US20260220678
2026-07-30
Physics
G06Q30/0619
The AI agent-driven online shopping support system is designed to autonomously manage online price tags across various digital environments. It leverages a digital territory scanner to discover content-rich areas such as web pages, social media, and augmented reality spaces. The system utilizes a context analysis engine to interpret content through multiple modalities like text, images, and videos, creating a comprehensive semantic context. This system facilitates seamless integration by obtaining necessary permissions via an authorization module, ensuring that the online price tags are appropriately placed within the digital territories.
Key components include a runtime node injection engine that places online price tags using methods like DOM injection and AR spatial injection. An optimization engine calculates the best product-seller-price configurations by assessing various commerce performance metrics. The system also features a hybrid revenue model, which combines impression-based and performance-based components, with adjustments for AI operational costs. A closed-loop feedback module continuously refines the system's accuracy by updating model parameters based on transaction outcomes.
Addressing limitations of prior systems, this invention introduces several advancements. It automates the identification and insertion of product tags, overcoming the manual processes of earlier methods. The system enhances product-to-content matching by utilizing multimodal semantic context analysis, thus improving relevance and engagement. Additionally, the optimization strategy considers the interests of exhibitors, sellers, and buyers, unlike previous models focused on single objectives. The dynamic nature of the node injection adapts to non-web environments, providing flexibility in diverse digital contexts.
The system's revenue model is innovative, integrating both impression-based and performance-based components, which are adjusted for AI processing costs. This approach ensures fair compensation for exhibitors while maintaining operational efficiency. The feedback mechanism, powered by reinforcement learning, allows the system to adapt and improve its transaction matching accuracy over time by analyzing buyer satisfaction and transaction outcomes, leading to better decision-making in future deployments.
Designed for exclusive use in online environments, the system operates without the need for physical proximity between buyers and commerce displays. This structural distinction sets it apart from traditional commerce systems that rely on physical installations. The flexibility of the system allows it to function across various digital territories, making it a versatile tool for modern online commerce. With its advanced AI capabilities, it provides a seamless and efficient shopping experience for users in diverse digital settings.