AI-Powered Collateral Aggregation for Project Workspaces

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Solution Overview

Problem

Current collaboration platforms require manual effort to identify and associate project collateral with workspaces, leading to inefficiencies and potential overlooking of relevant content, especially in large enterprises where similar content may already exist.

Innovation Solution

An AI-powered system that analyzes natural language descriptions of projects to automatically identify keywords, generates search queries, and presents relevant project-related collateral items, allowing users to easily select and associate them with workspaces without needing to formulate search queries manually.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual search and identification methods are used to locate project collateral, then users can find relevant content, but the process becomes time-consuming and users may overlook useful resources

Engineering Contradiction:

Improveaccuracy of locating relevant collateralVSAvoidtime to locate and add collateral

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system automatically performs the collateral identification and aggregation tasks without requiring manual user intervention. The AI-powered search engine autonomously analyzes project descriptions, searches datastores, and presents relevant collateral items, allowing the system to serve itself rather than requiring users to manually search and organize collateral.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical search processes with an AI-powered automated search engine that uses natural language processing and machine learning algorithms. This substitution transforms the manual, time-consuming process of searching through datastores into an automated intelligent system that quickly identifies and presents relevant collateral.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Ease of operation

If a simple text input interface is used for project descriptions, then the user interface is simplified, but the system must automatically extract meaningful search keywords from unstructured text

Engineering Contradiction:

Improvesimplicity of user interfaceVSAvoidcomplexity of keyword extraction process

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The patent introduces an intermediary layer consisting of NLP models and keyword extraction algorithms that bridge the simple user input and the complex search requirements. This intermediary automatically processes the unstructured text input, extracts meaningful keywords and concepts, and transforms them into effective search queries, shielding users from the underlying complexity.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system replaces manual keyword formulation with automated natural language processing techniques. Instead of requiring users to manually identify and input search keywords, the system uses AI models to automatically analyze the project description text, extract relevant keywords and concepts, and generate search queries, thereby simplifying the user interface while handling the complexity internally.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Productivity

If AI-powered automatic identification is implemented, then collateral aggregation efficiency is improved, but the system complexity and computational resources increase

Engineering Contradiction:

Improvecollateral aggregation efficiencyVSAvoidsystem architecture complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent segments the AI-powered system into distinct functional modules: an NLP module for text analysis and keyword extraction, a search engine module for querying datastores, and a presentation module for displaying results. This segmentation allows each component to be optimized independently and managed separately, reducing overall system complexity while maintaining high productivity.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent creates a multi-functional AI system that performs multiple tasks: analyzing project descriptions, extracting keywords, searching datastores, and presenting results. By designing a universal platform that handles all these functions within a single integrated system, the patent avoids the need for multiple separate tools and interfaces, thereby improving productivity without proportionally increasing complexity.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20240303568A1Artificial Intelligence-Powered Aggregation of Project-Related Collateral

Publication Date: 2024.09.12 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

A data processing system implements receiving, from a client device, first textual content inserted into a user interface element of a first user interface on the client device, the first textual content comprising a natural language description of a first project for which a first workspace is to be created, analyzing the first textual content to obtain keywords in the first textual content using an NLP model trained to receive the textual content and to output the keywords, conducting a search for candidate collateral items associated with each of the keywords using a first search engine, causing the client device to present the candidate collateral items on the first user interface, receiving, from the client device, a first user input selecting one or more of the collateral items from among the candidate collateral items, and causing the client device to present a second workspace user interface representing the first workspace.