Personalized Collaboration Interfaces for Ranked Work Unit Access
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Solution Overview
Problem
Existing web-based collaboration environments require substantial navigation through multiple views and pages to locate and access work unit records, leading to inefficiencies and increased resource consumption.
Innovation Solution
A system that customizes the user interface based on rankings of work unit records using machine learning models, prioritizing high-ranked records for faster access, leveraging user-level and domain-level interactions to improve navigation efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional navigation approaches are used to locate work unit records, then users can access any record in the collaboration environment, but users must navigate through multiple views and pages which increases navigation time and resource consumption
Solution Approach 1:
The system performs preliminary actions by pre-ranking work unit records based on user interactions and domain-level patterns before the user needs to access them. The machine learning model continuously updates record rankings in advance, so when a user needs to locate records, the highest-probability records are already positioned for immediate access, eliminating the need to navigate through multiple hierarchical levels.
Solution Approach 2:
The patent introduces an intermediary mechanism - a machine learning ranking system that acts as a mediator between the user's information needs and the collaboration environment's record storage structure. This intermediary translates user interaction patterns into prioritized record lists, serving as a bridge that eliminates direct navigation through complex hierarchical views while maintaining full record accessibility.
2Ease of operation
If the user interface displays all work unit records, then users have complete visibility of all work, but the interface becomes complex and requires substantial navigation to locate specific records
Solution Approach 1:
The system applies local quality by customizing the user interface to display different record prioritizations based on individual user interaction patterns and domain-level characteristics. Each user receives a personalized record ranking that highlights locally relevant work items, transforming the generic complex interface into a customized simplified view that maintains completeness while improving locateability.
Solution Approach 2:
The patent changes the parameter of record presentation from alphabetical or hierarchical ordering to machine learning-based probability ranking. This parameter transformation reorders records based on predicted user access probability, converting the interface from a static structure to a dynamic, adaptive presentation that simplifies navigation while maintaining comprehensive visibility.
3Measurement precision
If the system ranks records based on user-level interactions only, then individual user preferences are captured, but domain-level patterns and common work flows are not considered
Solution Approach 1:
The system merges two distinct ranking approaches: user-level interaction modeling and domain-level pattern analysis. The machine learning model combines individual user behavior data with collective domain patterns, merging these perspectives to generate a comprehensive ranking that captures both personal preferences and shared work workflows, thereby improving measurement precision while expanding adaptability.
Solution Approach 2:
The ranking system achieves universality by serving multiple functions simultaneously: it personalizes record presentation for individual users while also capturing domain-wide work patterns. The unified model handles both user-specific and organization-level ranking requirements, making the system adaptable to diverse ranking scenarios without requiring separate models for different purposes.
Data Source
AI summary
Systems and methods to customize a user interface of a collaboration environment based on ranking of work unit records managed by the collaboration environment are described herein. Exemplary implementations may: manage environment state information maintaining a collaboration environment; determine user-level record interaction information characterizing interactions of individual ones of the users with individual ones of the work unit records; determine domain-level record interaction information characterizing the interactions of a set of the users with the individual ones of the work unit records; generate, for the individual ones of the users, rankings of the work unit records based on the user-level record interaction information and the domain-level record interaction information; effectuate presentation of instances of a user interface of the collaboration environment customized for individual users based on the rankings; and/or perform other operations.