面向行业场景的短信模板智能匹配与效果预测优化方法
By combining machine learning inference engines and community detection algorithms with scenario temporal analysis and knowledge graphs, the contradiction between template and scenario dynamic adaptation in SMS service platforms is resolved, enabling dynamic optimization of SMS templates, improving response rate and resource utilization efficiency, and solving the problem of template strategies being out of touch with user needs in existing technologies.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- 深圳市智信科技有限公司
- Filing Date
- 2025-12-29
- Publication Date
- 2026-07-17
AI Technical Summary
Existing SMS service platforms have a contradiction between the dynamic evolution of industry scenarios and the static adaptation mechanism of SMS templates. This results in data collection and preprocessing failing to design adaptive strategies for the dynamic nature of scenarios, and the effect prediction model failing to deeply integrate the coupling relationship between scenario time-series data and user behavior data. Optimization strategies rely on manual experience mapping and cannot achieve intelligent linkage between template strategies and real-time scenario features. This leads to a disconnect between SMS content and user needs, a decrease in response rate, and a waste of marketing resources.
Adaptive data collection is achieved using a machine learning inference engine. Industry scenario features and user response decay features are extracted using community detection algorithms. Combined with scenario time-series evolution analysis and effect stage modeling, an industry knowledge graph is constructed to predict potential effect risks. Optimization strategies are generated through semantic association matching to achieve dynamic optimization of SMS templates.
It achieves dynamic semantic alignment between SMS templates and real-time scenarios, improves the timeliness and accuracy of scenario feature capture, quantifies the intensity of scenario fluctuations and user fatigue, identifies potential risks, generates personalized optimization strategies, significantly improves click-through rate and conversion rate, reduces ineffective strategy investment, and optimizes marketing resource allocation.
Smart Images
Figure CN121921061B_ABST