Live Object Simulation for Accurate Content Distribution
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Solution Overview
Problem
Existing systems face challenges in estimating the size of subsets of live objects matching filtering parameters in a timely and processor-efficient manner, especially when dealing with large datasets and dynamic changes in data stores, which affects the distribution of targeted content objects.
Innovation Solution
A simulation method is employed where stored objects are updated to simulate live objects, processed using filtering parameters and rules, and a confidence metric is calculated to estimate the number of actual live objects, allowing for adjustments to filtering parameters to achieve the desired subset size.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If querying stored objects to generate estimate, then object count estimation is provided, but processing time extends to weeks and processors are taxed
Solution Approach 1:
The system performs preliminary actions by maintaining pre-computed statistics and metadata about stored objects, including counts grouped by filtering parameters. When an estimation request arrives, the system uses these pre-prepared data structures to immediately generate estimates without querying the full dataset, thus avoiding weeks of processing time while maintaining accuracy.
Solution Approach 2:
The patent creates simplified copies or representations of the object data in the form of aggregate statistics and metadata structures. These copies contain summarized information that can be quickly queried for estimation purposes without accessing the full detailed object datasets, dramatically reducing processing time while preserving estimation accuracy.
2Measurement precision
If identifying full subset of matching objects, then accurate subset size is obtained, but processor load increases and real-time tasks are delayed
Solution Approach 1:
The system extracts only the necessary information for estimation from the full object dataset by using pre-computed statistics and metadata. Instead of identifying and processing the complete subset of matching objects, the system extracts aggregate count information that has been pre-calculated and stored, thereby obtaining accurate subset size estimates without the processor load of full subset identification.
Solution Approach 2:
The patent applies partial action by computing and maintaining only the specific aggregate statistics needed for estimation purposes, rather than processing complete object subsets. The system performs sufficient (but not excessive) processing to maintain accurate count metadata, which then enables rapid estimation without full subset identification, balancing accuracy with real-time performance.
3Productivity
If using stored objects for estimation, then estimate can be generated, but object structure variability and dynamic changes reduce accuracy
Solution Approach 1:
The system implements feedback mechanisms that continuously monitor and update statistics metadata as objects are added, removed, or modified in the data store. This ongoing feedback ensures that the pre-computed statistics remain synchronized with the current state of the data, maintaining estimation accuracy despite dynamic changes and object structure variability while preserving fast estimation capability.
Data Source
AI summary
Techniques are provided to quickly process stored objects to generate an accurate estimate of the number of live objects that will be responsive to a client's filtering request defined at least in part based on a filtering parameter. Stored objects generated from user interactions with webpages are modified to simulate live objects and processed by a live object-intake simulator. The simulated results are then filtered using the filtering parameter to form a subset of matching objects. An estimate of the number of live objects produced by the filtering parameter is generated using at least the count of the objects in the subset. A confidence metric of the estimate is computed, in some cases using a statistical model. Under certain conditions, the estimate is delivered, the confidence metric is delivered, and/or live object intake based on the filtering parameter is initiated.