Cross-platform advertisement automatic putting system

By building a cross-platform automated advertising delivery system, we can achieve deep understanding of multimodal content and adaptive optimization of strategies, which solves the problems of low advertising matching accuracy and inventory waste in existing technologies, and improves the accuracy and return on investment of advertising.

CN120851968APending Publication Date: 2025-10-28SHENZHEN XINXI LIANLIAN TECH CO LTD
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Patent Information

Application Number
CN202510957258.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-11
Publication Date
2025-10-28

AI Technical Summary

Technical Problem

Existing ad delivery systems lack depth and breadth in content understanding, are unable to perform multimodal deep understanding, and lack adaptive strategy optimization, resulting in low ad matching accuracy and wasted ad inventory.

Method used

Build a cross-platform automated advertising delivery system, including modules for multimodal content collection, analysis, relevance scoring, dynamic delivery strategies, and adaptive strategy optimization. Through deep understanding of multimodal content and adaptive optimization based on actual delivery return efficiency, it realizes cross-modal theme aggregation and hierarchical delivery strategies.

Benefits of technology

It significantly improves the accuracy and return on investment of advertising, increases the efficiency of advertising inventory utilization, solves the problems of rigid strategies and inability to iterate in existing technologies, and enhances the robustness and long-term profitability of the system.

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Abstract

The invention discloses an automatic cross-platform advertisement putting system and method, and belongs to the technical field of advertisement putting, and the system comprises a multi-modal content analysis module which is used for carrying out the cross-modal theme aggregation of the graph, text and video contents of a social platform, and generating a unified content vector; the correlation scoring module is used for calculating correlation scores of the content vectors and the commodities in the advertisement library; the dynamic delivery strategy module executes a hierarchical strategy according to the score: high-time-sharing accurate delivery, middle-time-sharing associated extension delivery, and low-time-sharing exploratory delivery decision algorithm is started to perform opportunity exploration; and the strategy self-adaptive optimization module is used for automatically adjusting various parameters in the putting strategy by periodically backtesting the actual putting return efficiency of the advertisement. Through combination of deep content awareness and strategy closed-loop optimization, the accuracy and the return rate of advertisement putting and the utilization efficiency of advertisement inventory are remarkably improved.
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