De-identified Digital Signal Analysis for Privacy-Preserving Productivity Insights
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Solution Overview
Problem
Remote work environments present challenges for managers in assessing employee health and productivity, as traditional survey methods have low response rates, provide noisy data, and fail to offer real-time insights, while also raising privacy concerns.
Innovation Solution
A computer-implemented digital worker data analysis system that collects and analyzes de-identified signals from remote workers' interactions, using machine learning models to provide actionable insights and personalized coaching, ensuring employee engagement and well-being without infringing on privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional survey methods are used to assess employee health and productivity, then employee privacy is protected, but real-time insights and data quality deteriorate due to low response rates and noisy data
Solution Approach 1:
The patent introduces digital signals as an intermediary between employee work activities and management assessment. Instead of directly surveying employees, the system collects passive digital signals (keyboard activity, application usage, meeting participation) that indirectly reflect productivity and engagement, resolving the contradiction by providing continuous data without direct employee intervention
Solution Approach 2:
The patent replaces the mechanical survey system with an automated digital signal collection and analysis system. Machine learning models process digital signals to generate productivity and engagement metrics, eliminating the need for manual survey completion while providing real-time insights
2Measurement precision
If intrusive monitoring approaches are adopted to gain real-time insights into employee work, then measurement precision improves, but employee privacy and trust deteriorate
Solution Approach 1:
The patent extracts only the necessary work-related information from digital signals while leaving out sensitive personal data. The system analyzes patterns in application usage, keyboard activity, and meeting participation to assess productivity without accessing or storing private communications, personal files, or sensitive employee information
Solution Approach 2:
The patent uses aggregated digital signal patterns as an intermediary layer between raw employee data and management insights. Individual digital signals are combined and analyzed collectively to produce team-level productivity metrics, preventing identification of individual employees while maintaining measurement precision
3Quantity of substance
If surveys are frequently distributed to gather employee feedback, then data quantity increases, but employee engagement and data quality deteriorate due to survey fatigue
Solution Approach 1:
The patent implements continuous passive collection of digital signals instead of periodic surveys. The system continuously monitors keyboard activity, application usage, and meeting participation without interruption, providing a steady stream of high-quality data that reflects actual work patterns without causing employee fatigue
Solution Approach 2:
The system allows digital signals to speak for themselves without requiring employee interpretation or response. Employees naturally generate digital signals through their work activities, and the system automatically analyzes these signals to produce insights, eliminating the need for employees to consciously participate in data collection
Data Source
AI summary
Embodiments provide a computer that includes one or more processors and instructions stored on one or more memory devices. The one or more processors executes the instructions to monitor, using one or more extractor application programming interfaces (APIs), a plurality of streams associated with user interactions with the computer, each of the plurality of streams including one or more events or activities associated with the user interactions, the user interactions being interactions with one or more different applications or platforms of the computer. The processor may extract contents and contexts from the plurality of streams using the one or more extractor APIs. The processor further determines one or more workflows associated with the user, the one or more workflows including the extracted contents and contexts, the workflows indicating the user interactions with the computer during a time series comprising a plurality of timestamps associated with times at which the user performed the interactions with the computer. The processor stores the one or more the workflows at the user computer. The processor determines one or more chunks of the one or more workflows, each chunk being a subpart of one of the one or more workflows and corresponding to a set of the plurality of timestamps associated with the subpart, each chunk including the content and contexts of the one or more workflows associated with the set of the plurality of timestamps. The processors evaluates the one or more chunks, the evaluating creating one or more inferred actions using the content and contexts of the one or more workflows associated with the set of the plurality of timestamps and automates one or more subsequent workflows associated with the user based on the one or more inferred actions.