User Hub Connection Recommendations Across Dispersed App Data
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Solution Overview
Problem
Users face challenges in identifying and connecting with relevant collaborators due to dispersed information across different applications, making efficient collaboration difficult.
Innovation Solution
A communication platform integrates various user applications through an application integration engine and a connection recommendation engine to aggregate user data, using AI/ML-based algorithms to recommend suitable collaborators based on user metadata and activity data, providing tailored connection recommendations via a user hub application.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If user information is dispersed across different applications, then information completeness is improved, but information accessibility deteriorates
Solution Approach 1:
The patent merges user information from multiple dispersed applications into a unified user profile. The system integrates data from calendar applications, contact applications, and other user applications to create a consolidated view of user information, making it easily accessible through a single interface while maintaining the completeness of information from all source applications.
Solution Approach 2:
The user profile system serves multiple functions simultaneously: it aggregates information from various applications, provides connection recommendations, displays user availability, and facilitates collaboration matching. This multi-functional approach allows the system to maintain information completeness while improving accessibility through a universal interface.
2Measurement precision
If connection recommendations are provided manually, then recommendation accuracy is improved, but time consumption deteriorates
Solution Approach 1:
The system automatically generates connection recommendations by analyzing user profile data, collaboration history, and availability information without requiring manual input from users. The recommendation engine self-servicefully processes user data, identifies potential collaborators, and presents recommendations, thereby maintaining accuracy while eliminating time consumption associated with manual recommendation processes.
Solution Approach 2:
The system incorporates feedback mechanisms where user interactions with recommendations and actual collaboration outcomes are analyzed to refine future recommendations. This feedback loop enables the system to improve recommendation accuracy automatically over time without increasing user time investment, as the system learns from past collaboration patterns and user preferences.
3Adaptability or versatility
If user data is aggregated from multiple applications, then collaboration relevance is improved, but system complexity deteriorates
Solution Approach 1:
The patent introduces a user profile as an intermediary layer between multiple applications and the recommendation engine. This intermediary consolidates and standardizes data from various applications (calendar, contacts, messaging) into a unified format, making it easier to process and analyze. The user profile acts as a mediator that simplifies the system architecture while enabling comprehensive data aggregation for relevant collaboration recommendations.
Data Source
AI summary
Example methods and systems for connection recommendation are provided. A communication platform provides a user hub application comprising one or more application modules corresponding to one or more user applications. The communication platform accesses user data associated with a plurality of users at the one or more user applications via the user hub application. The plurality of users includes a first user and a set of other users. The communication platform determines a connection recommendation for the first user based on the user data and provides the connection recommendation to the first user via the user hub application.