system

Abstract

We provide the system. [Solution] A means for collecting and storing internet trend data, A means of analyzing collected online trend data and generating sales forecast scores associated with products, A means for obtaining a user's past order history and selecting recommended products based on that history, A means of displaying recommended products selected by the user, A means for receiving order instructions from users, checking inventory, and processing orders, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional order receiving and placing systems, product selection often depends on the experience and sense of the order placer, and improving efficiency and accuracy is an issue. In particular, it is required to reflect in real time the rapidly changing market trends and perform optimal product selection. Also, the introduction of order receiving and placing systems in the retail and wholesale industries has been delayed, and it is necessary to realize a system with new functionality and convenience.

Means for Solving the Problems

[0005] This invention provides a system that generates sales forecast scores linked to products by collecting and analyzing online trend data. This system enables efficient product selection by selecting and displaying recommended products based on the user's past order history. Furthermore, by including a series of means for receiving order instructions from users, checking inventory, and processing orders, it enables real-time trend reflection and rapid ordering procedures. In addition, by utilizing artificial intelligence models for data analysis, the system achieves more accurate product forecasts and recommendations.

[0006] "Internet trend data" refers to data collected on information about the activities and trends of users online.

[0007] A "sales forecast score" is an indicator that predicts and quantifies how much a product will sell in the future.

[0008] "Order history" refers to data that records information about products that a user has ordered in the past.

[0009] A "recommended product" is a product that has been selected based on user needs and market trends, and which should be encouraged to be purchased.

[0010] "Inventory check" is the process of checking the current inventory levels of a product and determining whether it is possible to place an order.

[0011] An "artificial intelligence model" is an algorithm or program that analyzes large amounts of data, extracts useful information from it, and supports decision-making. [Brief explanation of the drawing]

[0012] [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]It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0014] First, the language used in the following description will be explained.

[0015] In the following embodiments, the 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.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] The order placement and receiving system according to this invention mainly consists of a server, a terminal, and a user. The server first collects internet trend data from external sources and stores it in a database. This includes online search trend information and human flow patterns. The server then analyzes this data using an artificial intelligence model and generates a sales forecast score for each product.

[0034] When a user logs in, the terminal retrieves the user's past order history from the server. During this process, the terminal identifies the user's preferences and purchasing trends, and based on this, recommends products. The terminal then displays these recommended products to the user and provides information about product details and projected demand.

[0035] Users can view details of items of interest from a recommended list via their device and place an order by adding them to their cart. The device sends the product information from the cart to the server, initiating the order processing. At this time, the server checks inventory, and if the items are in stock, it confirms the order and proceeds with payment processing.

[0036] As a concrete example, let's consider a retailer considering purchasing seasonal products using this system. When a user logs into their terminal, the system analyzes data on products expected to be popular this season, such as specific clothing or food items. Based on these results, the server provides a list of recommended products that the user might be interested in. The user can add the recommended products to their cart and complete the order smoothly. This system allows users to make data-driven purchasing decisions and select more appropriate products.

[0037] Therefore, this order placement and receiving system enables rapid and accurate product selection and ordering that reflects current trends.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The server periodically collects internet trend data and user flow data from external sources. This includes information such as search trends and visitor behavior based on location. The server also stores this data in a database to prepare for subsequent analysis.

[0041] Step 2:

[0042] The server uses the stored data to perform analysis using an artificial intelligence model. Specifically, it calculates a sales forecast score for each product and stores it in a database. This score is used to predict how much each product will sell in the future, based on past data and trend information.

[0043] Step 3:

[0044] When a user logs in, the terminal retrieves the user's past order history from the server. Based on this data, it analyzes the user's purchasing trends and preferences and requests the server to create a list of products best suited to the user.

[0045] Step 4:

[0046] The server selects recommended products based on the user's profile and analysis results, and sends this list to the terminal. The recommendation list includes product names, sales forecast scores, and inventory status.

[0047] Step 5:

[0048] The device displays a list of recommended products received by the user. The user can view detailed information about each product and add items they like to their cart.

[0049] Step 6:

[0050] The user presses the order button to confirm the order for the items added to their cart. The terminal sends this information to the server and starts processing the order.

[0051] Step 7:

[0052] The server checks inventory based on the received order details. If sufficient inventory is available, it confirms the order, proceeds with payment processing, and sends a confirmation email to the user.

[0053] (Example 1)

[0054] 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."

[0055] Traditional order management systems have the drawback of fixed product selection, making them unable to adequately respond to market changes and user preferences. Furthermore, they fail to accurately forecast demand considering online trends and customer flow patterns, making efficient inventory management and sales strategy development difficult.

[0056] 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.

[0057] In this invention, the server includes means for acquiring and storing trend data from external information sources, means for analyzing the acquired trend data and creating demand forecast indicators associated with items, and means for acquiring the end user's past purchase history and selecting recommended items based on that history. This enables product selection that accurately captures market trends and personalized recommendations based on user preferences.

[0058] "External information sources" refer to data sources obtained from internet platforms and data services, and include trend information and population movement patterns.

[0059] "Trend data" refers to information that reflects market changes and consumer interests, and includes search trends, social media posts, and related news.

[0060] "Storage" refers to keeping collected data in a state where it can be used later, and this includes storing it in databases or cloud storage.

[0061] "Analysis" refers to the process of extracting meaning from collected data and generating useful information and indicators, and includes the use of statistical methods and machine learning.

[0062] A "demand forecast indicator" refers to an indicator that shows the future sales volume and popularity of a product or service in the market using numerical values ​​or evaluations.

[0063] "Purchase history" refers to a record of transactions and purchasing activities that an end user has undertaken to date, including information such as the products purchased, their quantities, and prices.

[0064] "Recommended items" refer to products and services selected based on the preferences and needs of end users, and are based on predictive and historical data.

[0065] The order placement and receiving system according to this invention operates by coordinating the server, terminal, and user elements. A specific embodiment of this system is described below.

[0066] The server first acquires trend data from external sources. This is done through internet information platforms, such as search engine trend data and social media analysis. The acquired data is stored in a database for later analysis. The server uses this data to perform analysis using generative AI models. Specifically, machine learning frameworks such as TENSORFLOW® and PyTorch are used to generate demand forecasting indicators from the data. These indicators relate to sales and demand forecasts and can be used in product sales strategies.

[0067] When a user logs in, the device retrieves their past purchase history from the server. This allows for analysis of the user's preferences and past purchasing trends. Based on this data, the device uses algorithms such as collaborative filtering to select and present recommended items to the user. These recommendations are delivered through dynamically generated web pages and application screens and are updated in real time using JavaScript® and other technologies.

[0068] Users can select items of interest from the recommended products presented and view detailed information. If they like the product, they can add it to their cart and proceed with ordering. The terminal sends the cart information to the server, where inventory checks and purchase procedures are performed. The entire process, including payment, can be carried out smoothly through an online payment system, using the Stripe API or PayPal API.

[0069] For example, if a user wants to buy new sandals that reflect this summer's trends, the server will provide appropriate recommendations based on the latest trend data. A possible prompt would be, "Tell me about this summer's trendy sandals," to which the server would provide a list of recommended products. In response to this prompt, the server presents the best options based on the user's past purchase history and real-time trend data. This allows users to make data-driven purchases and choose the right product.

[0070] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0071] Step 1:

[0072] The server acquires trend data from external sources. Specifically, it uses APIs to collect trend data and population movement data from internet platforms. Inputs include search trend queries and social media posts. After collection, this data is stored in a database on the server, making it available for use in subsequent analysis steps.

[0073] Step 2:

[0074] The server performs analysis using a generative AI model based on acquired trend data. The input data is preprocessed using machine learning frameworks such as TensorFlow and PyTorch, and then demand forecasting indicators are created. This is done through time series analysis and statistical regression, and the output is a sales forecast score for each item. This score indicates the demand for a product and plays an important role in predicting increases or decreases in sales.

[0075] Step 3:

[0076] When a user logs in, the terminal retrieves their past purchase history from the server. The user ID is used as input, and past purchase data is retrieved. This data includes purchased items, purchase dates, prices, etc., and is analyzed on the terminal using a collaborative filtering algorithm. The output is a list of recommended items that reflect the user's preferences.

[0077] Step 4:

[0078] The terminal displays recommended items selected based on the analysis results to the user. Detailed information about the recommended products is provided through the user interface. This operation is performed by updating the displayed content in real time in conjunction with JavaScript. The input uses the ID and details of the recommended product, and the output is displayed as the product name, price, and stock information.

[0079] Step 5:

[0080] Users add items they are interested in to their cart via their device and proceed with the purchase. Specific actions include clicking the purchase button and adding items to the cart. The input is a list of selected items, and the device sends this information to the server. The output is presented as a purchase confirmation screen.

[0081] Step 6:

[0082] The server performs inventory checks and order processing based on the cart information received from the terminal. The input is the cart details; the server checks inventory against the database and confirms the purchase if the item is in stock. The output includes an order confirmation message and payment information. This process integrates with an online payment API to ensure secure payment completion.

[0083] (Application Example 1)

[0084] 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."

[0085] In online marketplaces, accurately understanding consumer purchasing trends and recommending appropriate products in a timely manner is crucial. However, traditional systems often fail to adequately provide customized offers based on individual user preferences or real-time inventory checks, making it difficult to improve the user experience and optimize sales opportunities.

[0086] 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.

[0087] In this invention, the server includes means for collecting and storing online trend data, means for analyzing the collected online trend data and generating sales forecast scores associated with products, and means for obtaining the user's past order history and selecting recommended products based on that history. This makes it possible to provide users with optimized product recommendations and present customized offers and discounts.

[0088] "Internet trend data" refers to information that shows users' activities and interests online, and is used to analyze trends in products and services.

[0089] "Means of collection" refers to a function or device for acquiring necessary data from external sources and incorporating it into a system.

[0090] "Means of analysis" refer to algorithms and systems used to process acquired data and derive information based on a specific purpose.

[0091] The "sales forecast score" is a numerical value used to predict future product sales figures, and is an index generated by a computer based on collected data.

[0092] "User's past order history" refers to a record of a user's past purchasing behavior and is used to understand that user's purchasing trends.

[0093] "Means for selecting recommended products" refers to a function or process for selecting products suitable for a user based on analysis results and user history.

[0094] A "customized offer" is a special proposal, such as a discount or perk, that is provided taking into account the needs and preferences of a particular user.

[0095] "Means of checking inventory information in real time" refers to a system or function for instantly obtaining and checking the current inventory status.

[0096] "Means of displaying to the user" refers to an interface or device for visually providing the user with analyzed or selected information.

[0097] "Means for receiving order instructions" refers to a function that obtains purchase requests from users and processes them within the system.

[0098] This system is an integrated platform consisting of servers, terminals, and users. The servers first collect online trend data using online APIs and web scraping techniques. This data includes information such as search trends and movement patterns. After collection, the servers store this information in a database and perform analysis using artificial intelligence models (for example, models using TensorFlow) to generate sales forecast scores for each product.

[0099] When a user logs in, the terminal retrieves the user's past order history from the server. Based on this history, the terminal understands the user's preferences and purchasing trends and generates personalized product recommendations. At the same time, it performs real-time inventory checks and presents the user with customized offers and discount information.

[0100] Users can view recommended products and offers displayed on their device and select items that interest them. Selected items are added to the cart, and when the user wishes to purchase them, they make a payment through a payment system (for example, a system using the Stripe API).

[0101] For example, using this system, popular sweaters and boots are recommended during the winter season, and special discounts are offered to users with a past purchase history. In this way, data-driven product recommendations and sales promotions are effectively implemented.

[0102] An example of a prompt to input into the generating AI model is: "Collect the necessary data to display a list of trending products for this winter and generate customized recommendations based on past purchase history."

[0103] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0104] Step 1:

[0105] The server collects internet trend data using online APIs and web scraping techniques. The input consists of search trend and movement pattern data obtained from various online sources. This data is formatted and filtered, processed into a database format, and then stored in the database.

[0106] Step 2:

[0107] The server runs a generative AI model using internet trend data stored in the database. The input is the trend data stored in the previous step. The AI ​​model analyzes this data and outputs a sales forecast score for each product. This model uses TensorFlow to predict sales trends.

[0108] Step 3:

[0109] When a user logs into their terminal, the terminal retrieves their past order history from the server. The input is the user ID, and the output is the user's past purchase history data. Based on this history data, the system analyzes the user's preferences and trends.

[0110] Step 4:

[0111] The terminal references the acquired purchase history and the server's sales forecast score to generate a personalized list of recommended products for the user. The input is past purchase history and sales forecast score, and the output is a list of recommended products. This process uses history matching and filtering algorithms.

[0112] Step 5:

[0113] The server accesses the inventory database in real time and checks it on the terminal. The input is a list of recommended products, and the output is information on whether or not they are in stock. The server verifies the inventory status and selects only the products that can be provided to the user.

[0114] Step 6:

[0115] Users view recommended products and customized offers on their device and add items of interest to their cart. Here, user selections are input, and the updated cart information is output. Based on user selections, the device displays high-priority products.

[0116] Step 7:

[0117] The terminal initiates the payment process, and the server executes the payment using the payment provider's API. Inputs are the selected items in the shopping cart and payment information, while output is the payment confirmation status. Once payment is complete, the order is finalized.

[0118] 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.

[0119] The order placement and receiving system according to the present invention achieves even more accurate product recommendations by incorporating an emotion engine that recognizes user emotions, in addition to analysis based on internet trend data and human flow data. The server acquires internet trend data and human flow data from external sources and stores them in a database. Using this information, the server analyzes this data through an artificial intelligence model and generates sales forecast scores for products.

[0120] Furthermore, the terminal utilizes an emotion engine to acquire user emotion data when the user registers. The emotion engine recognizes emotions from the user's facial expressions and voice input and stores them in a database. The server integrates past order history and emotion data to select recommended products.

[0121] For users, this system provides a mechanism that automatically displays recommended products based on past purchase history and current emotional state upon login. More specifically, the device dynamically adjusts the product list according to the user's emotions, providing a more personalized experience.

[0122] As a concrete example, consider a situation where a user is searching for sale items. When the emotion engine recognizes excitement or joy while the user is using their device, the server adjusts the recommendation list based on this, highlighting sale items in a more prominent way than usual. This makes it easier for the user to instantly find items that match their mood.

[0123] This system can improve marketing effectiveness by simultaneously understanding user needs and current emotional states, and optimizing product recommendations in real time.

[0124] The following describes the processing flow.

[0125] Step 1:

[0126] The server collects internet trend data and human traffic data from external sources and stores this data in a database. This includes search trends and purchasing patterns based on location information.

[0127] Step 2:

[0128] When a user logs in, the device uses the camera and microphone built into the user's device to collect emotional data. This allows the system to read the user's emotions from their facial expressions and tone of voice.

[0129] Step 3:

[0130] The server integrates acquired sentiment data with past order history and updates the user profile. Based on this data, an artificial intelligence model is used to select personalized product recommendations for each user.

[0131] Step 4:

[0132] The terminal displays a list of recommended products sent from the server to the user. This list is dynamically adjusted based on sales forecast scores and sentiment analysis results.

[0133] Step 5:

[0134] Users can view a list of recommendations and see details of products that interest them. They can then add desired items to their cart and proceed with the purchase.

[0135] Step 6:

[0136] The terminal sends the cart information to the server and initiates inventory checks and payment processing.

[0137] Step 7:

[0138] The server checks for stock availability and confirms the purchase. It then sends the user an email containing order confirmation and delivery date.

[0139] Step 8:

[0140] The device collects feedback on user purchasing behavior and reports it to the server to improve future recommendation quality.

[0141] This process allows users to receive product recommendations tailored to their emotional state, resulting in a more satisfying shopping experience.

[0142] (Example 2)

[0143] 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".

[0144] Traditional order management systems select recommended products based solely on past user purchasing history, resulting in insufficient personalization that takes into account real-time sentiment and trend information. Therefore, more accurate product recommendations are needed to improve the user experience and enhance marketing effectiveness.

[0145] 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.

[0146] In this invention, the server includes means for collecting and storing internet trend information and human flow information from electronic devices, means for analyzing the collected internet trend information and human flow information and generating predictive scores associated with products, and means for acquiring emotional information using an emotional recognition device when a user registers. This enables more personalized product recommendations based on the user's emotional information and trend information.

[0147] "Internet trend information" refers to data that shows consumer behavior, trends, and popular topics on the internet.

[0148] "Human flow information" refers to data that shows the movement patterns and distribution of people in a particular region or location.

[0149] An "emotion recognition device" is a device or software that analyzes the user's facial expressions and voice to identify their emotional state.

[0150] A "storage device" is hardware or software used to store collected information for a long period of time.

[0151] A "generative artificial intelligence model" is an algorithm that automatically generates new insights and predictions based on a wide variety of collected data.

[0152] A "predictive score" is an indicator that quantifies the future sales and demand for a product or service, generated based on analyzed data.

[0153] "Electronic devices" is a general term referring to computers and smart devices used for data collection and processing.

[0154] A "user" refers to an individual or legal entity that uses the system to place or receive orders for goods or services.

[0155] This invention is an order placement and receiving system that provides users with appropriate product recommendations by acquiring and analyzing multiple data points via a network. The main functions of the system are implemented by a server and terminals.

[0156] The server collects internet trend and human flow information from external data providers. This data is stored in a database and later serves as foundational material for analysis. RESTful APIs and cloud data services are often used for this data collection.

[0157] Next, the server analyzes the information in the database using a generative artificial intelligence model. The AI ​​model generates sales forecast scores related to products from the collected data. For example, prompts such as "predict next month's consumer trends and use them to boost sales" are input to the AI ​​model. In this process, machine learning libraries and data analysis software using Python are commonly used.

[0158] On the other hand, the terminal uses an emotion recognition device when the user accesses the system. User emotion information is obtained by analyzing facial expressions and voice input, and this data is recorded in a database. Facial expression analysis software and voice recognition software are utilized in this process.

[0159] For example, when a user shows interest in a particular event, an emotion recognition device detects that excitement, and the server immediately updates its recommendations for related products. This dynamic recommendation system allows users to efficiently find products that match their current emotions and needs.

[0160] This system aims to improve marketing effectiveness by integrating past purchase history with real-time sentiment data to provide users with more personalized product recommendations.

[0161] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0162] Step 1:

[0163] The server collects online trend information and pedestrian flow information from external data sources. This information is obtained via an API and sent to the server in JSON format. The server receives this information and stores it in a database. Specifically, upon receiving the data, the server removes duplicate data, extracts the necessary fields, and accurately stores them in the database.

[0164] Step 2:

[0165] The server performs data analysis using a generative AI model based on collected data stored in the database. The inputs used are online trend information and pedestrian flow information. The AI ​​model processes this data and outputs a sales forecast score related to the product. Specifically, it predicts future sales trends by comparing them with past trend data. This model is implemented using machine learning libraries such as TensorFlow.

[0166] Step 3:

[0167] The terminal activates the emotion recognition device when the user logs into the system. It receives input from the user's facial expressions and voice, analyzes it, and generates emotion data. The emotion recognition device analyzes how excited or happy the user is and stores this information in a database. The input uses the user's facial recognition data and voice data, and the output is data indicating the user's current emotional state.

[0168] Step 4:

[0169] The server integrates user emotional data and past purchase history, and analyzes it through a generative AI model. This integrated analysis generates data to recommend the most appropriate products to the user. Through the AI ​​model, the server interprets how the user's current emotional state influences their purchasing intent and determines the priority of recommended products.

[0170] Step 5:

[0171] The terminal displays a list of recommended products on the user's screen based on instructions from the server. The arrangement and visual effects of the product list are dynamically changed according to the user's emotional state. For example, if the user is expressing joy, discounted products are highlighted to create a visual impact. In this way, an environment is provided where users can easily find products that interest them.

[0172] (Application Example 2)

[0173] 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".

[0174] Existing order management systems have the challenge of not being able to reflect users' current emotions and interests, as product recommendations are based on users' past purchase history and online behavior data. This results in a failure to maximize user purchasing intent, leading to limited marketing effectiveness.

[0175] 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.

[0176] In this invention, the server includes means for collecting and storing internet trend data, means for analyzing the collected internet trend data and generating sales forecast scores associated with products, and means for recognizing the user's emotions and adjusting product recommendations based on emotion data. This makes it possible to provide personalized product recommendations in real time that are tailored to the user's emotional state.

[0177] "Internet trend data" refers to data that shows user behavior, interests, and trends on the internet.

[0178] A "sales forecast score" is an indicator generated to predict a product's future sales and is used in selecting recommended products.

[0179] "Past order history" refers to data that records the user's past purchase history of products.

[0180] "Recommended products" are products selected by the system based on the user's purchase history and emotional state.

[0181] "Emotional data" refers to information that indicates a user's emotional state, obtained by analyzing the user's facial expressions, voice, and other data.

[0182] An "artificial intelligence model" is a general term for algorithms and frameworks that automatically perform analysis and predictions based on diverse data.

[0183] In embodiments of the present invention, a server collects internet trend data and pedestrian flow data from external sources and stores this data in a database. The collected data is analyzed using software such as Python, pandas, and scikit-learn. This generates a sales forecast score for products. An artificial intelligence model is used for this, and it is integrated with the user's past order history.

[0184] Furthermore, the device incorporates an emotion recognition engine that captures and analyzes the user's facial expressions and voice in real time through the camera and microphone. This emotion data is also stored in a database. When a user logs in, the device utilizes this emotion data to dynamically adjust and display recommended products based on their state.

[0185] In an example of smart glasses use, the server displays a list of appropriate products based on data obtained from the user's facial expressions and voice when they view products. This makes it easy for the user to find products that match their current emotions.

[0186] For example, if a user is wearing smart glasses when going out on a rainy day, the system will prioritize displaying related products such as umbrellas and raincoats. This provides a comfortable shopping environment where users can find products that suit their mood that day.

[0187] Examples of prompt statements are as follows:

[0188] "If we sense 'undergone disappointment' rather than 'neutral' in the user's expression, we will add items from the appropriate product category to the recommendation list."

[0189] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0190] Step 1:

[0191] The server collects internet trend data and pedestrian flow data from external sources. Inputs are external APIs and data feeds, and output is raw data stored in a database. This data serves as foundational information for subsequent analysis.

[0192] Step 2:

[0193] The server analyzes the collected data and generates a sales forecast score for the products. The input is internet trend data and pedestrian flow data stored in a database, and the output is the forecast score. This process is performed using Python and scikit-learn, applying a machine learning model.

[0194] Step 3:

[0195] The device acquires the user's facial expressions and voice in real time and analyzes them with an emotion recognition engine. The input is raw data acquired by the device's camera and microphone, and the output is the user's emotion data. Libraries such as OpenCV and TensorFlow are used for this analysis.

[0196] Step 4:

[0197] The server integrates past order history and sentiment data to select recommended products. The input is order history and sentiment data stored in a database, and the output is a personalized list of recommended products. An artificial intelligence model is used in this process.

[0198] Step 5:

[0199] The terminal displays selected recommended products to the user. The input is a list of recommended products, and the output is product information displayed on the user's smart glasses screen. The user interface makes visual adjustments that match the user's current mood.

[0200] Step 6:

[0201] When a user views a product and reacts to its content, the device captures the change in emotion again using its emotion recognition engine and sends feedback to the server. The input is the newly acquired emotion data, and the output is a readjustment of the product list for the next user. This cycle results in a better purchasing experience that matches the user's emotions.

[0202] 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.

[0203] 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 those described above. 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 shown 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.

[0204] 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.

[0205] [Second Embodiment]

[0206] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0207] 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.

[0208] 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).

[0209] 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.

[0210] 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.

[0211] 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).

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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".

[0218] The order placement and receiving system according to this invention mainly consists of a server, a terminal, and a user. The server first collects internet trend data from external sources and stores it in a database. This includes online search trend information and human flow patterns. The server then analyzes this data using an artificial intelligence model and generates a sales forecast score for each product.

[0219] When a user logs in, the terminal retrieves the user's past order history from the server. During this process, the terminal identifies the user's preferences and purchasing trends, and based on this, recommends products. The terminal then displays these recommended products to the user and provides information about product details and projected demand.

[0220] Users can view details of items of interest from a recommended list via their device and place an order by adding them to their cart. The device sends the product information from the cart to the server, initiating the order processing. At this time, the server checks inventory, and if the items are in stock, it confirms the order and proceeds with payment processing.

[0221] As a concrete example, let's consider a retailer considering purchasing seasonal products using this system. When a user logs into their terminal, the system analyzes data on products expected to be popular this season, such as specific clothing or food items. Based on these results, the server provides a list of recommended products that the user might be interested in. The user can add the recommended products to their cart and complete the order smoothly. This system allows users to make data-driven purchasing decisions and select more appropriate products.

[0222] Therefore, this order placement and receiving system enables rapid and accurate product selection and ordering that reflects current trends.

[0223] The following describes the processing flow.

[0224] Step 1:

[0225] The server periodically collects internet trend data and user flow data from external sources. This includes information such as search trends and visitor behavior based on location. The server also stores this data in a database to prepare for subsequent analysis.

[0226] Step 2:

[0227] The server uses the stored data to perform analysis using an artificial intelligence model. Specifically, it calculates a sales forecast score for each product and stores it in a database. This score is used to predict how much each product will sell in the future, based on past data and trend information.

[0228] Step 3:

[0229] When a user logs in, the terminal retrieves the user's past order history from the server. Based on this data, it analyzes the user's purchasing trends and preferences and requests the server to create a list of products best suited to the user.

[0230] Step 4:

[0231] The server selects recommended products based on the user's profile and analysis results, and sends this list to the terminal. The recommendation list includes product names, sales forecast scores, and inventory status.

[0232] Step 5:

[0233] The device displays a list of recommended products received by the user. The user can view detailed information about each product and add items they like to their cart.

[0234] Step 6:

[0235] The user presses the order button to confirm the order for the items added to their cart. The terminal sends this information to the server and starts processing the order.

[0236] Step 7:

[0237] The server checks inventory based on the received order details. If sufficient inventory is available, it confirms the order, proceeds with payment processing, and sends a confirmation email to the user.

[0238] (Example 1)

[0239] 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."

[0240] Traditional order management systems have the drawback of fixed product selection, making them unable to adequately respond to market changes and user preferences. Furthermore, they fail to accurately forecast demand considering online trends and customer flow patterns, making efficient inventory management and sales strategy development difficult.

[0241] 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.

[0242] In this invention, the server includes means for acquiring and storing trend data from external information sources, means for analyzing the acquired trend data and creating demand forecast indicators associated with items, and means for acquiring the end user's past purchase history and selecting recommended items based on that history. This enables product selection that accurately captures market trends and personalized recommendations based on user preferences.

[0243] "External information sources" refer to data sources obtained from internet platforms and data services, and include trend information and population movement patterns.

[0244] "Trend data" refers to information that reflects market changes and consumer interests, and includes search trends, social media posts, and related news.

[0245] "Storage" refers to keeping collected data in a state where it can be used later, and this includes storing it in databases or cloud storage.

[0246] "Analysis" refers to the process of extracting meaning from collected data and generating useful information and indicators, and includes the use of statistical methods and machine learning.

[0247] A "demand forecast indicator" refers to an indicator that shows the future sales volume and popularity of a product or service in the market using numerical values ​​or evaluations.

[0248] "Purchase history" refers to a record of transactions and purchasing activities that an end user has undertaken to date, including information such as the products purchased, their quantities, and prices.

[0249] "Recommended items" refer to products and services selected based on the preferences and needs of end users, and are based on predictive and historical data.

[0250] The order placement and receiving system according to this invention operates by coordinating the server, terminal, and user elements. A specific embodiment of this system is described below.

[0251] The server first acquires trend data from external sources. This is done through internet information platforms, such as search engine trend data and social media analysis. The acquired data is stored in a database for later analysis. The server uses this data to perform analysis using generative AI models. Specifically, machine learning frameworks such as TensorFlow and PyTorch are used to generate demand forecasting metrics from the data. These metrics relate to sales and demand forecasts and can be used in product sales strategies.

[0252] When a user logs in, the device retrieves their past purchase history from the server. This allows for analysis of the user's preferences and past purchasing trends. Based on this data, the device uses algorithms such as collaborative filtering to select and present recommended items to the user. These recommendations are delivered through dynamically generated web pages and application screens, and are updated in real time using JavaScript and other technologies.

[0253] Users can select items of interest from the recommended products presented and view detailed information. If they like the product, they can add it to their cart and proceed with ordering. The terminal sends the cart information to the server, where inventory checks and purchase procedures are performed. The entire process, including payment, can be carried out smoothly through an online payment system, using the Stripe API or PayPal API.

[0254] For example, if a user wants to buy new sandals that reflect this summer's trends, the server will provide appropriate recommendations based on the latest trend data. A possible prompt would be, "Tell me about this summer's trendy sandals," to which the server would provide a list of recommended products. In response to this prompt, the server presents the best options based on the user's past purchase history and real-time trend data. This allows users to make data-driven purchases and choose the right product.

[0255] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0256] Step 1:

[0257] The server acquires trend data from external sources. Specifically, it uses APIs to collect trend data and population movement data from internet platforms. Inputs include search trend queries and social media posts. After collection, this data is stored in a database on the server, making it available for use in subsequent analysis steps.

[0258] Step 2:

[0259] The server performs analysis using a generative AI model based on acquired trend data. The input data is preprocessed using machine learning frameworks such as TensorFlow and PyTorch, and then demand forecasting indicators are created. This is done through time series analysis and statistical regression, and the output is a sales forecast score for each item. This score indicates the demand for a product and plays an important role in predicting increases or decreases in sales.

[0260] Step 3:

[0261] When a user logs in, the terminal retrieves their past purchase history from the server. The user ID is used as input, and past purchase data is retrieved. This data includes purchased items, purchase dates, prices, etc., and is analyzed on the terminal using a collaborative filtering algorithm. The output is a list of recommended items that reflect the user's preferences.

[0262] Step 4:

[0263] The terminal displays recommended items selected based on the analysis results to the user. Detailed information about the recommended products is provided through the user interface. This operation is performed by updating the displayed content in real time in conjunction with JavaScript. The input uses the ID and details of the recommended product, and the output is displayed as the product name, price, and stock information.

[0264] Step 5:

[0265] Users add items they are interested in to their cart via their device and proceed with the purchase. Specific actions include clicking the purchase button and adding items to the cart. The input is a list of selected items, and the device sends this information to the server. The output is presented as a purchase confirmation screen.

[0266] Step 6:

[0267] The server performs inventory checks and order processing based on the cart information received from the terminal. The input is the cart details; the server checks inventory against the database and confirms the purchase if the item is in stock. The output includes an order confirmation message and payment information. This process integrates with an online payment API to ensure secure payment completion.

[0268] (Application Example 1)

[0269] 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 glasses 214 will be referred to as the "terminal."

[0270] In online marketplaces, accurately understanding consumer purchasing trends and recommending appropriate products in a timely manner is crucial. However, traditional systems often fail to adequately provide customized offers based on individual user preferences or real-time inventory checks, making it difficult to improve the user experience and optimize sales opportunities.

[0271] 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.

[0272] In this invention, the server includes means for collecting and storing online trend data, means for analyzing the collected online trend data and generating sales forecast scores associated with products, and means for obtaining the user's past order history and selecting recommended products based on that history. This makes it possible to provide users with optimized product recommendations and present customized offers and discounts.

[0273] "Internet trend data" refers to information that shows users' activities and interests online, and is used to analyze trends in products and services.

[0274] "Means of collection" refers to a function or device for acquiring necessary data from external sources and incorporating it into a system.

[0275] "Means of analysis" refer to algorithms and systems used to process acquired data and derive information based on a specific purpose.

[0276] The "sales forecast score" is a numerical value used to predict future product sales figures, and is an index generated by a computer based on collected data.

[0277] "User's past order history" refers to a record of a user's past purchasing behavior and is used to understand that user's purchasing trends.

[0278] "Means for selecting recommended products" refers to a function or process for selecting products suitable for a user based on analysis results and user history.

[0279] A "customized offer" is a special proposal, such as a discount or perk, that is provided taking into account the needs and preferences of a particular user.

[0280] "Means of checking inventory information in real time" refers to a system or function for instantly obtaining and checking the current inventory status.

[0281] "Means of displaying to the user" refers to an interface or device for visually providing the user with analyzed or selected information.

[0282] "Means for receiving order instructions" refers to a function that obtains purchase requests from users and processes them within the system.

[0283] This system is an integrated platform consisting of a server, terminals, and users. First, the server collects online trend data using online APIs and web scraping technologies. This data includes information such as search trends and movement patterns. After data collection, the server stores the information in a database and performs analysis using an artificial intelligence model (e.g., a model using TensorFlow) to generate a sales prediction score for each product.

[0284] When the user logs in, the terminal obtains the user's past order history from the server. Based on this history, the terminal grasps the user's preferences and purchasing tendencies and generates personalized product recommendations. At the same time, it performs real-time inventory confirmation processing and presents customized offers and discount information to the user.

[0285] The user can view the recommended products and offers displayed on the terminal and select the products of interest from them. The selected products are added to the cart, and when the user wishes to purchase, payment is made through a payment system (e.g., a system using the Stripe API).

[0286] As a specific example, by using this system, popular sweaters and boots are listed as recommended products during the winter season, and special discounts are provided to users with a past purchase history. In this way, data-based product recommendations and sales promotion are effectively carried out.

[0287] An example of a prompt sentence input into the generative AI model is "Please collect the data necessary to display the list of trendy products for this winter and generate customized recommendations based on the past purchase history."

[0288] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0289] Step 1:

[0290] The server collects internet trend data using online APIs and web scraping techniques. The input consists of search trend and movement pattern data obtained from various online sources. This data is formatted and filtered, processed into a database format, and then stored in the database.

[0291] Step 2:

[0292] The server runs a generative AI model using internet trend data stored in the database. The input is the trend data stored in the previous step. The AI ​​model analyzes this data and outputs a sales forecast score for each product. This model uses TensorFlow to predict sales trends.

[0293] Step 3:

[0294] When a user logs into their terminal, the terminal retrieves their past order history from the server. The input is the user ID, and the output is the user's past purchase history data. Based on this history data, the system analyzes the user's preferences and trends.

[0295] Step 4:

[0296] The terminal references the acquired purchase history and the server's sales forecast score to generate a personalized list of recommended products for the user. The input is past purchase history and sales forecast score, and the output is a list of recommended products. This process uses history matching and filtering algorithms.

[0297] Step 5:

[0298] The server accesses the inventory database in real time and checks it on the terminal. The input is a list of recommended products, and the output is information on whether or not they are in stock. The server verifies the inventory status and selects only the products that can be provided to the user.

[0299] Step 6:

[0300] Users view recommended products and customized offers on their device and add items of interest to their cart. Here, user selections are input, and the updated cart information is output. Based on user selections, the device displays high-priority products.

[0301] Step 7:

[0302] The terminal initiates the payment process, and the server executes the payment using the payment provider's API. Inputs are the selected items in the shopping cart and payment information, while output is the payment confirmation status. Once payment is complete, the order is finalized.

[0303] 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.

[0304] The order placement and receiving system according to the present invention achieves even more accurate product recommendations by incorporating an emotion engine that recognizes user emotions, in addition to analysis based on internet trend data and human flow data. The server acquires internet trend data and human flow data from external sources and stores them in a database. Using this information, the server analyzes this data through an artificial intelligence model and generates sales forecast scores for products.

[0305] Furthermore, the terminal utilizes an emotion engine to acquire user emotion data when the user registers. The emotion engine recognizes emotions from the user's facial expressions and voice input and stores them in a database. The server integrates past order history and emotion data to select recommended products.

[0306] For the user, this system provides a mechanism where recommended products are automatically displayed based on the user's past purchase history and current emotional state when they log in. More specifically, the terminal provides a more personalized experience by dynamically adjusting the product list according to the user's emotions.

[0307] As a specific example, consider the situation where a user is looking for discounted products. When the user is using the terminal, if the emotion engine recognizes excitement or joy, the server adjusts the recommendation list based on this to highlight the discounted products in a more prominent way than usual. This makes it easier for the user to immediately find products that match their mood.

[0308] This system can improve the marketing effect by simultaneously grasping the user's needs and current emotional state and optimizing product recommendations in real time.

[0309] The following describes the processing flow.

[0310] Step 1:

[0311] The server collects net trend data and pedestrian flow data from external sources and stores them in a database. This includes purchase patterns based on search trends and location information.

[0312] Step 2:

[0313] When the user logs in, the terminal collects emotion data using the camera and microphone built into the user's device. This enables reading emotions from the user's facial expressions and tone of voice.

[0314] Step 3:

[0315] The server integrates the acquired emotion data and past order history to update the user profile. Based on this data, an artificial intelligence model is used to select personalized recommended products for the user.

[0316] Step 4:

[0317] The terminal displays a list of recommended products sent from the server to the user. This list is dynamically adjusted based on sales forecast scores and sentiment analysis results.

[0318] Step 5:

[0319] Users can view a list of recommendations and see details of products that interest them. They can then add desired items to their cart and proceed with the purchase.

[0320] Step 6:

[0321] The terminal sends the cart information to the server and initiates inventory checks and payment processing.

[0322] Step 7:

[0323] The server checks for stock availability and confirms the purchase. It then sends the user an email containing order confirmation and delivery date.

[0324] Step 8:

[0325] The device collects feedback on user purchasing behavior and reports it to the server to improve future recommendation quality.

[0326] This process allows users to receive product recommendations tailored to their emotional state, resulting in a more satisfying shopping experience.

[0327] (Example 2)

[0328] 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".

[0329] Traditional order management systems select recommended products based solely on past user purchasing history, resulting in insufficient personalization that takes into account real-time sentiment and trend information. Therefore, more accurate product recommendations are needed to improve the user experience and enhance marketing effectiveness.

[0330] 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.

[0331] In this invention, the server includes means for collecting and storing internet trend information and human flow information from electronic devices, means for analyzing the collected internet trend information and human flow information and generating predictive scores associated with products, and means for acquiring emotional information using an emotional recognition device when a user registers. This enables more personalized product recommendations based on the user's emotional information and trend information.

[0332] "Internet trend information" refers to data that shows consumer behavior, trends, and popular topics on the internet.

[0333] "Human flow information" refers to data that shows the movement patterns and distribution of people in a particular region or location.

[0334] An "emotion recognition device" is a device or software that analyzes the user's facial expressions and voice to identify their emotional state.

[0335] A "storage device" is hardware or software used to store collected information for a long period of time.

[0336] A "generative artificial intelligence model" is an algorithm that automatically generates new insights and predictions based on a wide variety of collected data.

[0337] A "predictive score" is an indicator that quantifies the future sales and demand for a product or service, generated based on analyzed data.

[0338] "Electronic devices" is a general term referring to computers and smart devices used for data collection and processing.

[0339] A "user" refers to an individual or legal entity that uses the system to place or receive orders for goods or services.

[0340] This invention is an order placement and receiving system that provides users with appropriate product recommendations by acquiring and analyzing multiple data points via a network. The main functions of the system are implemented by a server and terminals.

[0341] The server collects internet trend and human flow information from external data providers. This data is stored in a database and later serves as foundational material for analysis. RESTful APIs and cloud data services are often used for this data collection.

[0342] Next, the server analyzes the information in the database using a generative artificial intelligence model. The AI ​​model generates sales forecast scores related to products from the collected data. For example, prompts such as "predict next month's consumer trends and use them to boost sales" are input to the AI ​​model. In this process, machine learning libraries and data analysis software using Python are commonly used.

[0343] On the other hand, the terminal uses an emotion recognition device when the user accesses the system. User emotion information is obtained by analyzing facial expressions and voice input, and this data is recorded in a database. Facial expression analysis software and voice recognition software are utilized in this process.

[0344] For example, when a user shows interest in a particular event, an emotion recognition device detects that excitement, and the server immediately updates its recommendations for related products. This dynamic recommendation system allows users to efficiently find products that match their current emotions and needs.

[0345] This system aims to improve marketing effectiveness by integrating past purchase history with real-time sentiment data to provide users with more personalized product recommendations.

[0346] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0347] Step 1:

[0348] The server collects online trend information and pedestrian flow information from external data sources. This information is obtained via an API and sent to the server in JSON format. The server receives this information and stores it in a database. Specifically, upon receiving the data, the server removes duplicate data, extracts the necessary fields, and accurately stores them in the database.

[0349] Step 2:

[0350] The server performs data analysis using a generative AI model based on collected data stored in the database. The inputs used are online trend information and pedestrian flow information. The AI ​​model processes this data and outputs a sales forecast score related to the product. Specifically, it predicts future sales trends by comparing them with past trend data. This model is implemented using machine learning libraries such as TensorFlow.

[0351] Step 3:

[0352] The terminal activates the emotion recognition device when the user logs into the system. It receives input from the user's facial expressions and voice, analyzes it, and generates emotion data. The emotion recognition device analyzes how excited or happy the user is and stores this information in a database. The input uses the user's facial recognition data and voice data, and the output is data indicating the user's current emotional state.

[0353] Step 4:

[0354] The server integrates user emotional data and past purchase history, and analyzes it through a generative AI model. This integrated analysis generates data to recommend the most appropriate products to the user. Through the AI ​​model, the server interprets how the user's current emotional state influences their purchasing intent and determines the priority of recommended products.

[0355] Step 5:

[0356] The terminal displays a list of recommended products on the user's screen based on instructions from the server. The arrangement and visual effects of the product list are dynamically changed according to the user's emotional state. For example, if the user is expressing joy, discounted products are highlighted to create a visual impact. In this way, an environment is provided where users can easily find products that interest them.

[0357] (Application Example 2)

[0358] 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."

[0359] Existing order management systems have the challenge of not being able to reflect users' current emotions and interests, as product recommendations are based on users' past purchase history and online behavior data. This results in a failure to maximize user purchasing intent, leading to limited marketing effectiveness.

[0360] 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.

[0361] In this invention, the server includes means for collecting and storing internet trend data, means for analyzing the collected internet trend data and generating sales forecast scores associated with products, and means for recognizing the user's emotions and adjusting product recommendations based on emotion data. This makes it possible to provide personalized product recommendations in real time that are tailored to the user's emotional state.

[0362] "Internet trend data" refers to data that shows user behavior, interests, and trends on the internet.

[0363] A "sales forecast score" is an indicator generated to predict a product's future sales and is used in selecting recommended products.

[0364] "Past order history" refers to data that records the user's past purchase history of products.

[0365] "Recommended products" are products selected by the system based on the user's purchase history and emotional state.

[0366] "Emotional data" refers to information that indicates a user's emotional state, obtained by analyzing the user's facial expressions, voice, and other data.

[0367] An "artificial intelligence model" is a general term for algorithms and frameworks that automatically perform analysis and predictions based on diverse data.

[0368] In embodiments of the present invention, a server collects internet trend data and pedestrian flow data from external sources and stores this data in a database. The collected data is analyzed using software such as Python, pandas, and scikit-learn. This generates a sales forecast score for products. An artificial intelligence model is used for this, and it is integrated with the user's past order history.

[0369] Furthermore, the device incorporates an emotion recognition engine that captures and analyzes the user's facial expressions and voice in real time through the camera and microphone. This emotion data is also stored in a database. When a user logs in, the device utilizes this emotion data to dynamically adjust and display recommended products based on their state.

[0370] In an example of smart glasses use, the server displays a list of appropriate products based on data obtained from the user's facial expressions and voice when they view products. This makes it easy for the user to find products that match their current emotions.

[0371] For example, if a user is wearing smart glasses when going out on a rainy day, the system will prioritize displaying related products such as umbrellas and raincoats. This provides a comfortable shopping environment where users can find products that suit their mood that day.

[0372] Examples of prompt statements are as follows:

[0373] "If we sense 'undergone disappointment' rather than 'neutral' in the user's expression, we will add items from the appropriate product category to the recommendation list."

[0374] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0375] Step 1:

[0376] The server collects internet trend data and pedestrian flow data from external sources. Inputs are external APIs and data feeds, and output is raw data stored in a database. This data serves as foundational information for subsequent analysis.

[0377] Step 2:

[0378] The server analyzes the collected data and generates a sales forecast score for the products. The input is internet trend data and pedestrian flow data stored in a database, and the output is the forecast score. This process is performed using Python and scikit-learn, applying a machine learning model.

[0379] Step 3:

[0380] The device acquires the user's facial expressions and voice in real time and analyzes them with an emotion recognition engine. The input is raw data acquired by the device's camera and microphone, and the output is the user's emotion data. Libraries such as OpenCV and TensorFlow are used for this analysis.

[0381] Step 4:

[0382] The server integrates past order history and sentiment data to select recommended products. The input is order history and sentiment data stored in a database, and the output is a personalized list of recommended products. An artificial intelligence model is used in this process.

[0383] Step 5:

[0384] The terminal displays selected recommended products to the user. The input is a list of recommended products, and the output is product information displayed on the user's smart glasses screen. The user interface makes visual adjustments that match the user's current mood.

[0385] Step 6:

[0386] When a user views a product and reacts to its content, the device captures the change in emotion again using its emotion recognition engine and sends feedback to the server. The input is the newly acquired emotion data, and the output is a readjustment of the product list for the next user. This cycle results in a better purchasing experience that matches the user's emotions.

[0387] 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.

[0388] 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 those described above. 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 shown 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.

[0389] 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.

[0390] [Third Embodiment]

[0391] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0392] 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.

[0393] 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).

[0394] 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.

[0395] 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.

[0396] 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).

[0397] 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.

[0398] 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.

[0399] 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.

[0400] 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.

[0401] 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.

[0402] 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".

[0403] The order placement and receiving system according to this invention mainly consists of a server, a terminal, and a user. The server first collects internet trend data from external sources and stores it in a database. This includes online search trend information and human flow patterns. The server then analyzes this data using an artificial intelligence model and generates a sales forecast score for each product.

[0404] When a user logs in, the terminal retrieves the user's past order history from the server. During this process, the terminal identifies the user's preferences and purchasing trends, and based on this, recommends products. The terminal then displays these recommended products to the user and provides information about product details and projected demand.

[0405] Users can view details of items of interest from a recommended list via their device and place an order by adding them to their cart. The device sends the product information from the cart to the server, initiating the order processing. At this time, the server checks inventory, and if the items are in stock, it confirms the order and proceeds with payment processing.

[0406] As a concrete example, let's consider a retailer considering purchasing seasonal products using this system. When a user logs into their terminal, the system analyzes data on products expected to be popular this season, such as specific clothing or food items. Based on these results, the server provides a list of recommended products that the user might be interested in. The user can add the recommended products to their cart and complete the order smoothly. This system allows users to make data-driven purchasing decisions and select more appropriate products.

[0407] Therefore, this order placement and receiving system enables rapid and accurate product selection and ordering that reflects current trends.

[0408] The following describes the processing flow.

[0409] Step 1:

[0410] The server periodically collects internet trend data and user flow data from external sources. This includes information such as search trends and visitor behavior based on location. The server also stores this data in a database to prepare for subsequent analysis.

[0411] Step 2:

[0412] The server uses the stored data to perform analysis using an artificial intelligence model. Specifically, it calculates a sales forecast score for each product and stores it in a database. This score is used to predict how much each product will sell in the future, based on past data and trend information.

[0413] Step 3:

[0414] When a user logs in, the terminal retrieves the user's past order history from the server. Based on this data, it analyzes the user's purchasing trends and preferences and requests the server to create a list of products best suited to the user.

[0415] Step 4:

[0416] The server selects recommended products based on the user's profile and analysis results, and sends this list to the terminal. The recommendation list includes product names, sales forecast scores, and inventory status.

[0417] Step 5:

[0418] The device displays a list of recommended products received by the user. The user can view detailed information about each product and add items they like to their cart.

[0419] Step 6:

[0420] The user presses the order button to confirm the order for the items added to their cart. The terminal sends this information to the server and starts processing the order.

[0421] Step 7:

[0422] The server checks inventory based on the received order details. If sufficient inventory is available, it confirms the order, proceeds with payment processing, and sends a confirmation email to the user.

[0423] (Example 1)

[0424] 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."

[0425] Traditional order management systems have the drawback of fixed product selection, making them unable to adequately respond to market changes and user preferences. Furthermore, they fail to accurately forecast demand considering online trends and customer flow patterns, making efficient inventory management and sales strategy development difficult.

[0426] 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.

[0427] In this invention, the server includes means for acquiring and storing trend data from external information sources, means for analyzing the acquired trend data and creating demand forecast indicators associated with items, and means for acquiring the end user's past purchase history and selecting recommended items based on that history. This enables product selection that accurately captures market trends and personalized recommendations based on user preferences.

[0428] "External information sources" refer to data sources obtained from internet platforms and data services, and include trend information and population movement patterns.

[0429] "Trend data" refers to information that reflects market changes and consumer interests, and includes search trends, social media posts, and related news.

[0430] "Storage" refers to keeping collected data in a state where it can be used later, and this includes storing it in databases or cloud storage.

[0431] "Analysis" refers to the process of extracting meaning from collected data and generating useful information and indicators, and includes the use of statistical methods and machine learning.

[0432] A "demand forecast indicator" refers to an indicator that shows the future sales volume and popularity of a product or service in the market using numerical values ​​or evaluations.

[0433] "Purchase history" refers to a record of transactions and purchasing activities that an end user has undertaken to date, including information such as the products purchased, their quantities, and prices.

[0434] "Recommended items" refer to products and services selected based on the preferences and needs of end users, and are based on predictive and historical data.

[0435] The order placement and receiving system according to this invention operates by coordinating the server, terminal, and user elements. A specific embodiment of this system is described below.

[0436] The server first acquires trend data from external sources. This is done through internet information platforms, such as search engine trend data and social media analysis. The acquired data is stored in a database for later analysis. The server uses this data to perform analysis using generative AI models. Specifically, machine learning frameworks such as TensorFlow and PyTorch are used to generate demand forecasting metrics from the data. These metrics relate to sales and demand forecasts and can be used in product sales strategies.

[0437] When a user logs in, the device retrieves their past purchase history from the server. This allows for analysis of the user's preferences and past purchasing trends. Based on this data, the device uses algorithms such as collaborative filtering to select and present recommended items to the user. These recommendations are delivered through dynamically generated web pages and application screens, and are updated in real time using JavaScript and other technologies.

[0438] Users can select items of interest from the recommended products presented and view detailed information. If they like the product, they can add it to their cart and proceed with ordering. The terminal sends the cart information to the server, where inventory checks and purchase procedures are performed. The entire process, including payment, can be carried out smoothly through an online payment system, using the Stripe API or PayPal API.

[0439] For example, if a user wants to buy new sandals that reflect this summer's trends, the server will provide appropriate recommendations based on the latest trend data. A possible prompt would be, "Tell me about this summer's trendy sandals," to which the server would provide a list of recommended products. In response to this prompt, the server presents the best options based on the user's past purchase history and real-time trend data. This allows users to make data-driven purchases and choose the right product.

[0440] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0441] Step 1:

[0442] The server acquires trend data from external sources. Specifically, it uses APIs to collect trend data and population movement data from internet platforms. Inputs include search trend queries and social media posts. After collection, this data is stored in a database on the server, making it available for use in subsequent analysis steps.

[0443] Step 2:

[0444] The server performs analysis using a generative AI model based on acquired trend data. The input data is preprocessed using machine learning frameworks such as TensorFlow and PyTorch, and then demand forecasting indicators are created. This is done through time series analysis and statistical regression, and the output is a sales forecast score for each item. This score indicates the demand for a product and plays an important role in predicting increases or decreases in sales.

[0445] Step 3:

[0446] When a user logs in, the terminal retrieves their past purchase history from the server. The user ID is used as input, and past purchase data is retrieved. This data includes purchased items, purchase dates, prices, etc., and is analyzed on the terminal using a collaborative filtering algorithm. The output is a list of recommended items that reflect the user's preferences.

[0447] Step 4:

[0448] The terminal displays recommended items selected based on the analysis results to the user. Detailed information about the recommended products is provided through the user interface. This operation is performed by updating the displayed content in real time in conjunction with JavaScript. The input uses the ID and details of the recommended product, and the output is displayed as the product name, price, and stock information.

[0449] Step 5:

[0450] Users add items they are interested in to their cart via their device and proceed with the purchase. Specific actions include clicking the purchase button and adding items to the cart. The input is a list of selected items, and the device sends this information to the server. The output is presented as a purchase confirmation screen.

[0451] Step 6:

[0452] The server performs inventory checks and order processing based on the cart information received from the terminal. The input is the cart details; the server checks inventory against the database and confirms the purchase if the item is in stock. The output includes an order confirmation message and payment information. This process integrates with an online payment API to ensure secure payment completion.

[0453] (Application Example 1)

[0454] 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."

[0455] In online marketplaces, accurately understanding consumer purchasing trends and recommending appropriate products in a timely manner is crucial. However, traditional systems often fail to adequately provide customized offers based on individual user preferences or real-time inventory checks, making it difficult to improve the user experience and optimize sales opportunities.

[0456] 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.

[0457] In this invention, the server includes means for collecting and storing online trend data, means for analyzing the collected online trend data and generating sales forecast scores associated with products, and means for obtaining the user's past order history and selecting recommended products based on that history. This makes it possible to provide users with optimized product recommendations and present customized offers and discounts.

[0458] "Internet trend data" refers to information that shows users' activities and interests online, and is used to analyze trends in products and services.

[0459] "Means of collection" refers to a function or device for acquiring necessary data from external sources and incorporating it into a system.

[0460] "Means of analysis" refer to algorithms and systems used to process acquired data and derive information based on a specific purpose.

[0461] The "sales forecast score" is a numerical value used to predict future product sales figures, and is an index generated by a computer based on collected data.

[0462] "User's past order history" refers to a record of a user's past purchasing behavior and is used to understand that user's purchasing trends.

[0463] "Means for selecting recommended products" refers to a function or process for selecting products suitable for a user based on analysis results and user history.

[0464] A "customized offer" is a special proposal, such as a discount or perk, that is provided taking into account the needs and preferences of a particular user.

[0465] "Means of checking inventory information in real time" refers to a system or function for instantly obtaining and checking the current inventory status.

[0466] "Means of displaying to the user" refers to an interface or device for visually providing the user with analyzed or selected information.

[0467] "Means for receiving order instructions" refers to a function that obtains purchase requests from users and processes them within the system.

[0468] This system is an integrated platform consisting of servers, terminals, and users. The servers first collect online trend data using online APIs and web scraping techniques. This data includes information such as search trends and movement patterns. After collection, the servers store this information in a database and perform analysis using artificial intelligence models (for example, models using TensorFlow) to generate sales forecast scores for each product.

[0469] When a user logs in, the terminal retrieves the user's past order history from the server. Based on this history, the terminal understands the user's preferences and purchasing trends and generates personalized product recommendations. At the same time, it performs real-time inventory checks and presents the user with customized offers and discount information.

[0470] Users can view recommended products and offers displayed on their device and select items that interest them. Selected items are added to the cart, and when the user wishes to purchase them, they make a payment through a payment system (for example, a system using the Stripe API).

[0471] For example, using this system, popular sweaters and boots are recommended during the winter season, and special discounts are offered to users with a past purchase history. In this way, data-driven product recommendations and sales promotions are effectively implemented.

[0472] An example of a prompt to input into the generating AI model is: "Collect the necessary data to display a list of trending products for this winter and generate customized recommendations based on past purchase history."

[0473] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0474] Step 1:

[0475] The server collects internet trend data using online APIs and web scraping techniques. The input consists of search trend and movement pattern data obtained from various online sources. This data is formatted and filtered, processed into a database format, and then stored in the database.

[0476] Step 2:

[0477] The server runs a generative AI model using internet trend data stored in the database. The input is the trend data stored in the previous step. The AI ​​model analyzes this data and outputs a sales forecast score for each product. This model uses TensorFlow to predict sales trends.

[0478] Step 3:

[0479] When a user logs into their terminal, the terminal retrieves their past order history from the server. The input is the user ID, and the output is the user's past purchase history data. Based on this history data, the system analyzes the user's preferences and trends.

[0480] Step 4:

[0481] The terminal references the acquired purchase history and the server's sales forecast score to generate a personalized list of recommended products for the user. The input is past purchase history and sales forecast score, and the output is a list of recommended products. This process uses history matching and filtering algorithms.

[0482] Step 5:

[0483] The server accesses the inventory database in real time and checks it on the terminal. The input is a list of recommended products, and the output is information on whether or not they are in stock. The server verifies the inventory status and selects only the products that can be provided to the user.

[0484] Step 6:

[0485] Users view recommended products and customized offers on their device and add items of interest to their cart. Here, user selections are input, and the updated cart information is output. Based on user selections, the device displays high-priority products.

[0486] Step 7:

[0487] The terminal initiates the payment process, and the server executes the payment using the payment provider's API. Inputs are the selected items in the shopping cart and payment information, while output is the payment confirmation status. Once payment is complete, the order is finalized.

[0488] 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.

[0489] The order placement and receiving system according to the present invention achieves even more accurate product recommendations by incorporating an emotion engine that recognizes user emotions, in addition to analysis based on internet trend data and human flow data. The server acquires internet trend data and human flow data from external sources and stores them in a database. Using this information, the server analyzes this data through an artificial intelligence model and generates sales forecast scores for products.

[0490] Furthermore, the terminal utilizes an emotion engine to acquire user emotion data when the user registers. The emotion engine recognizes emotions from the user's facial expressions and voice input and stores them in a database. The server integrates past order history and emotion data to select recommended products.

[0491] For users, this system provides a mechanism that automatically displays recommended products based on past purchase history and current emotional state upon login. More specifically, the device dynamically adjusts the product list according to the user's emotions, providing a more personalized experience.

[0492] As a concrete example, consider a situation where a user is searching for sale items. When the emotion engine recognizes excitement or joy while the user is using their device, the server adjusts the recommendation list based on this, highlighting sale items in a more prominent way than usual. This makes it easier for the user to instantly find items that match their mood.

[0493] This system can improve marketing effectiveness by simultaneously understanding user needs and current emotional states, and optimizing product recommendations in real time.

[0494] The following describes the processing flow.

[0495] Step 1:

[0496] The server collects internet trend data and human traffic data from external sources and stores this data in a database. This includes search trends and purchasing patterns based on location information.

[0497] Step 2:

[0498] When a user logs in, the device uses the camera and microphone built into the user's device to collect emotional data. This allows the system to read the user's emotions from their facial expressions and tone of voice.

[0499] Step 3:

[0500] The server integrates acquired sentiment data with past order history and updates the user profile. Based on this data, an artificial intelligence model is used to select personalized product recommendations for each user.

[0501] Step 4:

[0502] The terminal displays a list of recommended products sent from the server to the user. This list is dynamically adjusted based on sales forecast scores and sentiment analysis results.

[0503] Step 5:

[0504] Users can view a list of recommendations and see details of products that interest them. They can then add desired items to their cart and proceed with the purchase.

[0505] Step 6:

[0506] The terminal sends the cart information to the server and initiates inventory checks and payment processing.

[0507] Step 7:

[0508] The server checks for stock availability and confirms the purchase. It then sends the user an email containing order confirmation and delivery date.

[0509] Step 8:

[0510] The device collects feedback on user purchasing behavior and reports it to the server to improve future recommendation quality.

[0511] This process allows users to receive product recommendations tailored to their emotional state, resulting in a more satisfying shopping experience.

[0512] (Example 2)

[0513] 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."

[0514] Traditional order management systems select recommended products based solely on past user purchasing history, resulting in insufficient personalization that takes into account real-time sentiment and trend information. Therefore, more accurate product recommendations are needed to improve the user experience and enhance marketing effectiveness.

[0515] 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.

[0516] In this invention, the server includes means for collecting and storing internet trend information and human flow information from electronic devices, means for analyzing the collected internet trend information and human flow information and generating predictive scores associated with products, and means for acquiring emotional information using an emotional recognition device when a user registers. This enables more personalized product recommendations based on the user's emotional information and trend information.

[0517] "Internet trend information" refers to data that shows consumer behavior, trends, and popular topics on the internet.

[0518] "Human flow information" refers to data that shows the movement patterns and distribution of people in a particular region or location.

[0519] An "emotion recognition device" is a device or software that analyzes the user's facial expressions and voice to identify their emotional state.

[0520] A "storage device" is hardware or software used to store collected information for a long period of time.

[0521] A "generative artificial intelligence model" is an algorithm that automatically generates new insights and predictions based on a wide variety of collected data.

[0522] A "predictive score" is an indicator that quantifies the future sales and demand for a product or service, generated based on analyzed data.

[0523] "Electronic devices" is a general term referring to computers and smart devices used for data collection and processing.

[0524] A "user" refers to an individual or legal entity that uses the system to place or receive orders for goods or services.

[0525] This invention is an order placement and receiving system that provides users with appropriate product recommendations by acquiring and analyzing multiple data points via a network. The main functions of the system are implemented by a server and terminals.

[0526] The server collects internet trend and human flow information from external data providers. This data is stored in a database and later serves as foundational material for analysis. RESTful APIs and cloud data services are often used for this data collection.

[0527] Next, the server analyzes the information in the database using a generative artificial intelligence model. The AI ​​model generates sales forecast scores related to products from the collected data. For example, prompts such as "predict next month's consumer trends and use them to boost sales" are input to the AI ​​model. In this process, machine learning libraries and data analysis software using Python are commonly used.

[0528] On the other hand, the terminal uses an emotion recognition device when the user accesses the system. User emotion information is obtained by analyzing facial expressions and voice input, and this data is recorded in a database. Facial expression analysis software and voice recognition software are utilized in this process.

[0529] For example, when a user shows interest in a particular event, an emotion recognition device detects that excitement, and the server immediately updates its recommendations for related products. This dynamic recommendation system allows users to efficiently find products that match their current emotions and needs.

[0530] This system aims to improve marketing effectiveness by integrating past purchase history with real-time sentiment data to provide users with more personalized product recommendations.

[0531] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0532] Step 1:

[0533] The server collects online trend information and pedestrian flow information from external data sources. This information is obtained via an API and sent to the server in JSON format. The server receives this information and stores it in a database. Specifically, upon receiving the data, the server removes duplicate data, extracts the necessary fields, and accurately stores them in the database.

[0534] Step 2:

[0535] The server performs data analysis using a generative AI model based on collected data stored in the database. The inputs used are online trend information and pedestrian flow information. The AI ​​model processes this data and outputs a sales forecast score related to the product. Specifically, it predicts future sales trends by comparing them with past trend data. This model is implemented using machine learning libraries such as TensorFlow.

[0536] Step 3:

[0537] The terminal activates the emotion recognition device when the user logs into the system. It receives input from the user's facial expressions and voice, analyzes it, and generates emotion data. The emotion recognition device analyzes how excited or happy the user is and stores this information in a database. The input uses the user's facial recognition data and voice data, and the output is data indicating the user's current emotional state.

[0538] Step 4:

[0539] The server integrates user emotional data and past purchase history, and analyzes it through a generative AI model. This integrated analysis generates data to recommend the most appropriate products to the user. Through the AI ​​model, the server interprets how the user's current emotional state influences their purchasing intent and determines the priority of recommended products.

[0540] Step 5:

[0541] The terminal displays a list of recommended products on the user's screen based on instructions from the server. The arrangement and visual effects of the product list are dynamically changed according to the user's emotional state. For example, if the user is expressing joy, discounted products are highlighted to create a visual impact. In this way, an environment is provided where users can easily find products that interest them.

[0542] (Application Example 2)

[0543] 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."

[0544] Existing order management systems have the challenge of not being able to reflect users' current emotions and interests, as product recommendations are based on users' past purchase history and online behavior data. This results in a failure to maximize user purchasing intent, leading to limited marketing effectiveness.

[0545] 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.

[0546] In this invention, the server includes means for collecting and storing internet trend data, means for analyzing the collected internet trend data and generating sales forecast scores associated with products, and means for recognizing the user's emotions and adjusting product recommendations based on emotion data. This makes it possible to provide personalized product recommendations in real time that are tailored to the user's emotional state.

[0547] "Internet trend data" refers to data that shows user behavior, interests, and trends on the internet.

[0548] A "sales forecast score" is an indicator generated to predict a product's future sales and is used in selecting recommended products.

[0549] "Past order history" refers to data that records the user's past purchase history of products.

[0550] "Recommended products" are products selected by the system based on the user's purchase history and emotional state.

[0551] "Emotional data" refers to information that indicates a user's emotional state, obtained by analyzing the user's facial expressions, voice, and other data.

[0552] An "artificial intelligence model" is a general term for algorithms and frameworks that automatically perform analysis and predictions based on diverse data.

[0553] In embodiments of the present invention, a server collects internet trend data and pedestrian flow data from external sources and stores this data in a database. The collected data is analyzed using software such as Python, pandas, and scikit-learn. This generates a sales forecast score for products. An artificial intelligence model is used for this, and it is integrated with the user's past order history.

[0554] Furthermore, the device incorporates an emotion recognition engine that captures and analyzes the user's facial expressions and voice in real time through the camera and microphone. This emotion data is also stored in a database. When a user logs in, the device utilizes this emotion data to dynamically adjust and display recommended products based on their state.

[0555] In an example of smart glasses use, the server displays a list of appropriate products based on data obtained from the user's facial expressions and voice when they view products. This makes it easy for the user to find products that match their current emotions.

[0556] For example, if a user is wearing smart glasses when going out on a rainy day, the system will prioritize displaying related products such as umbrellas and raincoats. This provides a comfortable shopping environment where users can find products that suit their mood that day.

[0557] Examples of prompt statements are as follows:

[0558] "If we sense 'undergone disappointment' rather than 'neutral' in the user's expression, we will add items from the appropriate product category to the recommendation list."

[0559] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0560] Step 1:

[0561] The server collects internet trend data and pedestrian flow data from external sources. Inputs are external APIs and data feeds, and output is raw data stored in a database. This data serves as foundational information for subsequent analysis.

[0562] Step 2:

[0563] The server analyzes the collected data and generates a sales forecast score for the products. The input is internet trend data and pedestrian flow data stored in a database, and the output is the forecast score. This process is performed using Python and scikit-learn, applying a machine learning model.

[0564] Step 3:

[0565] The device acquires the user's facial expressions and voice in real time and analyzes them with an emotion recognition engine. The input is raw data acquired by the device's camera and microphone, and the output is the user's emotion data. Libraries such as OpenCV and TensorFlow are used for this analysis.

[0566] Step 4:

[0567] The server integrates past order history and sentiment data to select recommended products. The input is order history and sentiment data stored in a database, and the output is a personalized list of recommended products. An artificial intelligence model is used in this process.

[0568] Step 5:

[0569] The terminal displays selected recommended products to the user. The input is a list of recommended products, and the output is product information displayed on the user's smart glasses screen. The user interface makes visual adjustments that match the user's current mood.

[0570] Step 6:

[0571] When a user views a product and reacts to its content, the device captures the change in emotion again using its emotion recognition engine and sends feedback to the server. The input is the newly acquired emotion data, and the output is a readjustment of the product list for the next user. This cycle results in a better purchasing experience that matches the user's emotions.

[0572] 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.

[0573] 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 those described above. 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 shown 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.

[0574] 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.

[0575] [Fourth Embodiment]

[0576] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0577] 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.

[0578] 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).

[0579] 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.

[0580] 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.

[0581] 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).

[0582] 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.

[0583] 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.

[0584] 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.

[0585] 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.

[0586] 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.

[0587] 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.

[0588] 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".

[0589] The order placement and receiving system according to this invention mainly consists of a server, a terminal, and a user. The server first collects internet trend data from external sources and stores it in a database. This includes online search trend information and human flow patterns. The server then analyzes this data using an artificial intelligence model and generates a sales forecast score for each product.

[0590] When a user logs in, the terminal retrieves the user's past order history from the server. During this process, the terminal identifies the user's preferences and purchasing trends, and based on this, recommends products. The terminal then displays these recommended products to the user and provides information about product details and projected demand.

[0591] Users can view details of items of interest from a recommended list via their device and place an order by adding them to their cart. The device sends the product information from the cart to the server, initiating the order processing. At this time, the server checks inventory, and if the items are in stock, it confirms the order and proceeds with payment processing.

[0592] As a concrete example, let's consider a retailer considering purchasing seasonal products using this system. When a user logs into their terminal, the system analyzes data on products expected to be popular this season, such as specific clothing or food items. Based on these results, the server provides a list of recommended products that the user might be interested in. The user can add the recommended products to their cart and complete the order smoothly. This system allows users to make data-driven purchasing decisions and select more appropriate products.

[0593] Therefore, this order placement and receiving system enables rapid and accurate product selection and ordering that reflects current trends.

[0594] The following describes the processing flow.

[0595] Step 1:

[0596] The server periodically collects internet trend data and user flow data from external sources. This includes information such as search trends and visitor behavior based on location. The server also stores this data in a database to prepare for subsequent analysis.

[0597] Step 2:

[0598] The server uses the stored data to perform analysis using an artificial intelligence model. Specifically, it calculates a sales forecast score for each product and stores it in a database. This score is used to predict how much each product will sell in the future, based on past data and trend information.

[0599] Step 3:

[0600] When a user logs in, the terminal retrieves the user's past order history from the server. Based on this data, it analyzes the user's purchasing trends and preferences and requests the server to create a list of products best suited to the user.

[0601] Step 4:

[0602] The server selects recommended products based on the user's profile and analysis results, and sends this list to the terminal. The recommendation list includes product names, sales forecast scores, and inventory status.

[0603] Step 5:

[0604] The device displays a list of recommended products received by the user. The user can view detailed information about each product and add items they like to their cart.

[0605] Step 6:

[0606] The user presses the order button to confirm the order for the items added to their cart. The terminal sends this information to the server and starts processing the order.

[0607] Step 7:

[0608] The server checks inventory based on the received order details. If sufficient inventory is available, it confirms the order, proceeds with payment processing, and sends a confirmation email to the user.

[0609] (Example 1)

[0610] 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".

[0611] Traditional order management systems have the drawback of fixed product selection, making them unable to adequately respond to market changes and user preferences. Furthermore, they fail to accurately forecast demand considering online trends and customer flow patterns, making efficient inventory management and sales strategy development difficult.

[0612] 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.

[0613] In this invention, the server includes means for acquiring and storing trend data from external information sources, means for analyzing the acquired trend data and creating demand forecast indicators associated with items, and means for acquiring the end user's past purchase history and selecting recommended items based on that history. This enables product selection that accurately captures market trends and personalized recommendations based on user preferences.

[0614] "External information sources" refer to data sources obtained from internet platforms and data services, and include trend information and population movement patterns.

[0615] "Trend data" refers to information that reflects market changes and consumer interests, and includes search trends, social media posts, and related news.

[0616] "Storage" refers to keeping collected data in a state where it can be used later, and this includes storing it in databases or cloud storage.

[0617] "Analysis" refers to the process of extracting meaning from collected data and generating useful information and indicators, and includes the use of statistical methods and machine learning.

[0618] A "demand forecast indicator" refers to an indicator that shows the future sales volume and popularity of a product or service in the market using numerical values ​​or evaluations.

[0619] "Purchase history" refers to a record of transactions and purchasing activities that an end user has undertaken to date, including information such as the products purchased, their quantities, and prices.

[0620] "Recommended items" refer to products and services selected based on the preferences and needs of end users, and are based on predictive and historical data.

[0621] The order placement and receiving system according to this invention operates by coordinating the server, terminal, and user elements. A specific embodiment of this system is described below.

[0622] The server first acquires trend data from external sources. This is done through internet information platforms, such as search engine trend data and social media analysis. The acquired data is stored in a database for later analysis. The server uses this data to perform analysis using generative AI models. Specifically, machine learning frameworks such as TensorFlow and PyTorch are used to generate demand forecasting metrics from the data. These metrics relate to sales and demand forecasts and can be used in product sales strategies.

[0623] When a user logs in, the device retrieves their past purchase history from the server. This allows for analysis of the user's preferences and past purchasing trends. Based on this data, the device uses algorithms such as collaborative filtering to select and present recommended items to the user. These recommendations are delivered through dynamically generated web pages and application screens, and are updated in real time using JavaScript and other technologies.

[0624] Users can select items of interest from the recommended products presented and view detailed information. If they like the product, they can add it to their cart and proceed with ordering. The terminal sends the cart information to the server, where inventory checks and purchase procedures are performed. The entire process, including payment, can be carried out smoothly through an online payment system, using the Stripe API or PayPal API.

[0625] For example, if a user wants to buy new sandals that reflect this summer's trends, the server will provide appropriate recommendations based on the latest trend data. A possible prompt would be, "Tell me about this summer's trendy sandals," to which the server would provide a list of recommended products. In response to this prompt, the server presents the best options based on the user's past purchase history and real-time trend data. This allows users to make data-driven purchases and choose the right product.

[0626] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0627] Step 1:

[0628] The server acquires trend data from external sources. Specifically, it uses APIs to collect trend data and population movement data from internet platforms. Inputs include search trend queries and social media posts. After collection, this data is stored in a database on the server, making it available for use in subsequent analysis steps.

[0629] Step 2:

[0630] The server performs analysis using a generative AI model based on acquired trend data. The input data is preprocessed using machine learning frameworks such as TensorFlow and PyTorch, and then demand forecasting indicators are created. This is done through time series analysis and statistical regression, and the output is a sales forecast score for each item. This score indicates the demand for a product and plays an important role in predicting increases or decreases in sales.

[0631] Step 3:

[0632] When a user logs in, the terminal retrieves their past purchase history from the server. The user ID is used as input, and past purchase data is retrieved. This data includes purchased items, purchase dates, prices, etc., and is analyzed on the terminal using a collaborative filtering algorithm. The output is a list of recommended items that reflect the user's preferences.

[0633] Step 4:

[0634] The terminal displays recommended items selected based on the analysis results to the user. Detailed information about the recommended products is provided through the user interface. This operation is performed by updating the displayed content in real time in conjunction with JavaScript. The input uses the ID and details of the recommended product, and the output is displayed as the product name, price, and stock information.

[0635] Step 5:

[0636] Users add items they are interested in to their cart via their device and proceed with the purchase. Specific actions include clicking the purchase button and adding items to the cart. The input is a list of selected items, and the device sends this information to the server. The output is presented as a purchase confirmation screen.

[0637] Step 6:

[0638] The server performs inventory checks and order processing based on the cart information received from the terminal. The input is the cart details; the server checks inventory against the database and confirms the purchase if the item is in stock. The output includes an order confirmation message and payment information. This process integrates with an online payment API to ensure secure payment completion.

[0639] (Application Example 1)

[0640] 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".

[0641] In online marketplaces, accurately understanding consumer purchasing trends and recommending appropriate products in a timely manner is crucial. However, traditional systems often fail to adequately provide customized offers based on individual user preferences or real-time inventory checks, making it difficult to improve the user experience and optimize sales opportunities.

[0642] 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.

[0643] In this invention, the server includes means for collecting and storing online trend data, means for analyzing the collected online trend data and generating sales forecast scores associated with products, and means for obtaining the user's past order history and selecting recommended products based on that history. This makes it possible to provide users with optimized product recommendations and present customized offers and discounts.

[0644] "Internet trend data" refers to information that shows users' activities and interests online, and is used to analyze trends in products and services.

[0645] "Means of collection" refers to a function or device for acquiring necessary data from external sources and incorporating it into a system.

[0646] "Means of analysis" refer to algorithms and systems used to process acquired data and derive information based on a specific purpose.

[0647] The "sales forecast score" is a numerical value used to predict future product sales figures, and is an index generated by a computer based on collected data.

[0648] "User's past order history" refers to a record of a user's past purchasing behavior and is used to understand that user's purchasing trends.

[0649] "Means for selecting recommended products" refers to a function or process for selecting products suitable for a user based on analysis results and user history.

[0650] A "customized offer" is a special proposal, such as a discount or perk, that is provided taking into account the needs and preferences of a particular user.

[0651] "Means of checking inventory information in real time" refers to a system or function for instantly obtaining and checking the current inventory status.

[0652] "Means of displaying to the user" refers to an interface or device for visually providing the user with analyzed or selected information.

[0653] "Means for receiving order instructions" refers to a function that obtains purchase requests from users and processes them within the system.

[0654] This system is an integrated platform consisting of servers, terminals, and users. The servers first collect online trend data using online APIs and web scraping techniques. This data includes information such as search trends and movement patterns. After collection, the servers store this information in a database and perform analysis using artificial intelligence models (for example, models using TensorFlow) to generate sales forecast scores for each product.

[0655] When a user logs in, the terminal retrieves the user's past order history from the server. Based on this history, the terminal understands the user's preferences and purchasing trends and generates personalized product recommendations. At the same time, it performs real-time inventory checks and presents the user with customized offers and discount information.

[0656] Users can view recommended products and offers displayed on their device and select items that interest them. Selected items are added to the cart, and when the user wishes to purchase them, they make a payment through a payment system (for example, a system using the Stripe API).

[0657] For example, using this system, popular sweaters and boots are recommended during the winter season, and special discounts are offered to users with a past purchase history. In this way, data-driven product recommendations and sales promotions are effectively implemented.

[0658] An example of a prompt to input into the generating AI model is: "Collect the necessary data to display a list of trending products for this winter and generate customized recommendations based on past purchase history."

[0659] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0660] Step 1:

[0661] The server collects internet trend data using online APIs and web scraping techniques. The input consists of search trend and movement pattern data obtained from various online sources. This data is formatted and filtered, processed into a database format, and then stored in the database.

[0662] Step 2:

[0663] The server runs a generative AI model using internet trend data stored in the database. The input is the trend data stored in the previous step. The AI ​​model analyzes this data and outputs a sales forecast score for each product. This model uses TensorFlow to predict sales trends.

[0664] Step 3:

[0665] When a user logs into their terminal, the terminal retrieves their past order history from the server. The input is the user ID, and the output is the user's past purchase history data. Based on this history data, the system analyzes the user's preferences and trends.

[0666] Step 4:

[0667] The terminal references the acquired purchase history and the server's sales forecast score to generate a personalized list of recommended products for the user. The input is past purchase history and sales forecast score, and the output is a list of recommended products. This process uses history matching and filtering algorithms.

[0668] Step 5:

[0669] The server accesses the inventory database in real time and checks it on the terminal. The input is a list of recommended products, and the output is information on whether or not they are in stock. The server verifies the inventory status and selects only the products that can be provided to the user.

[0670] Step 6:

[0671] Users view recommended products and customized offers on their device and add items of interest to their cart. Here, user selections are input, and the updated cart information is output. Based on user selections, the device displays high-priority products.

[0672] Step 7:

[0673] The terminal initiates the payment process, and the server executes the payment using the payment provider's API. Inputs are the selected items in the shopping cart and payment information, while output is the payment confirmation status. Once payment is complete, the order is finalized.

[0674] 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.

[0675] The order placement and receiving system according to the present invention achieves even more accurate product recommendations by incorporating an emotion engine that recognizes user emotions, in addition to analysis based on internet trend data and human flow data. The server acquires internet trend data and human flow data from external sources and stores them in a database. Using this information, the server analyzes this data through an artificial intelligence model and generates sales forecast scores for products.

[0676] Furthermore, the terminal utilizes an emotion engine to acquire user emotion data when the user registers. The emotion engine recognizes emotions from the user's facial expressions and voice input and stores them in a database. The server integrates past order history and emotion data to select recommended products.

[0677] For users, this system provides a mechanism that automatically displays recommended products based on past purchase history and current emotional state upon login. More specifically, the device dynamically adjusts the product list according to the user's emotions, providing a more personalized experience.

[0678] As a concrete example, consider a situation where a user is searching for sale items. When the emotion engine recognizes excitement or joy while the user is using their device, the server adjusts the recommendation list based on this, highlighting sale items in a more prominent way than usual. This makes it easier for the user to instantly find items that match their mood.

[0679] This system can improve marketing effectiveness by simultaneously understanding user needs and current emotional states, and optimizing product recommendations in real time.

[0680] The following describes the processing flow.

[0681] Step 1:

[0682] The server collects internet trend data and human traffic data from external sources and stores this data in a database. This includes search trends and purchasing patterns based on location information.

[0683] Step 2:

[0684] When a user logs in, the device uses the camera and microphone built into the user's device to collect emotional data. This allows the system to read the user's emotions from their facial expressions and tone of voice.

[0685] Step 3:

[0686] The server integrates acquired sentiment data with past order history and updates the user profile. Based on this data, an artificial intelligence model is used to select personalized product recommendations for each user.

[0687] Step 4:

[0688] The terminal displays a list of recommended products sent from the server to the user. This list is dynamically adjusted based on sales forecast scores and sentiment analysis results.

[0689] Step 5:

[0690] Users can view a list of recommendations and see details of products that interest them. They can then add desired items to their cart and proceed with the purchase.

[0691] Step 6:

[0692] The terminal sends the cart information to the server and initiates inventory checks and payment processing.

[0693] Step 7:

[0694] The server checks for stock availability and confirms the purchase. It then sends the user an email containing order confirmation and delivery date.

[0695] Step 8:

[0696] The device collects feedback on user purchasing behavior and reports it to the server to improve future recommendation quality.

[0697] This process allows users to receive product recommendations tailored to their emotional state, resulting in a more satisfying shopping experience.

[0698] (Example 2)

[0699] 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".

[0700] Traditional order management systems select recommended products based solely on past user purchasing history, resulting in insufficient personalization that takes into account real-time sentiment and trend information. Therefore, more accurate product recommendations are needed to improve the user experience and enhance marketing effectiveness.

[0701] 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.

[0702] In this invention, the server includes means for collecting and storing internet trend information and human flow information from electronic devices, means for analyzing the collected internet trend information and human flow information and generating predictive scores associated with products, and means for acquiring emotional information using an emotional recognition device when a user registers. This enables more personalized product recommendations based on the user's emotional information and trend information.

[0703] "Internet trend information" refers to data that shows consumer behavior, trends, and popular topics on the internet.

[0704] "Human flow information" refers to data that shows the movement patterns and distribution of people in a particular region or location.

[0705] An "emotion recognition device" is a device or software that analyzes the user's facial expressions and voice to identify their emotional state.

[0706] A "storage device" is hardware or software used to store collected information for a long period of time.

[0707] A "generative artificial intelligence model" is an algorithm that automatically generates new insights and predictions based on a wide variety of collected data.

[0708] A "predictive score" is an indicator that quantifies the future sales and demand for a product or service, generated based on analyzed data.

[0709] "Electronic devices" is a general term referring to computers and smart devices used for data collection and processing.

[0710] A "user" refers to an individual or legal entity that uses the system to place or receive orders for goods or services.

[0711] This invention is an order placement and receiving system that provides users with appropriate product recommendations by acquiring and analyzing multiple data points via a network. The main functions of the system are implemented by a server and terminals.

[0712] The server collects internet trend and human flow information from external data providers. This data is stored in a database and later serves as foundational material for analysis. RESTful APIs and cloud data services are often used for this data collection.

[0713] Next, the server analyzes the information in the database using a generative artificial intelligence model. The AI ​​model generates sales forecast scores related to products from the collected data. For example, prompts such as "predict next month's consumer trends and use them to boost sales" are input to the AI ​​model. In this process, machine learning libraries and data analysis software using Python are commonly used.

[0714] On the other hand, the terminal uses an emotion recognition device when the user accesses the system. User emotion information is obtained by analyzing facial expressions and voice input, and this data is recorded in a database. Facial expression analysis software and voice recognition software are utilized in this process.

[0715] For example, when a user shows interest in a particular event, an emotion recognition device detects that excitement, and the server immediately updates its recommendations for related products. This dynamic recommendation system allows users to efficiently find products that match their current emotions and needs.

[0716] This system aims to improve marketing effectiveness by integrating past purchase history with real-time sentiment data to provide users with more personalized product recommendations.

[0717] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0718] Step 1:

[0719] The server collects online trend information and pedestrian flow information from external data sources. This information is obtained via an API and sent to the server in JSON format. The server receives this information and stores it in a database. Specifically, upon receiving the data, the server removes duplicate data, extracts the necessary fields, and accurately stores them in the database.

[0720] Step 2:

[0721] The server performs data analysis using a generative AI model based on collected data stored in the database. The inputs used are online trend information and pedestrian flow information. The AI ​​model processes this data and outputs a sales forecast score related to the product. Specifically, it predicts future sales trends by comparing them with past trend data. This model is implemented using machine learning libraries such as TensorFlow.

[0722] Step 3:

[0723] The terminal activates the emotion recognition device when the user logs into the system. It receives input from the user's facial expressions and voice, analyzes it, and generates emotion data. The emotion recognition device analyzes how excited or happy the user is and stores this information in a database. The input uses the user's facial recognition data and voice data, and the output is data indicating the user's current emotional state.

[0724] Step 4:

[0725] The server integrates user emotional data and past purchase history, and analyzes it through a generative AI model. This integrated analysis generates data to recommend the most appropriate products to the user. Through the AI ​​model, the server interprets how the user's current emotional state influences their purchasing intent and determines the priority of recommended products.

[0726] Step 5:

[0727] The terminal displays a list of recommended products on the user's screen based on instructions from the server. The arrangement and visual effects of the product list are dynamically changed according to the user's emotional state. For example, if the user is expressing joy, discounted products are highlighted to create a visual impact. In this way, an environment is provided where users can easily find products that interest them.

[0728] (Application Example 2)

[0729] 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".

[0730] Existing order management systems have the challenge of not being able to reflect users' current emotions and interests, as product recommendations are based on users' past purchase history and online behavior data. This results in a failure to maximize user purchasing intent, leading to limited marketing effectiveness.

[0731] 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.

[0732] In this invention, the server includes means for collecting and storing internet trend data, means for analyzing the collected internet trend data and generating sales forecast scores associated with products, and means for recognizing the user's emotions and adjusting product recommendations based on emotion data. This makes it possible to provide personalized product recommendations in real time that are tailored to the user's emotional state.

[0733] "Internet trend data" refers to data that shows user behavior, interests, and trends on the internet.

[0734] A "sales forecast score" is an indicator generated to predict a product's future sales and is used in selecting recommended products.

[0735] "Past order history" refers to data that records the user's past purchase history of products.

[0736] "Recommended products" are products selected by the system based on the user's purchase history and emotional state.

[0737] "Emotional data" refers to information that indicates a user's emotional state, obtained by analyzing the user's facial expressions, voice, and other data.

[0738] An "artificial intelligence model" is a general term for algorithms and frameworks that automatically perform analysis and predictions based on diverse data.

[0739] In embodiments of the present invention, a server collects internet trend data and pedestrian flow data from external sources and stores this data in a database. The collected data is analyzed using software such as Python, pandas, and scikit-learn. This generates a sales forecast score for products. An artificial intelligence model is used for this, and it is integrated with the user's past order history.

[0740] Furthermore, the device incorporates an emotion recognition engine that captures and analyzes the user's facial expressions and voice in real time through the camera and microphone. This emotion data is also stored in a database. When a user logs in, the device utilizes this emotion data to dynamically adjust and display recommended products based on their state.

[0741] In an example of smart glasses use, the server displays a list of appropriate products based on data obtained from the user's facial expressions and voice when they view products. This makes it easy for the user to find products that match their current emotions.

[0742] For example, if a user is wearing smart glasses when going out on a rainy day, the system will prioritize displaying related products such as umbrellas and raincoats. This provides a comfortable shopping environment where users can find products that suit their mood that day.

[0743] Examples of prompt statements are as follows:

[0744] "If we sense 'undergone disappointment' rather than 'neutral' in the user's expression, we will add items from the appropriate product category to the recommendation list."

[0745] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0746] Step 1:

[0747] The server collects internet trend data and pedestrian flow data from external sources. Inputs are external APIs and data feeds, and output is raw data stored in a database. This data serves as foundational information for subsequent analysis.

[0748] Step 2:

[0749] The server analyzes the collected data and generates a sales forecast score for the products. The input is internet trend data and pedestrian flow data stored in a database, and the output is the forecast score. This process is performed using Python and scikit-learn, applying a machine learning model.

[0750] Step 3:

[0751] The device acquires the user's facial expressions and voice in real time and analyzes them with an emotion recognition engine. The input is raw data acquired by the device's camera and microphone, and the output is the user's emotion data. Libraries such as OpenCV and TensorFlow are used for this analysis.

[0752] Step 4:

[0753] The server integrates past order history and sentiment data to select recommended products. The input is order history and sentiment data stored in a database, and the output is a personalized list of recommended products. An artificial intelligence model is used in this process.

[0754] Step 5:

[0755] The terminal displays selected recommended products to the user. The input is a list of recommended products, and the output is product information displayed on the user's smart glasses screen. The user interface makes visual adjustments that match the user's current mood.

[0756] Step 6:

[0757] When a user views a product and reacts to its content, the device captures the change in emotion again using its emotion recognition engine and sends feedback to the server. The input is the newly acquired emotion data, and the output is a readjustment of the product list for the next user. This cycle results in a better purchasing experience that matches the user's emotions.

[0758] 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.

[0759] 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 those described above. 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 shown 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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."

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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 to be incorporated by reference.

[0779] The following is further disclosed regarding the embodiments described above.

[0780] (Claim 1)

[0781] A means for collecting and storing internet trend data,

[0782] A means of analyzing collected online trend data and generating sales forecast scores associated with products,

[0783] A means for obtaining a user's past order history and selecting recommended products based on that history,

[0784] A means of displaying recommended products selected by the user,

[0785] A means for receiving order instructions from users, checking inventory, and processing orders,

[0786] A system that includes this.

[0787] (Claim 2)

[0788] The system according to claim 1, which provides a means for predicting product sales by combining collected internet trend data and pedestrian flow data.

[0789] (Claim 3)

[0790] The system according to claim 1, which utilizes an artificial intelligence model for data analysis.

[0791] "Example 1"

[0792] (Claim 1)

[0793] Means for acquiring and storing trend data from external information sources,

[0794] A means of analyzing acquired trend data and creating demand forecast indicators associated with goods,

[0795] A means of obtaining the end user's past purchase history and selecting recommended items based on that history,

[0796] A means of presenting selected recommended items to end users,

[0797] A means of receiving purchase instructions from end users, confirming stockpiles, and carrying out purchasing procedures,

[0798] A system that includes this.

[0799] (Claim 2)

[0800] A system according to claim 1, which provides means for forecasting demand for goods by integrating trend data and population movement data.

[0801] (Claim 3)

[0802] The system according to claim 1, which uses a generative intelligence model for data analysis.

[0803] "Application Example 1"

[0804] (Claim 1)

[0805] A means for collecting and storing internet trend data,

[0806] A means of analyzing collected online trend data and generating sales forecast scores associated with products,

[0807] A means for obtaining a user's past order history and selecting recommended products based on that history,

[0808] A means of presenting users with customized offers and discounts,

[0809] A way to check inventory information in real time,

[0810] A means of displaying recommended products selected by the user,

[0811] A means for receiving order instructions from users, checking inventory, and processing orders,

[0812] A system that includes this.

[0813] (Claim 2)

[0814] The system according to claim 1, which provides a means for predicting product sales by combining collected internet trend data and pedestrian flow data.

[0815] (Claim 3)

[0816] The system according to claim 1, which utilizes an artificial intelligence model for data analysis.

[0817] "Example 2 of combining an emotion engine"

[0818] (Claim 1)

[0819] A means for collecting and storing internet trend information and human flow information from electronic devices,

[0820] A means for analyzing collected online trend information and human flow information to generate predictive scores associated with products,

[0821] A means of acquiring emotional information using an emotion recognition device when a user registers,

[0822] A means of storing acquired emotional information in a memory device,

[0823] A method for selecting recommended products based on the integration of the user's past purchase history and emotional information,

[0824] A means of displaying recommended products selected by the user,

[0825] A means for receiving purchase instructions from users, checking inventory, and processing orders,

[0826] A system that includes this.

[0827] (Claim 2)

[0828] The system according to claim 1, which provides means for generating a product prediction score by combining collected online trend information and pedestrian flow information.

[0829] (Claim 3)

[0830] The system according to claim 1, which utilizes a generative artificial intelligence model for information analysis.

[0831] "Application example 2 when combining with an emotional engine"

[0832] (Claim 1)

[0833] A means for collecting and storing internet trend data,

[0834] A means of analyzing collected online trend data and generating sales forecast scores associated with products,

[0835] A means for obtaining a user's past order history and selecting recommended products based on that history,

[0836] A means of displaying recommended products selected by the user,

[0837] A means for receiving order instructions from users, checking inventory, and processing orders,

[0838] A means of recognizing user emotions and adjusting product recommendations based on emotion data,

[0839] A system that includes this.

[0840] (Claim 2)

[0841] The system according to claim 1, which provides a means for predicting product sales by combining collected internet trend data, human flow data, and sentiment data.

[0842] (Claim 3)

[0843] The system according to claim 1, which uses an artificial intelligence model for data analysis and processes input data from an emotion recognition engine. [Explanation of Symbols]

[0844] 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 for collecting and storing internet trend data, A means of analyzing collected online trend data and generating sales forecast scores associated with products, A means for obtaining a user's past order history and selecting recommended products based on that history, A means of displaying recommended products selected by the user, A means for receiving order instructions from users, checking inventory, and processing orders, A system that includes this.

2. The system according to claim 1, which includes means for predicting product sales by combining collected internet trend data and pedestrian flow data.

3. The system according to claim 1, which includes means for using an artificial intelligence model for data analysis.

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