Client-Side Resource Estimation Using Nearby Sample Dots
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Solution Overview
Problem
Existing computerized systems rely on network calls to online service platforms for resource estimation, which can be inefficient and resource-intensive, especially under unfavorable network conditions, leading to latency and increased data transmission requirements.
Innovation Solution
Implementing a client-side estimation system using client-side versions of digital rules (CSVDR) and coarse values (CVF) to generate local estimates of resources, reducing the need for network calls and optimizing resource estimation processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If network calls are made to online service platforms for resource estimation, then accurate resource estimates can be obtained, but latency and data transmission requirements increase
Solution Approach 1:
The system pre-processes and stores resource estimation data locally on client devices before network connectivity is needed. By performing the resource estimation computation in advance and caching the results, the system eliminates real-time network latency while maintaining estimation accuracy through local data retrieval
Solution Approach 2:
The invention creates local copies of resource estimation data and models on client devices, replicating the essential computation capabilities from the online service platform. This copying approach allows clients to perform estimations independently without continuous network access, reducing latency while preserving accuracy through locally stored reference data
2Measurement precision
If comprehensive parameter sets are transmitted for accurate resource estimation, then estimation precision improves, but data transmission and storage requirements increase
Solution Approach 1:
The system segments the resource estimation data into essential parameters stored locally on client devices and supplementary parameters that can be retrieved on-demand from the online service platform. This segmentation reduces the initial data package size transmitted to clients while maintaining the ability to achieve accurate estimates by combining local and remote data as needed
Solution Approach 2:
The invention implements local quality by storing only the most frequently used and critical estimation parameters locally on client devices, while less frequently accessed or more detailed parameters remain on the online service platform. This approach optimizes the balance between local data package size and estimation accuracy by tailoring the local data set to the specific needs and usage patterns of each client
Data Source
AI summary
Systems and methods electronically generate sample dots, produce resources associated with the dots and estimate a resource for a target point from known resources of dots near the target point based on client side version of digital rules, cataloged data and coarse values previously received from the online service platform. A client receives cataloged data of a cataloged domain in which the cataloged data includes data representing a plurality of dots and a respective computed resource value for each of the dots and each dot of the plurality of dots represents a point in the cataloged domain. In response to confirming a target point is in the cataloged domain, the system discovers a closest one or more dots to the target point based on the cataloged data, estimates a statistic for a resource for the target point based on the respective computed resource values of the closest one or more dots, stores the estimated statistic in a memory, and produces the local estimate based on the estimated statistic.