17958261. TRANSFER-LEARNING FOR STRUCTURED DATA WITH REGARD TO JOURNEYS DEFINED BY SETS OF ACTIONS simplified abstract (Microsoft Technology Licensing, LLC)
Contents
- 1 TRANSFER-LEARNING FOR STRUCTURED DATA WITH REGARD TO JOURNEYS DEFINED BY SETS OF ACTIONS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 TRANSFER-LEARNING FOR STRUCTURED DATA WITH REGARD TO JOURNEYS DEFINED BY SETS OF ACTIONS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
TRANSFER-LEARNING FOR STRUCTURED DATA WITH REGARD TO JOURNEYS DEFINED BY SETS OF ACTIONS
Organization Name
Microsoft Technology Licensing, LLC
Inventor(s)
Sharath Kumar Rangappa of Bangalore (IN)
Akash Kodibail of Bengaluru (IN)
TRANSFER-LEARNING FOR STRUCTURED DATA WITH REGARD TO JOURNEYS DEFINED BY SETS OF ACTIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 17958261 titled 'TRANSFER-LEARNING FOR STRUCTURED DATA WITH REGARD TO JOURNEYS DEFINED BY SETS OF ACTIONS
Simplified Explanation
The patent application describes techniques for transfer-learning for structured data related to journeys defined by sets of actions using deep neural networks.
- First DNN trained for a first journey using structured data.
- Weights of nodes in the first DNN transferred to nodes in a second DNN for a second journey.
- An embedding layer replaces the final layer of the first DNN in the second DNN.
- Weights of nodes in the embedding layer initialized based on the probability of co-occurrence of features.
- Softmax function applied on the final layer of the second DNN to indicate possible next actions.
Potential Applications
The technology can be applied in various fields such as recommendation systems, personalized marketing, and predictive analytics.
Problems Solved
This technology helps in improving the accuracy and efficiency of predicting next actions based on structured data related to journeys.
Benefits
The benefits of this technology include enhanced decision-making, improved user experience, and increased automation in various processes.
Potential Commercial Applications
Potential commercial applications include personalized product recommendations, targeted advertising, and optimized resource allocation in logistics.
Possible Prior Art
One possible prior art could be the use of transfer learning in neural networks for pattern recognition tasks.
Unanswered Questions
How does the embedding layer improve the transfer-learning process in the second DNN?
The embedding layer helps in capturing the relationships between features in the structured data, leading to better generalization and prediction performance.
What are the implications of using transfer-learning for structured data in real-time applications?
The real-time applications may require continuous updating of weights and embeddings based on new data, which could impact the overall performance and efficiency of the system.
Original Abstract Submitted
Techniques are described herein that are capable of performing transfer-learning for structured data with regard to journeys defined by sets of actions. A first deep neural network (DNN) for a first journey is trained using structured data. Weights of nodes in the first DNN are transferred to nodes in a second DNN for a second journey using transfer-learning. An embedding layer replaces a final layer of the first DNN in the second DNN to provide an output with a same number of nodes as a pre-final layer of the first DNN. Weights of the nodes in the embedding layer are initialized based at least on a probability that a new feature of the second journey co-occurs with each feature in the structured data. A softmax function is applied on a final layer of the second DNN to indicate possible next actions of the second journey.
