Insight-Assisted Introductions for Faster Collaborator Matching

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Solution Overview

Problem

Large organizations face inefficiencies due to duplication of work and difficulty in identifying appropriate talent for collaborations, hindering effective collaboration efforts.

Innovation Solution

Leveraging captured user metadata and graph techniques to identify organization personnel best suited for collaborations through k-partite metadata graphs, which analyze user profiles and relationships to suggest potential collaborators.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If traditional manual methods are used to identify collaborators, then employees can find partners through personal networks, but the process is time-consuming and results in duplication of work across the organization

Engineering Contradiction:

Improvecollaboration efficiencyVSAvoidtime to identify collaborators

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent replaces manual, mechanical methods of collaborator identification (personal networks, word-of-mouth) with an automated computational system that uses graph processing and machine learning algorithms to analyze organizational data and suggest collaborators, thereby reducing time loss while maintaining productivity

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system introduces an intermediary platform (the collaborator identification system) that mediates between employees seeking collaborators and the organizational database, using graph processing technology to efficiently match users with suitable partners based on multiple criteria without requiring direct manual searching

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If the organization maintains detailed user metadata and relationship graphs, then accurate collaborator identification is possible, but the system complexity and computational requirements increase

Engineering Contradiction:

Improvecollaborator matching accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent segments the complex organizational data into structured components (user profiles, skill sets, project histories, relationship graphs) and processes them through modular graph processing steps, allowing accurate collaborator identification while managing system complexity through organized data architecture

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs preliminary actions by pre-processing and structuring organizational data into graph formats, pre-calculating relationship metrics and user attributes, so that when collaborator identification is needed, the complex analysis has already been partially completed, reducing real-time computational requirements

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12567037B2Learning acceleration using insight-assisted introductions

Publication Date: 2026.03.03 DELL PROD LP

AI summary

A method and system for learning acceleration using insight-assisted introductions. More often than not, and still overwhelmingly unresolved due to difficulty in its identification, the duplication of work and effort, across large organizations, tend to grossly contribute to the many inefficiencies afflicting said organizations. One approach to minimizing, if not eliminating, this dilemma may be through the encouragement of collaborations. Finding the appropriate talent(s) within a large organization, however, to pursue said collaborations poses yet another hindrance. Embodiments disclosed herein, accordingly, leverage captured user metadata, as well as graph techniques, to identify organization personnel best suited for collaborations involving at least partially overlapping subject matter.