Plaid inc. (20240303244). PREDICTING DATA AVAILABILITY AND SCHEDULING DATA PULLS simplified abstract
Contents
PREDICTING DATA AVAILABILITY AND SCHEDULING DATA PULLS
Organization Name
Inventor(s)
Vivek Manoj Gandhi of New York City NY (US)
Jeremy Mason-herr of Santa Cruz CA (US)
Maksim Rozen of New York NY (US)
PREDICTING DATA AVAILABILITY AND SCHEDULING DATA PULLS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240303244 titled 'PREDICTING DATA AVAILABILITY AND SCHEDULING DATA PULLS
The abstract of this patent application describes a data aggregator that can predict when new information associated with a data record will be available and schedule data pulls accordingly.
- The data aggregator receives indications associated with data records and applies a model to generate predictions about the availability of new information.
- Based on these predictions, the data aggregator can refrain from requesting new information and schedule data pulls for a later time.
- The data aggregator can also receive indications of rate limits associated with hosts for multiple data records and use rules to prioritize and schedule data pulls accordingly.
Potential Applications: - This technology could be used in data aggregation systems to optimize the timing of data pulls and reduce unnecessary requests. - It could be valuable in industries where real-time data updates are crucial, such as finance or weather forecasting.
Problems Solved: - Helps prevent unnecessary data requests, reducing strain on servers and improving overall system efficiency. - Enables more accurate predictions about when new data will be available, improving data processing workflows.
Benefits: - Increased efficiency in data aggregation processes. - Improved resource management by avoiding unnecessary data requests. - Enhanced accuracy in predicting data availability.
Commercial Applications: Title: "Optimized Data Aggregation System for Efficient Resource Management" This technology could be utilized in various industries such as finance, e-commerce, and research institutions to streamline data aggregation processes and improve overall system performance.
Prior Art: While there may be similar technologies in the field of data aggregation and prediction, further research is needed to identify specific prior art related to this innovative approach.
Frequently Updated Research: Researchers in the field of data science and machine learning may be conducting studies on optimizing data aggregation processes and predicting data availability. Stay updated on relevant publications and conferences in these areas for the latest advancements.
Questions about the technology: 1. How does this technology improve resource management in data aggregation systems? 2. What are the potential implications of using predictive models to schedule data pulls in real-time applications?
Original Abstract Submitted
in some implementations, a data aggregator may receive an indication associated with a data record. the data aggregator may apply a model to the indication to generate a prediction regarding when new information associated with the data record will be available. based on the prediction, the data aggregator may refrain from requesting new information and may schedule a pull for new information associated with the data record for a later time. additionally, or alternatively, the data aggregator may receive an indication associated with a plurality of data pulls that are associated with a plurality of data records and may receive an indication of a rate limit associated with a host for the plurality of data records. the data aggregator may apply rules to generate a ranking of the plurality of data pulls and may schedule the plurality of data pulls based on the ranking and the rate limit.
