makes it possible to recommend products based on the user's specific preferences in real time.

[0265] The terminals, configured as smartphones or dedicated devices, are responsible for presenting information from the server to the user. When a user scans a product using the terminal, they can instantly obtain detailed price information about that product. The server collects price information from different suppliers for each product and presents the best option.

[0266] For example, when a user scans for coffee at a supermarket, the server instantly retrieves price information from multiple suppliers and presents the user with the most economical option. It also displays discount coupons for cafes that can be used immediately, allowing the user to shop efficiently.

[0267] Examples of prompt messages include, "Recommend the best products for this user based on their most recent purchase history," and "Generate coupons available nearby based on their location."

[0268] This entire system utilizes generative artificial intelligence models and advanced analytical techniques to enable effective data processing and personalized services. This results in time and cost savings for users, and provides a more satisfying purchasing experience.

[0269] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0270] Step 1:

[0271] The server collects purchase history each time a user makes a purchase. Inputs include purchase data such as the date and time of purchase, product name, price, and store information. The server stores this data in a database and organizes it for each user. The purpose of properly storing the data is to make it available for later analysis.

[0272] Step 2:

[0273] The server acquires the user's location information. The input consists of GPS data and Wi-Fi location information provided by the user's smartphone or device. Based on this, the server identifies geographical patterns. The output is location data used to determine services and products suitable for the user's current location. This geographical data is updated in real time and forms the basis for providing optimal suggestions to the user.

[0274] Step 3:

[0275] The server uses a generative artificial intelligence (AI) model to analyze collected purchase history. Past purchase history from the database is used as input. The AI ​​model analyzes this data to understand the user's consumption trends and preferences. The output is a personalized product list tailored to the user. Based on the analysis results, more accurate product recommendations become possible.

[0276] Step 4:

[0277] The server generates personalized product recommendations and coupons based on the user's location information and product analysis results. The input consists of product recommendation data and location data generated by an AI model. The server integrates this data to create coupons and product recommendations tailored to the user. The output is product recommendation information and coupon data sent to the user's device.

[0278] Step 5:

[0279] The terminal displays product recommendations and coupon information sent from the server to the user. Input includes recommendation information and coupon data from the server. The terminal visually presents this information through a user interface, making it immediately available to the user. Output is a screen display designed to encourage the user's purchasing behavior.

[0280] Step 6:

[0281] When a user scans a product in a store, the terminal sends the barcode information of the product to the server. The input is barcode scan data through user interaction. The server collects price information from multiple suppliers and presents the most economical option to the user. The output is the price comparison result and related coupon information. This enables the user to make a purchase at the best price.

[0282] (Application Example 1)

[0283] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0284] Modern consumers seek efficient and personalized information provision in shopping at physical stores. However, there is a problem that it is difficult to obtain optimal product information, price comparison, and discount information in real-time at the time of purchase. Also, the lack of purchase support utilizing smart devices is an issue.

[0285] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0286] In this invention, the server includes means for obtaining and analyzing the purchase history of the user, means for obtaining and analyzing the current location information of the user, and means for generating and immediately presenting personalized product proposals. This enables consumers to efficiently select products in physical stores and make the most economical purchases.

[0287] The "purchase history" is a record of products and services purchased by consumers in the past, and is data for grasping consumption patterns and preferences.

[0288] The "current location information" is data regarding the location where the consumer is currently located, and is used to provide geographical related information.

[0289] "Personalized product recommendations" refer to product recommendations selected based on a consumer's individual purchase history and interests, providing the optimal choices to meet individual needs.

[0290] "Price information" refers to data on the prices offered by different suppliers for a particular item, and serves as a basis for consumers to make informed decisions about the best deals.

[0291] A "discount coupon" is a type of voucher that offers a discount when purchasing designated goods or services, thereby providing consumers with an economic benefit.

[0292] A "smartphone or mobile device" is a portable device with advanced computing capabilities and communication functions, enabling the use of various applications.

[0293] "Real-time" refers to a state where information processing and communication occur instantly without delay, allowing consumers to obtain the information they need at that moment.

[0294] A "generative model" is an algorithm or computational method used to generate new information or options based on data, and is a technology that is attracting particular attention in the field of artificial intelligence.

[0295] "Contrast" is the act of comparing multiple options or data to reveal their differences and characteristics, and is a technique that helps in making the optimal choice.

[0296] The system for implementing this invention comprises a server, a terminal, and a user. The server acquires the user's purchase history and current location information and analyzes it using a generative AI model. This generates personalized product suggestions suitable for the user. Specifically, the server is built using Python or Node.js, and the data is stored in MySQL or MongoDB. TensorFlow or PyTorch is used to implement the generative model for analysis.

[0297] The terminal uses a smartphone or other portable device and presents necessary information to the user using front-end technologies such as React Native. On the terminal, the user scans a product to obtain barcode information and sends that information to the server. The server retrieves product prices from different suppliers and presents the user with the best option in real time.

[0298] For example, when a user scans milk in a store, the server collects price information and finds the most economical provider. The user can then use this information to make the best purchase. Furthermore, using discount coupons provided simultaneously allows for even more efficient shopping.

[0299] An example of a prompt message is, "Analyze user ID: 12345's purchase data for the past three months to generate new milk product recommendations." Such a system allows users to enjoy an efficient and personalized shopping experience in physical stores.

[0300] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0301] Step 1:

[0302] The user scans the product using the camera on their device. The input is a barcode image captured by the camera, and the output is a recognized product ID. This product ID is matched against the product database, and the corresponding product information is sent to the server.

[0303] Step 2:

[0304] The server retrieves price information for the corresponding product from the database based on the received product ID. The input is the product ID, and price information from different suppliers in the database is retrieved. The output is a list of price information from each supplier.

[0305] Step 3:

[0306] The server analyzes the acquired price information using the generation AI model and calculates the optimal price. The input is a list of price information, and the output is information on the optimal purchase destination. Through this operation, the most economical option according to the user's needs is identified.

[0307] Step 4:

[0308] The server acquires the user's current location information and generates discount coupons available at nearby stores. The input is the user's current location information and the analysis result by the AI model, and appropriate discount coupon information is generated as the output. The generated discount coupon information is transmitted to the user's terminal.

[0309] Step 5:

[0310] The terminal presents the optimal price information and discount coupon information received from the server to the user. The input is data from the server, and the output is visually presented to the user through the user interface of the terminal. Through this presentation, the user can shop efficiently and effectively.

[0311] Step 6:

[0312] The user determines the purchase based on the presented information and applies the discount coupon using the terminal. The input is the user's selection, and the output is the final purchase information. Through this process, the user can obtain a personalized purchase experience at the physical store.

[0313] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0314] This invention is a system that combines an emotion engine to improve the user's purchasing experience. The system consists of three main components: a server, a terminal, and an emotion engine. The server has the basic functions of collecting and analyzing the user's purchase history and location information, while also utilizing the user's emotional information obtained by the emotion engine.

[0315] The server collects users' past purchase history and location information as numerous data points and analyzes them in real time. In addition, the emotion engine estimates the user's emotions from voice input and biometric information and provides this information to the server. This emotion information is used to make personalized product recommendations and coupon offers more appropriate.

[0316] The device presents product recommendations and coupon information sent from the server to the user, reflecting their changing emotions. The device works in conjunction with an emotion engine, enabling it to offer emotionally appropriate product selections and benefits when the user shows interest in a product or when signs of hesitation are detected.

[0317] For example, if the emotion engine detects a user's stress while they are searching for products through their device, the server will enhance its recommendations for relaxing products based on this information. For instance, if a user shows signs of tension while browsing expensive items, the server will recommend lower-priced alternatives within the same category to make the shopping experience more comfortable. Furthermore, if excitement or joy is detected during the purchasing process, the server will offer immediately available discount coupons to encourage purchases.

[0318] Thus, the system of the present invention aims to enhance user satisfaction by incorporating user emotions into the feedback, providing a more refined and personalized purchasing experience.

[0319] The following describes the processing flow.

[0320] Step 1:

[0321] Users use a terminal to search for products or scan products within the store. The terminal then sends the scanned product information to the server.

[0322] Step 2:

[0323] The device inputs voice data and biometric information from the user into an emotion engine to analyze the user's emotions. The results of this analysis are sent to the server as information indicating the user's current emotional state.

[0324] Step 3:

[0325] Based on the received product information and the user's emotional state, the server generates a personalized product recommendation list, referencing past purchase history and location information. During this process, the recommendations are adjusted according to the user's emotional state.

[0326] Step 4:

[0327] The server sends the generated product recommendation list to the terminal, which then displays it to the user in real time.

[0328] Step 5:

[0329] If a user expresses interest in a presented product, the device requests the server for available pricing information about that product.

[0330] Step 6:

[0331] The server collects pricing information from different providers for a product and determines the best place to buy it. It also generates special discounts and coupons, taking into account the user's emotional state, as needed.

[0332] Step 7:

[0333] The server sends the best purchase source and special discount information to the terminal, which then presents this information to the user.

[0334] Step 8:

[0335] If the user decides to proceed with a purchase, the device will use the selected retailer to support the purchase process.

[0336] This entire process allows users to receive a customized purchasing experience tailored to their emotions and specific needs, leading to increased satisfaction.

[0337] (Example 2)

[0338] Next, we will describe Example 2. 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".

[0339] To provide a personalized purchasing experience, it is necessary to offer information that takes into account not only the user's past behavior but also their current emotional state. However, conventional systems lack product recommendations and coupon offers that reflect the user's emotions, which limits the potential for improving user satisfaction.

[0340] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0341] In this invention, the server includes means for analyzing the user's past purchase history and location information, means for estimating and acquiring emotional information from the user's voice and biometric information, and means for generating personalized product recommendations based on the said data. This makes it possible to provide a more precise and personalized purchasing experience.

[0342] "Purchase history" refers to data about products and services that a user has purchased in the past.

[0343] "Location information" refers to data about a user's current location or places they have visited in the past.

[0344] "Emotional information" refers to data on the emotional state estimated based on the user's voice and biometric information.

[0345] "Personalized product recommendations" refer to recommendations for products and services that are optimized for a specific user, generated based on the user's purchase history, location information, and sentiment information.

[0346] A "terminal" is an electronic device used by a user to receive information, and includes smartphones, tablets, and other similar devices.

[0347] A "supplier" refers to a company or business that provides specific goods or services.

[0348] "Generative AI technology" is an artificial intelligence technology that generates user-optimized recommendations and information based on diverse input data.

[0349] This system is built around a server, terminals, and an emotion engine to enhance the user's purchasing experience. The server collects and analyzes user purchase history and location information. It utilizes Python and SQL as data processing languages, and general-purpose MySQL or PostgreSQL as its database management system. To process data in real time, the server uses a widely used cloud service platform.

[0350] The device has the function of receiving and displaying personalized product recommendations and coupons to the user. The device utilizes a mobile phone with either an Android or iOS operating system and works in conjunction with an emotion engine. The emotion engine collects and analyzes voice and biometric information through input devices such as microphones and cameras. This is used to estimate the user's emotions. AI technology is used in this process. For example, voice recognition software is used for voice processing, and an emotion analysis API is used for emotion estimation.

[0351] As a concrete example, suppose a user is considering purchasing an expensive item using their device, and the emotion engine detects the user's tension. In this case, the server recommends a cheaper item in the same category and also enhances the presentation with information on items that have a relaxing effect. Furthermore, if a situation is detected where the user's purchase intent is heightened, an immediate discount coupon is provided to encourage the user's purchasing behavior.

[0352] An example of a prompt message is, "Please describe the procedure for detecting changes in the user's emotions and recommending appropriate products based on their situation while searching for products online." In this way, the present invention is a system that can improve the user's purchasing experience by providing sophisticated product recommendations that take emotions into consideration.

[0353] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0354] Step 1:

[0355] The server retrieves users' purchase history and location information from a database. The retrieved data is then input into an analysis tool to extract characteristics such as purchase patterns and popular visited locations. This data processing uses Python for data cleaning and aggregation, resulting in organized information for each user.

[0356] Step 2:

[0357] The device inputs the user's voice and biometric information through sensors and transmits it to the emotion engine. The emotion engine estimates the user's current emotional state based on this information. Specifically, a machine learning model analyzes emotions from voice tone and facial expressions, and classifies the results into categories such as "relaxed" and "stressed."

[0358] Step 3:

[0359] The server combines the analysis results from Step 1 with the emotional information obtained in Step 2 and inputs it into the AI ​​model that generates product recommendations. This model creates a list of products that are most suitable for the user. For example, for a user who is experiencing an emotional state of tension, the model will generate recommendations that emphasize products that are expected to have a relaxing effect.

[0360] Step 4:

[0361] The server sends the generated product recommendation list to the terminal. The terminal uses its user interface to display customized product recommendations and coupons on the screen. This output includes promotional messages composed of text and images, designed to be easily understood visually by the user.

[0362] Step 5:

[0363] Users review product recommendations displayed on their devices and either select items they are interested in or use offered coupons. This selection information is sent back from the device to the server and updated as part of the user's behavior history. This feedback data is then used to improve the quality of future recommendations.

[0364] (Application Example 2)

[0365] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0366] Modern consumers demand diverse and personalized shopping experiences, but existing systems fail to adequately provide flexible product recommendations and coupons tailored to consumers' emotions and circumstances. Furthermore, physical stores struggle to provide real-time information that meets customer needs, resulting in a lack of efficient means to optimally stimulate purchasing intent.

[0367] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0368] In this invention, the server includes means for acquiring biometric information and estimating the user's emotions, means for generating personalized product recommendations based on the user's purchase history, location information, and emotional information, and means for adaptively displaying product recommendations and coupons via a smart device according to the emotions. This makes it possible to provide a flexible and personalized purchasing experience that is tailored to the user's emotions and circumstances.

[0369] "Users" refer to individuals who use the system and are the entities that provide purchase history and location information.

[0370] "Purchase history" refers to information that shows a record of products a user has purchased in the past and functions as an important data point for recommending products.

[0371] "Location information" refers to data that indicates the user's current location or a specific point, and is used to provide personalized services.

[0372] "Biometric information" refers to data that indicates the user's physical state and reactions, and is used for emotion estimation.

[0373] "Emotional information" refers to data that indicates the emotional state of users and is used for product recommendations and coupon provision.

[0374] "Product recommendations" are product options presented to users, and are personalized based on purchase history, location information, and sentiment information.

[0375] A "coupon" is a voucher or code used to offer discounts or benefits to users, and it plays a role in stimulating purchasing intent.

[0376] A "smart device" is a device capable of processing and displaying electronic data, and functions as an interface within this system.

[0377] "Product recognition" is the process of identifying a specific product and obtaining information based on its characteristics.

[0378] A "supplier" is an entity that provides or sells goods and is the source of price information.

[0379] The system implementing this invention mainly consists of three elements: a server, a terminal, and an emotion engine. This system collects the user's purchase history, location information, and biometric information through a smart device worn by the user. The server uses a generative AI model based on this data to estimate the user's emotional information.

[0380] The server analyzes the data by linking it with acquired purchase history and location information to generate personalized product recommendations and coupons. This analysis utilizes machine learning frameworks such as TensorFlow and PyTorch, which run on Google Cloud and Amazon Web Services. The generated product recommendations and coupons are sent to the user's smart device in real time, and are displayed adaptively according to their emotions.

[0381] The device functions as an interface to provide the user with this received information visually and audibly. Smart glasses and smartphones fulfill this role, displaying information at appropriate times in response to changes in the user's emotions. Augmented reality (AR) technology may also be used for this display.

[0382] As a concrete example, if tension is detected when a customer picks up a specific product in a physical store, the system will recommend another product within that category that offers better value for money. The AI ​​model generated in this case operates using the following prompt messages.

[0383] Example prompt: "Generate a list of products that can help you relax, based on your recent purchase history and current emotional state, 'Tension'."

[0384] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0385] Step 1:

[0386] The server receives biometric information, purchase history, and location information from smart devices. This information is used as input. A data collection module acquires this data in real time and stores it directly in the database.

[0387] Step 2:

[0388] The server analyzes biometric information to estimate the user's emotional state. Based on the input biometric information, an emotion engine operates and outputs the current emotion through voice and image analysis. For example, emotions such as tension and joy are identified by the AI ​​model.

[0389] Step 3:

[0390] The server generates personalized product recommendations based on purchase history, location information, and estimated sentiment information. The AI ​​recommendation engine receives this data as input, analyzes it using TensorFlow, and outputs a list of products best suited to the user. In this process, the generating AI model uses the prompt statement "Generate recommended products based on recent purchase history and current sentiment 'XX'."

[0391] Step 4:

[0392] The server generates product recommendations and coupons, then sends them to the device. The transmitted data arrives at the device and is then provided to the user visually or audibly. For example, product information might be displayed on smart glasses using augmented reality (AR) technology.

[0393] Step 5:

[0394] Users make purchasing decisions based on the presented product information and coupons. The system confirms the user's output action of selecting a product via their smart device, and the device resends that selection to the server, recording it as purchase data.

[0395] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0396] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0397] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0398] [Third Embodiment]

[0399] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0400] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0401] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0402] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0403] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0404] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0405] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0406] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0407] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0408] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0409] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0410] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0411] This invention is a system for improving the user's purchasing experience and is realized through three main components: a server, a terminal, and the user. The server plays a central role in analyzing purchase history and location information collected from the user and generating personalized product recommendations and coupons.

[0412] The server maintains users' past purchase history in a database and periodically analyzes this data using an artificial intelligence model. Based on the analysis results, it understands users' consumption trends and recommends products best suited to their individual preferences and needs. Furthermore, by knowing the user's current location, the server can generate and provide geographically appropriate coupons in real time.

[0413] The terminal serves to display product recommendations and coupon information sent from the server to the user. Through the terminal, users can view products of interest and utilize offered coupons. When a user scans or searches for a specific product, the terminal instantly sends that information to the server, which then retrieves price information and suggests the best place to purchase it.

[0414] As a concrete example, when a user scans an item in a supermarket, the terminal sends the item information to a server. The server instantly retrieves price information from multiple providers, compares it, and then presents the most economical option for the user. Furthermore, any coupons that can be used at that time are also presented, allowing the user to shop more efficiently by utilizing those coupons.

[0415] The system according to the present invention can save users time and costs and provide a more satisfying purchasing experience.

[0416] The following describes the processing flow.

[0417] Step 1:

[0418] The server collects each user's past purchase history data and stores it in a database. This includes information such as the date and time, purchased items, amount, and location of purchase.

[0419] Step 2:

[0420] The device obtains permission from the user to share location information and sends that information to the server.

[0421] Step 3:

[0422] The server uses the acquired purchase history and location information to perform data analysis using an AI model. This analysis identifies the user's purchasing patterns and preferences.

[0423] Step 4:

[0424] The server generates a product list tailored to the user based on the analysis results. This list reflects the user's past behavior and current trends.

[0425] Step 5:

[0426] The server sends the generated product list to the terminal and presents it to the user.

[0427] Step 6:

[0428] The user selects an item of interest from the presented product list and checks its details.

[0429] Step 7:

[0430] The device scans or searches for products the user is interested in and sends that information to the server.

[0431] Step 8:

[0432] The server instantly retrieves prices from multiple providers for the scanned or searched product and performs a comparative analysis.

[0433] Step 9:

[0434] The server returns the price analysis results to the terminal and presents the user with the best place to buy and its details.

[0435] Step 10:

[0436] The server generates relevant coupons based on the user's current location and purchase history, and provides them to the user via the terminal.

[0437] Step 11:

[0438] Based on the presented purchase information and coupons, the user selects and executes the most suitable purchase.

[0439] (Example 1)

[0440] Next, we will describe Example 1. 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."

[0441] While modern consumers have access to a wide variety of products, they spend a lot of time and effort finding products that suit their individual preferences. Furthermore, opportunities to identify the best place to buy in real time and to receive effective discounts and benefits to encourage purchases are limited. Therefore, there is a need to provide an efficient and personalized shopping experience.

[0442] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0443] In this invention, the server includes processing means for acquiring and analyzing the user's purchase history, processing means for acquiring and analyzing the user's geographical information, and processing means for generating personalized product suggestions based on the purchase history and geographical information. This makes it possible to efficiently recommend products based on the user's preferences and provide an optimal purchasing experience.

[0444] "Purchase history" refers to records of products that a user has purchased in the past, including the date and time of purchase, product name, price, and store information.

[0445] "Geographic information" refers to location data used to identify the user's current location, and is data obtained from GPS information and Wi-Fi location information.

[0446] "Product recommendations" refer to information that recommends products suitable for the user based on their purchase history and geographical information.

[0447] "Price information" refers to information about the price of a specific product, and includes multiple price data obtained from different suppliers.

[0448] "Supplier" refers to a business or store that provides goods or services.

[0449] A "discount service" is a monetary benefit that users can receive by meeting certain conditions, and includes coupons and promotions.

[0450] A "generative AI model" is an artificial intelligence method or system used to analyze user data and generate personalized product recommendations and other information.

[0451] This invention provides a system comprising a server, terminals, and users to improve the user's purchasing experience. The server collects and analyzes the user's purchase history and geographical information using specific hardware and software. The hardware used includes a high-performance database server, and the software includes a machine learning platform for generative AI models. The server continuously analyzes purchase history data and utilizes generative AI models to understand the user's consumption trends.

[0452] The server acquires users' geographical information in real time, for example, through GPS and location services, and uses this data to generate region-specific discount services. At the same time, it uses a generative AI model to provide personalized product recommendations. This makes it possible to recommend products based on the user's specific preferences in real time.

[0453] The terminals, configured as smartphones or dedicated devices, are responsible for presenting information from the server to the user. When a user scans a product using the terminal, they can instantly obtain detailed price information about that product. The server collects price information from different suppliers for each product and presents the best option.

[0454] For example, when a user scans for coffee at a supermarket, the server instantly retrieves price information from multiple suppliers and presents the user with the most economical option. It also displays discount coupons for cafes that can be used immediately, allowing the user to shop efficiently.

[0455] Examples of prompt messages include, "Recommend the best products for this user based on their most recent purchase history," and "Generate coupons available nearby based on their location."

[0456] This entire system utilizes generative artificial intelligence models and advanced analytical techniques to enable effective data processing and personalized services. This results in time and cost savings for users, and provides a more satisfying purchasing experience.

[0457] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0458] Step 1:

[0459] The server collects purchase history each time a user makes a purchase. Inputs include purchase data such as the date and time of purchase, product name, price, and store information. The server stores this data in a database and organizes it for each user. The purpose of properly storing the data is to make it available for later analysis.

[0460] Step 2:

[0461] The server acquires the user's location information. The input consists of GPS data and Wi-Fi location information provided by the user's smartphone or device. Based on this, the server identifies geographical patterns. The output is location data used to determine services and products suitable for the user's current location. This geographical data is updated in real time and forms the basis for providing optimal suggestions to the user.

[0462] Step 3:

[0463] The server uses a generative artificial intelligence (AI) model to analyze collected purchase history. Past purchase history from the database is used as input. The AI ​​model analyzes this data to understand the user's consumption trends and preferences. The output is a personalized product list tailored to the user. Based on the analysis results, more accurate product recommendations become possible.

[0464] Step 4:

[0465] The server generates personalized product recommendations and coupons based on the user's location information and product analysis results. The input consists of product recommendation data and location data generated by an AI model. The server integrates this data to create coupons and product recommendations tailored to the user. The output is product recommendation information and coupon data sent to the user's device.

[0466] Step 5:

[0467] The terminal displays product recommendations and coupon information sent from the server to the user. Input includes recommendation information and coupon data from the server. The terminal visually presents this information through a user interface, making it immediately available to the user. Output is a screen display designed to encourage the user's purchasing behavior.

[0468] Step 6:

[0469] When a user scans an item in a store, the terminal sends the item's barcode information to the server. The input is the barcode scan data generated by the user interaction. The server collects price information from multiple suppliers and presents the user with the most economical option. The output is the price comparison results and associated coupon information. This allows the user to purchase at the best possible price.

[0470] (Application Example 1)

[0471] Next, we will explain Application Example 1. In the following explanation, 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."

[0472] Modern consumers demand efficient and personalized information when shopping in physical stores. However, they face the challenge of difficulty obtaining the most relevant product information, price comparisons, and discount information in real time at the time of purchase. Furthermore, the insufficient use of smart devices for purchasing support is also a problem.

[0473] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0474] In this invention, the server includes means for acquiring and analyzing the user's purchase history, means for acquiring and analyzing the user's current location information, and means for generating and immediately presenting personalized product suggestions. This enables consumers to efficiently select products in physical stores and make the most economical purchases.

[0475] "Purchase history" refers to records of goods and services that consumers have purchased in the past, and is data used to understand consumption patterns and preferences.

[0476] "Current location information" refers to data about the consumer's current location and is used to provide geographically relevant information.

[0477] "Personalized product recommendations" refer to product recommendations selected based on a consumer's individual purchase history and interests, providing the optimal choices to meet individual needs.

[0478] "Price information" refers to data on the prices offered by different suppliers for a particular item, and serves as a basis for consumers to make informed decisions about the best deals.

[0479] A "discount coupon" is a type of voucher that offers a discount when purchasing designated goods or services, thereby providing consumers with an economic benefit.

[0480] A "smartphone or mobile device" is a portable device with advanced computing capabilities and communication functions, enabling the use of various applications.

[0481] "Real-time" refers to a state where information processing and communication occur instantly without delay, allowing consumers to obtain the information they need at that moment.

[0482] A "generative model" is an algorithm or computational method used to generate new information or options based on data, and is a technology that is attracting particular attention in the field of artificial intelligence.

[0483] "Contrast" is the act of comparing multiple options or data to reveal their differences and characteristics, and is a technique that helps in making the optimal choice.

[0484] The system for implementing this invention comprises a server, a terminal, and a user. The server acquires the user's purchase history and current location information and analyzes it using a generative AI model. This generates personalized product suggestions suitable for the user. Specifically, the server is built using Python or Node.js, and the data is stored in MySQL or MongoDB. TensorFlow or PyTorch is used to implement the generative model for analysis.

[0485] The terminal uses a smartphone or other portable device and presents necessary information to the user using front-end technologies such as React Native. On the terminal, the user scans a product to obtain barcode information and sends that information to the server. The server retrieves product prices from different suppliers and presents the user with the best option in real time.

[0486] For example, when a user scans milk in a store, the server collects price information and finds the most economical provider. The user can then use this information to make the best purchase. Furthermore, using discount coupons provided simultaneously allows for even more efficient shopping.

[0487] An example of a prompt message is, "Analyze user ID: 12345's purchase data for the past three months to generate new milk product recommendations." Such a system allows users to enjoy an efficient and personalized shopping experience in physical stores.

[0488] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0489] Step 1:

[0490] The user scans the product using the camera on their device. The input is a barcode image captured by the camera, and the output is a recognized product ID. This product ID is matched against the product database, and the corresponding product information is sent to the server.

[0491] Step 2:

[0492] The server retrieves price information for the corresponding product from the database based on the received product ID. The input is the product ID, and price information from different suppliers in the database is retrieved. The output is a list of price information from each supplier.

[0493] Step 3:

[0494] The server analyzes the acquired price information using a generating AI model to calculate the optimal price. The input is a list of price information, and the output is information on the best place to buy. This calculation identifies the most economical option that meets the user's needs.

[0495] Step 4:

[0496] The server obtains the user's current location information and generates discount coupons usable at nearby stores. The input consists of the user's current location information and the results of an AI model analysis; the output is appropriate discount coupon information. The generated discount coupon information is then sent to the user's device.

[0497] Step 5:

[0498] The terminal displays optimal price information and discount coupon information received from the server to the user. Input is data from the server, and output is visually presented to the user through the terminal's user interface. This presentation allows the user to shop efficiently and effectively.

[0499] Step 6:

[0500] The user makes a purchase decision based on the information presented and applies a discount coupon using the terminal. The input is the user's selection, and the output is the final purchase information. This process allows the user to have a personalized shopping experience in a physical store.

[0501] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0502] This invention is a system that combines an emotion engine to improve the user's purchasing experience. The system consists of three main components: a server, a terminal, and an emotion engine. The server has the basic functions of collecting and analyzing the user's purchase history and location information, while also utilizing the user's emotional information obtained by the emotion engine.

[0503] The server collects users' past purchase history and location information as numerous data points and analyzes them in real time. In addition, the emotion engine estimates the user's emotions from voice input and biometric information and provides this information to the server. This emotion information is used to make personalized product recommendations and coupon offers more appropriate.

[0504] The device presents product recommendations and coupon information sent from the server to the user, reflecting their changing emotions. The device works in conjunction with an emotion engine, enabling it to offer emotionally appropriate product selections and benefits when the user shows interest in a product or when signs of hesitation are detected.

[0505] For example, if the emotion engine detects a user's stress while they are searching for products through their device, the server will enhance its recommendations for relaxing products based on this information. For instance, if a user shows signs of tension while browsing expensive items, the server will recommend lower-priced alternatives within the same category to make the shopping experience more comfortable. Furthermore, if excitement or joy is detected during the purchasing process, the server will offer immediately available discount coupons to encourage purchases.

[0506] Thus, the system of the present invention aims to enhance user satisfaction by incorporating user emotions into the feedback, providing a more refined and personalized purchasing experience.

[0507] The following describes the processing flow.

[0508] Step 1:

[0509] Users use a terminal to search for products or scan products within the store. The terminal then sends the scanned product information to the server.

[0510] Step 2:

[0511] The device inputs voice data and biometric information from the user into an emotion engine to analyze the user's emotions. The results of this analysis are sent to the server as information indicating the user's current emotional state.

[0512] Step 3:

[0513] Based on the received product information and the user's emotional state, the server generates a personalized product recommendation list, referencing past purchase history and location information. During this process, the recommendations are adjusted according to the user's emotional state.

[0514] Step 4:

[0515] The server sends the generated product recommendation list to the terminal, which then displays it to the user in real time.

[0516] Step 5:

[0517] If a user expresses interest in a presented product, the device requests the server for available pricing information about that product.

[0518] Step 6:

[0519] The server collects pricing information from different providers for a product and determines the best place to buy it. It also generates special discounts and coupons, taking into account the user's emotional state, as needed.

[0520] Step 7:

[0521] The server sends the best purchase source and special discount information to the terminal, which then presents this information to the user.

[0522] Step 8:

[0523] If the user decides to proceed with a purchase, the device will use the selected retailer to support the purchase process.

[0524] This entire process allows users to receive a customized purchasing experience tailored to their emotions and specific needs, leading to increased satisfaction.

[0525] (Example 2)

[0526] Next, we will describe Example 2. 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."

[0527] To provide a personalized purchasing experience, it is necessary to offer information that takes into account not only the user's past behavior but also their current emotional state. However, conventional systems lack product recommendations and coupon offers that reflect the user's emotions, which limits the potential for improving user satisfaction.

[0528] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0529] In this invention, the server includes means for analyzing the user's past purchase history and location information, means for estimating and acquiring emotional information from the user's voice and biometric information, and means for generating personalized product recommendations based on the said data. This makes it possible to provide a more precise and personalized purchasing experience.

[0530] "Purchase history" refers to data about products and services that a user has purchased in the past.

[0531] "Location information" refers to data about a user's current location or places they have visited in the past.

[0532] "Emotional information" refers to data on the emotional state estimated based on the user's voice and biometric information.

[0533] "Personalized product recommendations" refer to recommendations for products and services that are optimized for a specific user, generated based on the user's purchase history, location information, and sentiment information.

[0534] A "terminal" is an electronic device used by a user to receive information, and includes smartphones, tablets, and other similar devices.

[0535] A "supplier" refers to a company or business that provides specific goods or services.

[0536] "Generative AI technology" is an artificial intelligence technology that generates user-optimized recommendations and information based on diverse input data.

[0537] This system is built around a server, terminals, and an emotion engine to enhance the user's purchasing experience. The server collects and analyzes user purchase history and location information. It utilizes Python and SQL as data processing languages, and general-purpose MySQL or PostgreSQL as its database management system. To process data in real time, the server uses a widely used cloud service platform.

[0538] The device has the function of receiving and displaying personalized product recommendations and coupons to the user. The device utilizes a mobile phone with either an Android or iOS operating system and works in conjunction with an emotion engine. The emotion engine collects and analyzes voice and biometric information through input devices such as microphones and cameras. This is used to estimate the user's emotions. AI technology is used in this process. For example, voice recognition software is used for voice processing, and an emotion analysis API is used for emotion estimation.

[0539] As a concrete example, suppose a user is considering purchasing an expensive item using their device, and the emotion engine detects the user's tension. In this case, the server recommends a cheaper item in the same category and also enhances the presentation with information on items that have a relaxing effect. Furthermore, if a situation is detected where the user's purchase intent is heightened, an immediate discount coupon is provided to encourage the user's purchasing behavior.

[0540] An example of a prompt message is, "Please describe the procedure for detecting changes in the user's emotions and recommending appropriate products based on their situation while searching for products online." In this way, the present invention is a system that can improve the user's purchasing experience by providing sophisticated product recommendations that take emotions into consideration.

[0541] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0542] Step 1:

[0543] The server retrieves users' purchase history and location information from a database. The retrieved data is then input into an analysis tool to extract characteristics such as purchase patterns and popular visited locations. This data processing uses Python for data cleaning and aggregation, resulting in organized information for each user.

[0544] Step 2:

[0545] The device inputs the user's voice and biometric information through sensors and transmits it to the emotion engine. The emotion engine estimates the user's current emotional state based on this information. Specifically, a machine learning model analyzes emotions from voice tone and facial expressions, and classifies the results into categories such as "relaxed" and "stressed."

[0546] Step 3:

[0547] The server combines the analysis results from Step 1 with the emotional information obtained in Step 2 and inputs it into the AI ​​model that generates product recommendations. This model creates a list of products that are most suitable for the user. For example, for a user who is experiencing an emotional state of tension, the model will generate recommendations that emphasize products that are expected to have a relaxing effect.

[0548] Step 4:

[0549] The server sends the generated product recommendation list to the terminal. The terminal uses its user interface to display customized product recommendations and coupons on the screen. This output includes promotional messages composed of text and images, designed to be easily understood visually by the user.

[0550] Step 5:

[0551] Users review product recommendations displayed on their devices and either select items they are interested in or use offered coupons. This selection information is sent back from the device to the server and updated as part of the user's behavior history. This feedback data is then used to improve the quality of future recommendations.

[0552] (Application Example 2)

[0553] Next, we will explain application example 2. In the following explanation, 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."

[0554] Modern consumers demand diverse and personalized shopping experiences, but existing systems fail to adequately provide flexible product recommendations and coupons tailored to consumers' emotions and circumstances. Furthermore, physical stores struggle to provide real-time information that meets customer needs, resulting in a lack of efficient means to optimally stimulate purchasing intent.

[0555] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0556] In this invention, the server includes means for acquiring biometric information and estimating the user's emotions, means for generating personalized product recommendations based on the user's purchase history, location information, and emotional information, and means for adaptively displaying product recommendations and coupons via a smart device according to the emotions. This makes it possible to provide a flexible and personalized purchasing experience that is tailored to the user's emotions and circumstances.

[0557] "Users" refer to individuals who use the system and are the entities that provide purchase history and location information.

[0558] "Purchase history" refers to information that shows a record of products a user has purchased in the past and functions as an important data point for recommending products.

[0559] "Location information" refers to data that indicates the user's current location or a specific point, and is used to provide personalized services.

[0560] "Biometric information" refers to data that indicates the user's physical state and reactions, and is used for emotion estimation.

[0561] "Emotional information" refers to data that indicates the emotional state of users and is used for product recommendations and coupon provision.

[0562] "Product recommendations" are product options presented to users, and are personalized based on purchase history, location information, and sentiment information.

[0563] A "coupon" is a voucher or code used to offer discounts or benefits to users, and it plays a role in stimulating purchasing intent.

[0564] A "smart device" is a device capable of processing and displaying electronic data, and functions as an interface within this system.

[0565] "Product recognition" is the process of identifying a specific product and obtaining information based on its characteristics.

[0566] A "supplier" is an entity that provides or sells goods and is the source of price information.

[0567] The system implementing this invention mainly consists of three elements: a server, a terminal, and an emotion engine. This system collects the user's purchase history, location information, and biometric information through a smart device worn by the user. The server uses a generative AI model based on this data to estimate the user's emotional information.

[0568] The server analyzes the data by linking it with acquired purchase history and location information to generate personalized product recommendations and coupons. This analysis utilizes machine learning frameworks such as TensorFlow and PyTorch, which run on Google Cloud and Amazon Web Services. The generated product recommendations and coupons are sent to the user's smart device in real time, and are displayed adaptively according to their emotions.

[0569] The device functions as an interface to provide the user with this received information visually and audibly. Smart glasses and smartphones fulfill this role, displaying information at appropriate times in response to changes in the user's emotions. Augmented reality (AR) technology may also be used for this display.

[0570] As a concrete example, if tension is detected when a customer picks up a specific product in a physical store, the system will recommend another product within that category that offers better value for money. The AI ​​model generated in this case operates using the following prompt messages.

[0571] Example prompt: "Generate a list of products that can help you relax, based on your recent purchase history and current emotional state, 'Tension'."

[0572] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0573] Step 1:

[0574] The server receives biometric information, purchase history, and location information from smart devices. This information is used as input. A data collection module acquires this data in real time and stores it directly in the database.

[0575] Step 2:

[0576] The server analyzes biometric information to estimate the user's emotional state. Based on the input biometric information, an emotion engine operates and outputs the current emotion through voice and image analysis. For example, emotions such as tension and joy are identified by the AI ​​model.

[0577] Step 3:

[0578] The server generates personalized product recommendations based on purchase history, location information, and estimated sentiment information. The AI ​​recommendation engine receives this data as input, analyzes it using TensorFlow, and outputs a list of products best suited to the user. In this process, the generating AI model uses the prompt statement "Generate recommended products based on recent purchase history and current sentiment 'XX'."

[0579] Step 4:

[0580] The server generates product recommendations and coupons, then sends them to the device. The transmitted data arrives at the device and is then provided to the user visually or audibly. For example, product information might be displayed on smart glasses using augmented reality (AR) technology.

[0581] Step 5:

[0582] Users make purchasing decisions based on the presented product information and coupons. The system confirms the user's output action of selecting a product via their smart device, and the device resends that selection to the server, recording it as purchase data.

[0583] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0584] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0585] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0586] [Fourth Embodiment]

[0587] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0588] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0589] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0590] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0591] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0592] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0593] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0594] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0595] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0596] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0597] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0598] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0599] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0600] This invention is a system for improving the user's purchasing experience and is realized through three main components: a server, a terminal, and the user. The server plays a central role in analyzing purchase history and location information collected from the user and generating personalized product recommendations and coupons.

[0601] The server maintains users' past purchase history in a database and periodically analyzes this data using an artificial intelligence model. Based on the analysis results, it understands users' consumption trends and recommends products best suited to their individual preferences and needs. Furthermore, by knowing the user's current location, the server can generate and provide geographically appropriate coupons in real time.

[0602] The terminal serves to display product recommendations and coupon information sent from the server to the user. Through the terminal, users can view products of interest and utilize offered coupons. When a user scans or searches for a specific product, the terminal instantly sends that information to the server, which then retrieves price information and suggests the best place to purchase it.

[0603] As a concrete example, when a user scans an item in a supermarket, the terminal sends the item information to a server. The server instantly retrieves price information from multiple providers, compares it, and then presents the most economical option for the user. Furthermore, any coupons that can be used at that time are also presented, allowing the user to shop more efficiently by utilizing those coupons.

[0604] The system according to the present invention can save users time and costs and provide a more satisfying purchasing experience.

[0605] The following describes the processing flow.

[0606] Step 1:

[0607] The server collects each user's past purchase history data and stores it in a database. This includes information such as the date and time, purchased items, amount, and location of purchase.

[0608] Step 2:

[0609] The device obtains permission from the user to share location information and sends that information to the server.

[0610] Step 3:

[0611] The server uses the acquired purchase history and location information to perform data analysis using an AI model. This analysis identifies the user's purchasing patterns and preferences.

[0612] Step 4:

[0613] The server generates a product list tailored to the user based on the analysis results. This list reflects the user's past behavior and current trends.

[0614] Step 5:

[0615] The server sends the generated product list to the terminal and presents it to the user.

[0616] Step 6:

[0617] The user selects an item of interest from the presented product list and checks its details.

[0618] Step 7:

[0619] The device scans or searches for products the user is interested in and sends that information to the server.

[0620] Step 8:

[0621] The server instantly retrieves prices from multiple providers for the scanned or searched product and performs a comparative analysis.

[0622] Step 9:

[0623] The server returns the price analysis results to the terminal and presents the user with the best place to buy and its details.

[0624] Step 10:

[0625] The server generates relevant coupons based on the user's current location and purchase history, and provides them to the user via the terminal.

[0626] Step 11:

[0627] Based on the presented purchase information and coupons, the user selects and executes the most suitable purchase.

[0628] (Example 1)

[0629] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0630] While modern consumers have access to a wide variety of products, they spend a lot of time and effort finding products that suit their individual preferences. Furthermore, opportunities to identify the best place to buy in real time and to receive effective discounts and benefits to encourage purchases are limited. Therefore, there is a need to provide an efficient and personalized shopping experience.

[0631] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0632] In this invention, the server includes processing means for acquiring and analyzing the user's purchase history, processing means for acquiring and analyzing the user's geographical information, and processing means for generating personalized product suggestions based on the purchase history and geographical information. This makes it possible to efficiently recommend products based on the user's preferences and provide an optimal purchasing experience.

[0633] "Purchase history" refers to records of products that a user has purchased in the past, including the date and time of purchase, product name, price, and store information.

[0634] "Geographic information" refers to location data used to identify the user's current location, and is data obtained from GPS information and Wi-Fi location information.

[0635] "Product recommendations" refer to information that recommends products suitable for the user based on their purchase history and geographical information.

[0636] "Price information" refers to information about the price of a specific product, and includes multiple price data obtained from different suppliers.

[0637] "Supplier" refers to a business or store that provides goods or services.

[0638] A "discount service" is a monetary benefit that users can receive by meeting certain conditions, and includes coupons and promotions.

[0639] A "generative AI model" is an artificial intelligence method or system used to analyze user data and generate personalized product recommendations and other information.

[0640] This invention provides a system comprising a server, terminals, and users to improve the user's purchasing experience. The server collects and analyzes the user's purchase history and geographical information using specific hardware and software. The hardware used includes a high-performance database server, and the software includes a machine learning platform for generative AI models. The server continuously analyzes purchase history data and utilizes generative AI models to understand the user's consumption trends.

[0641] The server acquires users' geographical information in real time, for example, through GPS and location services, and uses this data to generate region-specific discount services. At the same time, it uses a generative AI model to provide personalized product recommendations. This makes it possible to recommend products based on the user's specific preferences in real time.

[0642] The terminals, configured as smartphones or dedicated devices, are responsible for presenting information from the server to the user. When a user scans a product using the terminal, they can instantly obtain detailed price information about that product. The server collects price information from different suppliers for each product and presents the best option.

[0643] For example, when a user scans for coffee at a supermarket, the server instantly retrieves price information from multiple suppliers and presents the user with the most economical option. It also displays discount coupons for cafes that can be used immediately, allowing the user to shop efficiently.

[0644] Examples of prompt messages include, "Recommend the best products for this user based on their most recent purchase history," and "Generate coupons available nearby based on their location."

[0645] This entire system utilizes generative artificial intelligence models and advanced analytical techniques to enable effective data processing and personalized services. This results in time and cost savings for users, and provides a more satisfying purchasing experience.

[0646] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0647] Step 1:

[0648] The server collects purchase history each time a user makes a purchase. Inputs include purchase data such as the date and time of purchase, product name, price, and store information. The server stores this data in a database and organizes it for each user. The purpose of properly storing the data is to make it available for later analysis.

[0649] Step 2:

[0650] The server acquires the user's location information. The input consists of GPS data and Wi-Fi location information provided by the user's smartphone or device. Based on this, the server identifies geographical patterns. The output is location data used to determine services and products suitable for the user's current location. This geographical data is updated in real time and forms the basis for providing optimal suggestions to the user.

[0651] Step 3:

[0652] The server uses a generative artificial intelligence (AI) model to analyze collected purchase history. Past purchase history from the database is used as input. The AI ​​model analyzes this data to understand the user's consumption trends and preferences. The output is a personalized product list tailored to the user. Based on the analysis results, more accurate product recommendations become possible.

[0653] Step 4:

[0654] The server generates personalized product recommendations and coupons based on the user's location information and product analysis results. The input consists of product recommendation data and location data generated by an AI model. The server integrates this data to create coupons and product recommendations tailored to the user. The output is product recommendation information and coupon data sent to the user's device.

[0655] Step 5:

[0656] The terminal displays product recommendations and coupon information sent from the server to the user. Input includes recommendation information and coupon data from the server. The terminal visually presents this information through a user interface, making it immediately available to the user. Output is a screen display designed to encourage the user's purchasing behavior.

[0657] Step 6:

[0658] When a user scans an item in a store, the terminal sends the item's barcode information to the server. The input is the barcode scan data generated by the user interaction. The server collects price information from multiple suppliers and presents the user with the most economical option. The output is the price comparison results and associated coupon information. This allows the user to purchase at the best possible price.

[0659] (Application Example 1)

[0660] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0661] Modern consumers demand efficient and personalized information when shopping in physical stores. However, they face the challenge of difficulty obtaining the most relevant product information, price comparisons, and discount information in real time at the time of purchase. Furthermore, the insufficient use of smart devices for purchasing support is also a problem.

[0662] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0663] In this invention, the server includes means for acquiring and analyzing the user's purchase history, means for acquiring and analyzing the user's current location information, and means for generating and immediately presenting personalized product suggestions. This enables consumers to efficiently select products in physical stores and make the most economical purchases.

[0664] "Purchase history" refers to records of goods and services that consumers have purchased in the past, and is data used to understand consumption patterns and preferences.

[0665] "Current location information" refers to data about the consumer's current location and is used to provide geographically relevant information.

[0666] "Personalized product recommendations" refer to product recommendations selected based on a consumer's individual purchase history and interests, providing the optimal choices to meet individual needs.

[0667] "Price information" refers to data on the prices offered by different suppliers for a particular item, and serves as a basis for consumers to make informed decisions about the best deals.

[0668] A "discount coupon" is a type of voucher that offers a discount when purchasing designated goods or services, thereby providing consumers with an economic benefit.

[0669] A "smartphone or mobile device" is a portable device with advanced computing capabilities and communication functions, enabling the use of various applications.

[0670] "Real-time" refers to a state where information processing and communication occur instantly without delay, allowing consumers to obtain the information they need at that moment.

[0671] A "generative model" is an algorithm or computational method used to generate new information or options based on data, and is a technology that is attracting particular attention in the field of artificial intelligence.

[0672] "Contrast" is the act of comparing multiple options or data to reveal their differences and characteristics, and is a technique that helps in making the optimal choice.

[0673] The system for implementing this invention comprises a server, a terminal, and a user. The server acquires the user's purchase history and current location information and analyzes it using a generative AI model. This generates personalized product suggestions suitable for the user. Specifically, the server is built using Python or Node.js, and the data is stored in MySQL or MongoDB. TensorFlow or PyTorch is used to implement the generative model for analysis.

[0674] The terminal uses a smartphone or other portable device and presents necessary information to the user using front-end technologies such as React Native. On the terminal, the user scans a product to obtain barcode information and sends that information to the server. The server retrieves product prices from different suppliers and presents the user with the best option in real time.

[0675] For example, when a user scans milk in a store, the server collects price information and finds the most economical provider. The user can then use this information to make the best purchase. Furthermore, using discount coupons provided simultaneously allows for even more efficient shopping.

[0676] An example of a prompt message is, "Analyze user ID: 12345's purchase data for the past three months to generate new milk product recommendations." Such a system allows users to enjoy an efficient and personalized shopping experience in physical stores.

[0677] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0678] Step 1:

[0679] The user scans the product using the camera on their device. The input is a barcode image captured by the camera, and the output is a recognized product ID. This product ID is matched against the product database, and the corresponding product information is sent to the server.

[0680] Step 2:

[0681] The server retrieves price information for the corresponding product from the database based on the received product ID. The input is the product ID, and price information from different suppliers in the database is retrieved. The output is a list of price information from each supplier.

[0682] Step 3:

[0683] The server analyzes the acquired price information using a generating AI model to calculate the optimal price. The input is a list of price information, and the output is information on the best place to buy. This calculation identifies the most economical option that meets the user's needs.

[0684] Step 4:

[0685] The server obtains the user's current location information and generates discount coupons usable at nearby stores. The input consists of the user's current location information and the results of an AI model analysis; the output is appropriate discount coupon information. The generated discount coupon information is then sent to the user's device.

[0686] Step 5:

[0687] The terminal displays optimal price information and discount coupon information received from the server to the user. Input is data from the server, and output is visually presented to the user through the terminal's user interface. This presentation allows the user to shop efficiently and effectively.

[0688] Step 6:

[0689] The user makes a purchase decision based on the information presented and applies a discount coupon using the terminal. The input is the user's selection, and the output is the final purchase information. This process allows the user to have a personalized shopping experience in a physical store.

[0690] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0691] This invention is a system that combines an emotion engine to improve the user's purchasing experience. The system consists of three main components: a server, a terminal, and an emotion engine. The server has the basic functions of collecting and analyzing the user's purchase history and location information, while also utilizing the user's emotional information obtained by the emotion engine.

[0692] The server collects users' past purchase history and location information as numerous data points and analyzes them in real time. In addition, the emotion engine estimates the user's emotions from voice input and biometric information and provides this information to the server. This emotion information is used to make personalized product recommendations and coupon offers more appropriate.

[0693] The device presents product recommendations and coupon information sent from the server to the user, reflecting their changing emotions. The device works in conjunction with an emotion engine, enabling it to offer emotionally appropriate product selections and benefits when the user shows interest in a product or when signs of hesitation are detected.

[0694] For example, if the emotion engine detects a user's stress while they are searching for products through their device, the server will enhance its recommendations for relaxing products based on this information. For instance, if a user shows signs of tension while browsing expensive items, the server will recommend lower-priced alternatives within the same category to make the shopping experience more comfortable. Furthermore, if excitement or joy is detected during the purchasing process, the server will offer immediately available discount coupons to encourage purchases.

[0695] Thus, the system of the present invention aims to enhance user satisfaction by incorporating user emotions into the feedback, providing a more refined and personalized purchasing experience.

[0696] The following describes the processing flow.

[0697] Step 1:

[0698] Users use a terminal to search for products or scan products within the store. The terminal then sends the scanned product information to the server.

[0699] Step 2:

[0700] The device inputs voice data and biometric information from the user into an emotion engine to analyze the user's emotions. The results of this analysis are sent to the server as information indicating the user's current emotional state.

[0701] Step 3:

[0702] Based on the received product information and the user's emotional state, the server generates a personalized product recommendation list, referencing past purchase history and location information. During this process, the recommendations are adjusted according to the user's emotional state.

[0703] Step 4:

[0704] The server sends the generated product recommendation list to the terminal, which then displays it to the user in real time.

[0705] Step 5:

[0706] If a user expresses interest in a presented product, the device requests the server for available pricing information about that product.

[0707] Step 6:

[0708] The server collects pricing information from different providers for a product and determines the best place to buy it. It also generates special discounts and coupons, taking into account the user's emotional state, as needed.

[0709] Step 7:

[0710] The server sends the best purchase source and special discount information to the terminal, which then presents this information to the user.

[0711] Step 8:

[0712] If the user decides to proceed with a purchase, the device will use the selected retailer to support the purchase process.

[0713] This entire process allows users to receive a customized purchasing experience tailored to their emotions and specific needs, leading to increased satisfaction.

[0714] (Example 2)

[0715] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0716] To provide a personalized purchasing experience, it is necessary to offer information that takes into account not only the user's past behavior but also their current emotional state. However, conventional systems lack product recommendations and coupon offers that reflect the user's emotions, which limits the potential for improving user satisfaction.

[0717] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0718] In this invention, the server includes means for analyzing the user's past purchase history and location information, means for estimating and acquiring emotional information from the user's voice and biometric information, and means for generating personalized product recommendations based on the said data. This makes it possible to provide a more precise and personalized purchasing experience.

[0719] "Purchase history" refers to data about products and services that a user has purchased in the past.

[0720] "Location information" refers to data about a user's current location or places they have visited in the past.

[0721] "Emotional information" refers to data on the emotional state estimated based on the user's voice and biometric information.

[0722] "Personalized product recommendations" refer to recommendations for products and services that are optimized for a specific user, generated based on the user's purchase history, location information, and sentiment information.

[0723] A "terminal" is an electronic device used by a user to receive information, and includes smartphones, tablets, and other similar devices.

[0724] A "supplier" refers to a company or business that provides specific goods or services.

[0725] "Generative AI technology" is an artificial intelligence technology that generates user-optimized recommendations and information based on diverse input data.

[0726] This system is built around a server, terminals, and an emotion engine to enhance the user's purchasing experience. The server collects and analyzes user purchase history and location information. It utilizes Python and SQL as data processing languages, and general-purpose MySQL or PostgreSQL as its database management system. To process data in real time, the server uses a widely used cloud service platform.

[0727] The device has the function of receiving and displaying personalized product recommendations and coupons to the user. The device utilizes a mobile phone with either an Android or iOS operating system and works in conjunction with an emotion engine. The emotion engine collects and analyzes voice and biometric information through input devices such as microphones and cameras. This is used to estimate the user's emotions. AI technology is used in this process. For example, voice recognition software is used for voice processing, and an emotion analysis API is used for emotion estimation.

[0728] As a concrete example, suppose a user is considering purchasing an expensive item using their device, and the emotion engine detects the user's tension. In this case, the server recommends a cheaper item in the same category and also enhances the presentation with information on items that have a relaxing effect. Furthermore, if a situation is detected where the user's purchase intent is heightened, an immediate discount coupon is provided to encourage the user's purchasing behavior.

[0729] An example of a prompt message is, "Please describe the procedure for detecting changes in the user's emotions and recommending appropriate products based on their situation while searching for products online." In this way, the present invention is a system that can improve the user's purchasing experience by providing sophisticated product recommendations that take emotions into consideration.

[0730] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0731] Step 1:

[0732] The server retrieves users' purchase history and location information from a database. The retrieved data is then input into an analysis tool to extract characteristics such as purchase patterns and popular visited locations. This data processing uses Python for data cleaning and aggregation, resulting in organized information for each user.

[0733] Step 2:

[0734] The device inputs the user's voice and biometric information through sensors and transmits it to the emotion engine. The emotion engine estimates the user's current emotional state based on this information. Specifically, a machine learning model analyzes emotions from voice tone and facial expressions, and classifies the results into categories such as "relaxed" and "stressed."

[0735] Step 3:

[0736] The server combines the analysis results from Step 1 with the emotional information obtained in Step 2 and inputs it into the AI ​​model that generates product recommendations. This model creates a list of products that are most suitable for the user. For example, for a user who is experiencing an emotional state of tension, the model will generate recommendations that emphasize products that are expected to have a relaxing effect.

[0737] Step 4:

[0738] The server sends the generated product recommendation list to the terminal. The terminal uses its user interface to display customized product recommendations and coupons on the screen. This output includes promotional messages composed of text and images, designed to be easily understood visually by the user.

[0739] Step 5:

[0740] Users review product recommendations displayed on their devices and either select items they are interested in or use offered coupons. This selection information is sent back from the device to the server and updated as part of the user's behavior history. This feedback data is then used to improve the quality of future recommendations.

[0741] (Application Example 2)

[0742] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0743] Modern consumers demand diverse and personalized shopping experiences, but existing systems fail to adequately provide flexible product recommendations and coupons tailored to consumers' emotions and circumstances. Furthermore, physical stores struggle to provide real-time information that meets customer needs, resulting in a lack of efficient means to optimally stimulate purchasing intent.

[0744] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0745] In this invention, the server includes means for acquiring biometric information and estimating the user's emotions, means for generating personalized product recommendations based on the user's purchase history, location information, and emotional information, and means for adaptively displaying product recommendations and coupons via a smart device according to the emotions. This makes it possible to provide a flexible and personalized purchasing experience that is tailored to the user's emotions and circumstances.

[0746] "Users" refer to individuals who use the system and are the entities that provide purchase history and location information.

[0747] "Purchase history" refers to information that shows a record of products a user has purchased in the past and functions as an important data point for recommending products.

[0748] "Location information" refers to data that indicates the user's current location or a specific point, and is used to provide personalized services.

[0749] "Biometric information" refers to data that indicates the user's physical state and reactions, and is used for emotion estimation.

[0750] "Emotional information" refers to data that indicates the emotional state of users and is used for product recommendations and coupon provision.

[0751] "Product recommendations" are product options presented to users, and are personalized based on purchase history, location information, and sentiment information.

[0752] A "coupon" is a voucher or code used to offer discounts or benefits to users, and it plays a role in stimulating purchasing intent.

[0753] A "smart device" is a device capable of processing and displaying electronic data, and functions as an interface within this system.

[0754] "Product recognition" is the process of identifying a specific product and obtaining information based on its characteristics.

[0755] A "supplier" is an entity that provides or sells goods and is the source of price information.

[0756] The system implementing this invention mainly consists of three elements: a server, a terminal, and an emotion engine. This system collects the user's purchase history, location information, and biometric information through a smart device worn by the user. The server uses a generative AI model based on this data to estimate the user's emotional information.

[0757] The server analyzes the data by linking it with acquired purchase history and location information to generate personalized product recommendations and coupons. This analysis utilizes machine learning frameworks such as TensorFlow and PyTorch, which run on Google Cloud and Amazon Web Services. The generated product recommendations and coupons are sent to the user's smart device in real time, and are displayed adaptively according to their emotions.

[0758] The device functions as an interface to provide the user with this received information visually and audibly. Smart glasses and smartphones fulfill this role, displaying information at appropriate times in response to changes in the user's emotions. Augmented reality (AR) technology may also be used for this display.

[0759] As a concrete example, if tension is detected when a customer picks up a specific product in a physical store, the system will recommend another product within that category that offers better value for money. The AI ​​model generated in this case operates using the following prompt messages.

[0760] Example prompt: "Generate a list of products that can help you relax, based on your recent purchase history and current emotional state, 'Tension'."

[0761] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0762] Step 1:

[0763] The server receives biometric information, purchase history, and location information from smart devices. This information is used as input. A data collection module acquires this data in real time and stores it directly in the database.

[0764] Step 2:

[0765] The server analyzes biometric information to estimate the user's emotional state. Based on the input biometric information, an emotion engine operates and outputs the current emotion through voice and image analysis. For example, emotions such as tension and joy are identified by the AI ​​model.

[0766] Step 3:

[0767] The server generates personalized product recommendations based on purchase history, location information, and estimated sentiment information. The AI ​​recommendation engine receives this data as input, analyzes it using TensorFlow, and outputs a list of products best suited to the user. In this process, the generating AI model uses the prompt statement "Generate recommended products based on recent purchase history and current sentiment 'XX'."

[0768] Step 4:

[0769] The server generates product recommendations and coupons, then sends them to the device. The transmitted data arrives at the device and is then provided to the user visually or audibly. For example, product information might be displayed on smart glasses using augmented reality (AR) technology.

[0770] Step 5:

[0771] Users make purchasing decisions based on the presented product information and coupons. The system confirms the user's output action of selecting a product via their smart device, and the device resends that selection to the server, recording it as purchase data.

[0772] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0773] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0774] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0775] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0776] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0777] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0778] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0779] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0780] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0781] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0782] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0783] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0784] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0785] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0786] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0787] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0788] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0789] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0790] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0791] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0792] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0793] The following is further disclosed regarding the embodiments described above.

[0794] (Claim 1)

[0795] A means of obtaining and analyzing the user's purchase history,

[0796] A means of acquiring and analyzing the user's location information,

[0797] A means for generating personalized product recommendations based on the aforementioned purchase history and location information,

[0798] A means of presenting the aforementioned personalized product recommendations to the user in real time,

[0799] A means of obtaining price information from different providers for a product when scanning or searching for that product,

[0800] A means for comparing the acquired price information and presenting the user with the optimal place to purchase,

[0801] A means for generating and presenting personalized coupons based on the aforementioned purchase history and location information,

[0802] A system that includes this.

[0803] (Claim 2)

[0804] The system according to claim 1, further comprising means for coordinating the aforementioned product recommendation, price comparison, and coupon provision.

[0805] (Claim 3)

[0806] The system according to claim 1, wherein the analysis means is performed using a generative artificial intelligence model.

[0807] "Example 1"

[0808] (Claim 1)

[0809] A processing method for acquiring and analyzing the user's purchase history,

[0810] A processing means for acquiring and analyzing the geographical information of users,

[0811] Processing means for generating personalized product suggestions based on the purchase history and geographical information,

[0812] A processing means for continuously presenting the personalized product suggestions to the user,

[0813] A processing means for obtaining price information from different suppliers for a product when product information is entered or searched for,

[0814] A processing means that compares the acquired price information and shows the user the optimal source of purchase,

[0815] Processing means for generating and presenting personalized discount services based on the purchase history and geographical information,

[0816] A processing device that includes a processing device.

[0817] (Claim 2)

[0818] The processing apparatus according to claim 1, further comprising processing means for coordinating the aforementioned product proposal, price comparison, and provision of discount services.

[0819] (Claim 3)

[0820] The processing apparatus according to claim 1, wherein the analysis processing means is performed using a generated AI model.

[0821] "Application Example 1"

[0822] (Claim 1)

[0823] A means of acquiring and analyzing users' purchase history,

[0824] A means of acquiring and analyzing the user's current location information,

[0825] A means for generating personalized product suggestions based on the aforementioned purchase history and the aforementioned current location information,

[0826] A means for immediately presenting the aforementioned personalized product suggestions to the user,

[0827] A means of obtaining price information from different suppliers for an item when scanning or searching for that item,

[0828] A means for comparing the acquired price information and presenting the user with the optimal source of purchase,

[0829] A means for generating and presenting a personalized discount coupon based on the purchase history and current location information,

[0830] A means of scanning products using the camera of a smartphone or other mobile device,

[0831] A system that includes this.

[0832] (Claim 2)

[0833] The system according to claim 1, further comprising means for coordinating the aforementioned product proposal, price comparison, and discount coupon provision, and presenting them in a real-time optimized manner.

[0834] (Claim 3)

[0835] The system according to claim 1, wherein the analysis means is performed using a generative model.

[0836] "Example 2 of combining an emotion engine"

[0837] (Claim 1)

[0838] A means for acquiring and analyzing the user's past purchase history and location information,

[0839] A means for estimating and acquiring emotional information from the user's voice and biometric information,

[0840] A means for generating personalized product recommendations based on the aforementioned purchase history, location information, and sentiment information,

[0841] A means for presenting the aforementioned personalized product recommendations to the terminal in real time,

[0842] A means of obtaining price information from different suppliers for a product when searching for or selecting that product,

[0843] A means for comparing the acquired price information and presenting the optimal purchase source to the terminal,

[0844] A means for generating and presenting personalized coupons based on the aforementioned purchase history, location information, and emotional information,

[0845] An information processing system that includes this.

[0846] (Claim 2)

[0847] The information processing system according to claim 1, further comprising means for coordinating the aforementioned product recommendation, price comparison, and coupon provision.

[0848] (Claim 3)

[0849] The information processing system according to claim 1, wherein the analysis means is performed using generative AI technology.

[0850] "Application example 2 when combining with an emotional engine"

[0851] (Claim 1)

[0852] A means of acquiring and analyzing the user's purchase history,

[0853] A means of acquiring and analyzing the user's location information,

[0854] A means of acquiring biometric information and estimating the user's emotions,

[0855] A means for generating personalized product recommendations based on the purchase history, location information, and sentiment information,

[0856] A means of presenting the aforementioned personalized product recommendations to the user in real time,

[0857] A means of obtaining price information from different suppliers for a product when the product is recognized,

[0858] A means for comparing the acquired price information and presenting the user with the optimal place to purchase,

[0859] A means for generating and presenting personalized coupons based on the purchase history, location information, and emotion information,

[0860] A means for adaptively displaying product recommendations and coupons according to emotions via a smart device,

[0861] A system that includes this.

[0862] (Claim 2)

[0863] The system according to claim 1, further comprising means for coordinating the aforementioned product recommendation, price comparison, and coupon provision.

[0864] (Claim 3)

[0865] The system according to claim 1, wherein the analysis means and emotion estimation means are performed using a generative artificial intelligence model. [Explanation of Symbols]

[0866] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of obtaining and analyzing the user's purchase history, A means of acquiring and analyzing the user's location information, A means for generating personalized product recommendations based on the aforementioned purchase history and location information, A means of presenting the aforementioned personalized product recommendations to the user in real time, A means of obtaining price information from different providers for a product when scanning or searching for that product, A means for comparing the acquired price information and presenting the user with the optimal place to purchase, A means for generating and presenting personalized coupons based on the aforementioned purchase history and location information, A system that includes this.

2. The system according to claim 1, further comprising means for coordinating the aforementioned product recommendation, price comparison, and coupon provision.

3. The system according to claim 1, wherein the analysis means is performed using a generative artificial intelligence model.

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