User Intent Prediction for Network Resource Allocation
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Solution Overview
Problem
Current communication systems struggle with efficient resource allocation and network deployment due to the dynamic and time-varying nature of user intent, leading to suboptimal communication efficiency.
Innovation Solution
A communication control method based on user intent prediction, which involves determining user intent data from historical content and service requests, fusing this data with historical service data, and using a deep learning network to generate intent prediction information for advance network architecture deployment and resource configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If advance deployment of network architecture and advance configuration of network resource are attempted in a highly dynamic time-varying network environment, then communication efficiency can be improved, but the current NLP-based user intent recognition cannot complete this task accurately in time
Solution Approach 1:
The patent performs user intent prediction in advance using deep learning models before the user actually requests content or services. By predicting future user intent based on historical behavior patterns, the system proactively completes network architecture deployment and resource configuration beforehand, eliminating the time delay between intent recognition and network response.
Solution Approach 2:
The patent replaces traditional NLP-based semantic analysis with a deep learning-based intent prediction system. The deep learning model processes historical user behavior data to automatically predict future intent, substituting the manual, rule-based NLP approach with an automated, data-driven system that operates faster and more accurately in dynamic environments.
2Measurement precision
If traditional NLP model is used for user intent recognition, then semantic understanding can be achieved to some extent, but accurate and timely intent prediction cannot be achieved in highly dynamic network environments
Solution Approach 1:
The patent fundamentally changes the parameters used for intent recognition. Instead of relying on semantic analysis of user input text (traditional NLP approach), the system uses deep learning models that analyze historical user behavior data, request patterns, and contextual information. This parameter change from semantic text analysis to behavioral pattern recognition enables accurate intent prediction in dynamic environments.
Solution Approach 2:
The patent implements a dynamic intent prediction system that continuously learns from historical user behavior data and adapts to changing user preferences and network conditions. The deep learning model is trained on historical data and can dynamically adjust its predictions based on evolving user patterns, making the system adaptable to highly dynamic network environments unlike static NLP models.
Data Source
AI summary
Semantic analysis is performed on semantic data of content and/or a service that are/is historically requested by a user, to obtain intent data corresponding to the content/service historically requested by the user. After the intent data and the content and/or the service that are/is historically requested by the user (namely, historical data of the content and/or the service that are/is requested by the user) are fused, fused data is analyzed by using a deep learning algorithm, to obtain intent prediction information of the user. Advance deployment of a network architecture and advance configuration of a network resource are performed based on the intent prediction information of the user, so that the network resource is allocated correctly, properly, and in time, and proper and effective deployment of the network architecture is ensured, thereby improving communication efficiency.