International business machines corporation (20240193411). GENERATING CAUSAL ASSOCIATION RANKINGS USING DYNAMIC EMBEDDINGS simplified abstract
Contents
- 1 GENERATING CAUSAL ASSOCIATION RANKINGS USING DYNAMIC EMBEDDINGS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 GENERATING CAUSAL ASSOCIATION RANKINGS USING DYNAMIC EMBEDDINGS - A simplified explanation of the abstract
- 1.4 Potential Applications
- 1.5 Problems Solved
- 1.6 Benefits
- 1.7 Commercial Applications
- 1.8 Prior Art
- 1.9 Frequently Updated Research
- 1.10 Questions about Causal Association Rankings
- 1.11 Original Abstract Submitted
GENERATING CAUSAL ASSOCIATION RANKINGS USING DYNAMIC EMBEDDINGS
Organization Name
international business machines corporation
Inventor(s)
Jiri Navratil of Cortlandt Manor NY (US)
Karthikeyan Shanmugam of Bengaluru (IN)
Youssef Mroueh of New York NY (US)
Inkit Padhi of White Plains NY (US)
GENERATING CAUSAL ASSOCIATION RANKINGS USING DYNAMIC EMBEDDINGS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240193411 titled 'GENERATING CAUSAL ASSOCIATION RANKINGS USING DYNAMIC EMBEDDINGS
The patent application describes a method for generating causal association rankings for candidate events using dynamic deep neural network generated embeddings.
- Automatically receives a window of candidate events, including events of a first type preceding target events of interest.
- Generates contrastive windows of candidate events, each corresponding to a different dropped candidate event from the received window.
- Identifies matching historical windows of events with embeddings close in distance to the embeddings of the contrastive windows and calculates a first score for each match.
- Identifies matching incident windows and calculates a corresponding second score.
- Uses the first and second scores to generate causal association rankings.
Potential Applications
This technology could be applied in various fields such as predictive analytics, anomaly detection, and recommendation systems.
Problems Solved
This technology addresses the challenge of accurately ranking causal associations between events within a sequence.
Benefits
The technology offers a more precise and automated way to determine causal relationships between events, leading to improved decision-making and insights.
Commercial Applications
The technology could be utilized in industries such as finance, healthcare, and marketing to enhance data analysis and forecasting capabilities.
Prior Art
Researchers interested in this technology may explore prior studies on event sequence analysis and causal inference methods.
Frequently Updated Research
Stay informed about the latest advancements in deep learning models for event sequence analysis to enhance the performance of this technology.
Questions about Causal Association Rankings
How does the technology determine the causal association between events?
The technology uses dynamic deep neural network generated embeddings to calculate scores for matching historical and incident windows, which are then used to rank causal associations.
What are the potential limitations of this technology in real-world applications?
The technology may face challenges in handling large-scale event sequences and complex causal relationships, requiring further optimization and scalability.
Original Abstract Submitted
an embodiment for generating causal association rankings for candidate events within a window of candidate events using dynamic deep neural network generated embeddings. the embodiment may automatically receive a window of candidate events including events of a first type preceding one or more target events of interest. the embodiment may automatically generate contrastive windows of candidate events, each of the contrastive windows of candidate events of the first type corresponding to a different dropped candidate event from the received window of candidate events. the embodiment may automatically identify matching historical windows of events having resulting embeddings that are close in distance to the embeddings corresponding to the embeddings of the contrastive windows and calculate a first score for each match. the embodiment may automatically identify matching incident windows and calculate a corresponding second score. the embodiment may use the first and second scores to generate casual association rankings.
