Productivity and Collaboration Index Validation for Federated Recommendations
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Solution Overview
Problem
Existing computer-implemented systems for federated collaboration environments face inaccuracies and inefficiencies in providing recommendations to users, as they fail to accurately quantify productivity and collaboration, leading to suboptimal workplace efficiency and resource utilization.
Innovation Solution
The implementation of a productivity and collaboration index system that generates scores based on user interactions and actions across multiple applications, using digital metadata and Principal Component Analysis (PCA) to produce actionable recommendations for improving user productivity and collaboration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional recommendation models are used in federated collaboration systems, then the system can provide recommendations to users, but the accuracy and effectiveness of recommendations deteriorates due to inability to properly quantify productivity and collaboration
Solution Approach 1:
The patent transforms qualitative collaboration activities into quantitative parameters by creating productivity indexes and collaboration indexes. These indexes convert user interactions, document activities, and communication patterns into measurable numerical values that can be processed by recommendation models, thereby improving both measurement precision and recommendation effectiveness.
Solution Approach 2:
The patent introduces index calculation modules as intermediary components between raw user activities and recommendation models. These modules act as mediators that process, aggregate, and normalize collaboration data into standardized indexes, enabling accurate quantification of productivity and collaboration metrics that feed into the recommendation system.
2Measurement precision
If comprehensive user activity data is collected across multiple applications, then the recommendation accuracy can be improved, but the resource consumption including network bandwidth and CPU cycles increases
Solution Approach 1:
The patent extracts only the essential features and activities needed for productivity and collaboration measurement from the comprehensive user data. By identifying and extracting relevant actions (document creation, editing, communication patterns) while filtering out unnecessary data, the system maintains recommendation accuracy while reducing resource consumption for data collection and processing.
Solution Approach 2:
The patent segments user activities into distinct categories (productivity actions, collaboration actions, communication patterns) and processes each segment separately. This segmentation allows the system to collect comprehensive data across multiple applications while managing resource consumption through modular, targeted processing of specific activity types rather than uniform processing of all data.
Data Source
AI summary
A computer-implemented method comprises, generating, for each group of a plurality of groups of user accounts, a collaboration index value representing a first level of interaction of the each group with other groups of the plurality of groups, the interaction comprising a plurality of operations using a plurality of different computer-implemented applications or functions, generating, for each group of the plurality of groups, a productivity index value, each productivity index value representing a second level of productivity of the respective group, the productivity comprising creating or editing electronic documents using the plurality of different computer-implemented applications or functions, storing, in one or more data repositories, a plurality of first recommendation segments for a collaboration index, each of the first recommendation segments for the collaboration index indicating a range of collaboration index values, storing, in one or more data repositories, a plurality of second recommendation segments for a productivity index, each of the second recommendation segments for the productivity index indicating a range of productivity index values, assigning a first recommendation to each first recommendation segment for the collaboration index and to each second recommendation segment for the productivity index, determining a second recommendation for each group of the plurality of groups based at least on: the collaboration index value of the respective group, the productivity index value of the respective group, the plurality of first recommendation segments for the collaboration index, and the plurality of second recommendation segments for the productivity index and generating and causing displaying, at a computer associated with each group of the plurality of groups, a digital data display that indicates the second recommendation for the respective group.