Cross-Platform Personalized Content Serving with Unified Story Pool
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Solution Overview
Problem
Existing online retail platforms face challenges in dynamically presenting content across multiple channels without duplication, workload, and interaction tracking, and higher workload, and interaction difficulties in tracking guest interactions, and higher workload for creative teams.
Innovation Solution
The patent applies to the field of environmental pollution control and purification technology, specifically involving the simultaneous removal of Hg0 from flue gas and Hg2+ from waste liquid, using metal sulfides as adsorbents to adsorb and convert Hg0 from flue gas and Hg2+ from waste liquid into stable mercury sulfide compounds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If content is manually configured and adapted for each platform channel, then content presentation can be customized for specific channel requirements, but workload for creative teams increases and content setup becomes complex
Solution Approach 1:
The patent implements a universal content pool where stories are created once and can be served across multiple platforms (web, mobile web, native apps) without manual duplication. The system automatically adapts the same content to different channels, eliminating the need for separate content configuration for each platform while maintaining platform-specific optimization.
Solution Approach 2:
Instead of manually creating and configuring content for each platform, the system uses automated copying and serving of stories from a centralized pool to multiple platforms. The content is replicated and delivered through API calls to different channels, reducing manual workload while maintaining content consistency across platforms.
2Adaptability or versatility
If separate content setup is performed for each platform, then platform-specific requirements can be met, but tracking user interactions becomes difficult and content duplication occurs
Solution Approach 1:
The patent merges content management and interaction tracking into a unified system. A single content pool serves all platforms, and user interactions (clicks, views) are tracked centrally through standardized API calls. This unified approach maintains platform-specific content delivery while enabling consistent and accurate tracking of user interactions across all channels.
3Productivity
If manual content configuration is used for each platform, then content can be optimized for specific channels, but computational resources and time are consumed
Solution Approach 1:
The system performs preliminary actions by pre-processing and standardizing content into a universal format that can be automatically served to multiple platforms. Content is prepared in advance with standardized metadata and structures, enabling rapid deployment across web, mobile web, and native app channels without time-consuming manual configuration for each platform.
4Ease of manufacture
If content is curated and associated with specific pages, then relevant promotional information can be presented, but content may be presented to users who do not express interest in that content
Solution Approach 1:
The patent implements dynamic content serving that adapts based on real-time user behavior and inferred interests. Instead of static page-based content association, the system dynamically selects and serves stories from the content pool based on user interactions, device type, location, and other contextual factors, ensuring content relevance while maintaining simple content curation processes.
Data Source
AI summary
The disclosed systems and methods provide a system for dynamically serving personalized content for presentation across computing devices of different guests in an online retail environment. A server system can: retrieve, from a data repository, a subset of predefined content stories having a predetermined activation status and guest purchase data for at least a portion of guests in the online retail environment, the guest purchase data indicating products each guest purchased, determine product embeddings for each story in the subset, each embedding indicating a product linked to the story, determine, based on applying a guest story similarity (GSS) model to the guest purchase data for each guest, a similarity score between the guest purchase data and the embeddings that indicates a likelihood the guest would purchase products embedded in the story, and serve a content story having a similarity score that satisfies one or more guest personalization criteria.


