Plaid inc. (20240303237). PREDICTING DATA AVAILABILITY AND SCHEDULING DATA PULLS simplified abstract
Contents
- 1 PREDICTING DATA AVAILABILITY AND SCHEDULING DATA PULLS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 PREDICTING DATA AVAILABILITY AND SCHEDULING DATA PULLS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Predictive Data Aggregation Technology
- 1.13 Original Abstract Submitted
PREDICTING DATA AVAILABILITY AND SCHEDULING DATA PULLS
Organization Name
Inventor(s)
Vivek Manoj Gandhi of New York 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 20240303237 titled 'PREDICTING DATA AVAILABILITY AND SCHEDULING DATA PULLS
Simplified Explanation
In some implementations, a data aggregator can predict when new information associated with a data record will be available, allowing it to schedule data pulls more efficiently.
- The data aggregator receives indications associated with data records and applies a model to generate predictions.
- Based on these predictions, the data aggregator can schedule data pulls for a later time, avoiding unnecessary requests for new information.
- The data aggregator can also prioritize data pulls based on rate limits associated with hosts, ensuring efficient data retrieval.
Key Features and Innovation
- Prediction of when new information will be available for data records.
- Efficient scheduling of data pulls based on predictions to avoid unnecessary requests.
- Prioritization of data pulls based on rate limits associated with hosts.
Potential Applications
This technology can be applied in various industries such as finance, healthcare, and marketing where timely access to data is crucial for decision-making processes.
Problems Solved
- Inefficient data retrieval processes.
- Unnecessary requests for new information.
- Lack of prioritization in data pulls.
Benefits
- Improved efficiency in data retrieval.
- Cost savings by avoiding unnecessary requests.
- Enhanced decision-making processes through timely access to data.
Commercial Applications
- "Predictive Data Aggregation Technology for Efficient Data Retrieval in Finance and Healthcare Markets"
- This technology can be used by financial institutions and healthcare providers to streamline data retrieval processes and make informed decisions based on timely information.
Prior Art
Further research can be conducted in the field of predictive data aggregation technologies to explore existing solutions and advancements in this area.
Frequently Updated Research
Stay updated on advancements in predictive data aggregation technologies to leverage the latest innovations for efficient data retrieval processes.
Questions about Predictive Data Aggregation Technology
How does predictive data aggregation technology improve data retrieval processes?
Predictive data aggregation technology enhances data retrieval processes by predicting when new information will be available, allowing for efficient scheduling of data pulls.
What industries can benefit from predictive data aggregation technology?
Industries such as finance, healthcare, and marketing can benefit from predictive data aggregation technology by improving decision-making processes through timely access to data.
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.
