Deep Learning Network for Enterprise Predictive Analytics
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Solution Overview
Problem
Current CRM systems lack the ability to leverage machine learning for predictive analytics, relying on resource-intensive feature engineering to overcome data sparsity and achieve accurate predictions, which limits their effectiveness in optimizing enterprise outcomes.
Innovation Solution
A deep learning network is trained using both raw enterprise data and global data sources to automatically analyze and predict enterprise outcomes, eliminating the need for extensive feature engineering by employing transfer learning and semantic relationship mining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional CRM systems use resource-intensive feature engineering to overcome data sparsity, then prediction accuracy can be improved, but system complexity and resource consumption increase
Solution Approach 1:
The patent replaces traditional mechanical feature engineering processes with a deep learning network that automatically learns features from raw data. The neural network's distributed representation learning mechanism substitutes manual feature extraction, reducing system complexity while maintaining or improving prediction accuracy through automated pattern recognition in the data.
Solution Approach 2:
The patent changes the fundamental parameters of the analytics system by transitioning from sparse enterprise data with manual features to dense global data with automated neural network features. This parameter change in data density and feature representation enables accurate predictions without resource-intensive engineering processes.
2Measurement precision
If traditional CRM systems perform extensive feature engineering to achieve accurate predictions, then prediction quality improves, but processing time and resource consumption increase
Solution Approach 1:
The patent applies preliminary action by pre-training the deep learning network on large global datasets before deployment. This pre-training phase performs the computationally intensive feature learning in advance, so that during actual prediction operations, the network can quickly process enterprise data without requiring extensive real-time feature engineering, thus reducing processing time while maintaining prediction quality.
3Adaptability or versatility
If CRM systems integrate multiple global data sources to enhance analytics, then predictive capability improves, but data integration complexity increases
Solution Approach 1:
The patent applies universality by designing a deep learning network that can process multiple types of global data sources (social media, search logs, commercial databases) through a unified architecture. The network's distributed representation learning mechanism universally handles diverse data formats and structures, enabling enhanced predictive capability across different data sources without requiring separate integration processes for each source type.
4Measurement precision
If traditional systems rely on enterprise-specific data only, then data privacy is maintained, but predictive accuracy is limited due to data sparsity
Solution Approach 1:
The patent uses the deep learning network's distributed representation learning as an intermediary that processes global data sources and transforms them into learned features and embeddings. These intermediaries capture patterns and relationships from abundant global data without exposing raw enterprise-specific information, thereby improving predictive accuracy through enhanced data availability while maintaining data privacy through the network's learned representations.
Data Source
AI summary
A deep learning network is trained to automatically analyze enterprise data. Raw data from one or more global data sources is received, and a specific training dataset that includes data exemplary of the enterprise data is also received. The raw data from the global data sources is used to pre-train the deep learning network to predict the results of a specific enterprise outcome scenario. The specific training dataset is then used to further train the deep learning network to predict the results of a specific enterprise outcome scenario. Alternately, the raw data from the global data sources may be automatically mined to identify semantic relationships there-within, and the identified semantic relationships may be used to pre-train the deep learning network to predict the results of a specific enterprise outcome scenario.