Virtual Management System for Employee Productivity Analysis
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Solution Overview
Problem
Businesses face difficulties in analyzing employee productivity due to ambiguous and disparate data sources from various devices and systems, making it challenging to aggregate and utilize this data for resource management and efficiency improvements.
Innovation Solution
A computer-implemented method and system that captures and structures employee data from multiple sources, disambiguates the data, and generates metrics to optimize resource usage by transforming unstructured data into structured formats and applying rule sets to derive a digital productivity footprint.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is captured from multiple disparate sources (badge readers, computing systems, mobile devices), then the quantity and coverage of employee activity data is improved, but the complexity of aggregating and analyzing the data increases due to different data structures and formats
Solution Approach 1:
The patent introduces a centralized data aggregation platform that serves as an intermediary between disparate data sources (badge readers, computing systems, mobile devices) and the analysis system. This platform receives data in various formats from multiple sources, standardizes it into a common structure, and makes it available for unified analysis, thereby reducing the complexity of direct integration between all data sources.
Solution Approach 2:
The system transforms data from different sources by changing its parameters and format. Data from badge readers, computing systems, and mobile devices is converted from their native formats into a standardized structure with consistent fields and data types, enabling uniform analysis across all employee activity data regardless of its origin.
2Ease of operation
If traditional single-source data analysis methods are used, then the simplicity of analysis is maintained, but the accuracy and reliability of employee productivity measurement deteriorates due to data ambiguities
Solution Approach 1:
The patent merges data from multiple sources (badge swipe data, computing system logs, mobile device data) into a unified employee activity profile. By combining these complementary data sources, the system resolves ambiguities that exist in any single source alone, thereby improving measurement accuracy while maintaining analytical simplicity through the integrated view.
Solution Approach 2:
The system uses feedback from multiple data sources to validate and correct employee activity measurements. When data from one source is ambiguous or incomplete, feedback from other sources helps resolve the uncertainty, improving the overall accuracy of productivity measurements while keeping the analysis process manageable through automated cross-validation.
3Quantity of substance
If data from multiple sources is aggregated without disambiguation, then the completeness of employee activity coverage is improved, but the reliability of the data deteriorates due to ambiguities in individual data sources
Solution Approach 1:
The centralized aggregation platform acts as an intermediary that receives complete data from all sources and applies disambiguation logic. It identifies and resolves conflicts or ambiguities in the aggregated data by cross-referencing multiple sources, thereby maintaining data completeness while improving reliability through systematic disambiguation processes.
Data Source
AI summary
A computer-implemented method and system are provided for optimizing resource usage, wherein the resources include employees of an organization. The method includes collecting employee data including structured data and unstructured data through multiple input channels over at least one network and storing the employee data collected over the multiple input channels in at least one computer memory. The method further includes accessing the computer memory using at least one computer processor and executing instructions to perform multiple operations on the stored data. The operations include transforming the unstructured data into structured data and disambiguating the structured data. The operations additionally include applying rule sets to the transformed data and the structured data to derive a digital productivity footprint for each employee and analyzing the derived digital footprints to optimize resource usage.