People-Centric Collaborator Prediction for Pending Document Actions

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Users in cloud-based collaborative environments spend significant time identifying collaborators and documents to interact with, leading to inefficient use of resources and disrupted user experience due to extensive searching and reviewing of numerous documents with pending actions.

Innovation Solution

A system and method that predicts collaborators and relevant documents using machine learning or heuristic approaches based on past collaboration attributes, providing users with direct access to predicted collaborators and pending actions, reducing the need for extensive searching and enhancing resource efficiency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of time

If users manually identify collaborators and documents in cloud-based collaborative environments, then users can access all available documents and collaborators, but users spend significant time searching and reviewing numerous documents leading to inefficient resource usage

Engineering Contradiction:

Improvetime spent searching for collaborators and documentsVSAvoidcollaboration efficiency

Core Design Contradiction:

Loss of timeVSProductivity

Solution Approach 1:

The system performs preliminary actions by analyzing past collaboration data, user profiles, and document metadata before users need to search. Collaborators and documents are pre-ranked and pre-filtered based on relevance algorithms, so when users need to collaborate, the most relevant options are already prepared and presented, eliminating the need for manual searching through all available content.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The manual mechanical process of users searching through documents and contacting collaborators is replaced with an automated information system. The system uses algorithms to automatically identify relevant collaborators and documents based on analysis of collaboration history, user preferences, and content metadata, substituting human effort with computational processing.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If users interact with all shared documents and collaborators, then users can ensure no relevant information is missed, but network bandwidth and processing resources are wasted on unnecessary interactions

Engineering Contradiction:

Improvecompleteness of collaboration coverageVSAvoidnetwork bandwidth and processing resources

Core Design Contradiction:

ReliabilityVSLoss of energy

Solution Approach 1:

Instead of treating all collaborators and documents uniformly, the system applies local quality by differentiating between high-priority and low-priority interactions. Relevance algorithms analyze specific characteristics of each user-document-collaborator relationship and assign different levels of importance, allowing the system to focus resources on locally optimized relevant interactions rather than uniformly processing all possible interactions.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system performs partial action by selectively processing only the most relevant subset of collaborators and documents rather than all available content. Relevance thresholds filter out low-priority items, allowing the system to achieve sufficient collaboration coverage with a fraction of the total content, avoiding excessive processing of unnecessary materials.

Inventive Principle:

Principle #16Partial or excessive action

3Adaptability or versatility

If cloud-based platforms provide extensive collaborative tools and shared documents, then users have access to comprehensive collaboration capabilities, but users experience disrupted user experience due to extensive searching and reviewing

Engineering Contradiction:

Improvecollaboration tool capabilitiesVSAvoiduser experience during document search and review

Core Design Contradiction:

Adaptability or versatilityVSEase of operation

Solution Approach 1:

The system introduces an intermediary layer between the user and the extensive collaborative tools and documents. Relevance algorithms act as mediators that interpret user needs and translate them into targeted recommendations, filtering the vast array of available tools and documents into a manageable subset. This intermediary processing maintains access to comprehensive capabilities while shielding users from the complexity of navigating all available options.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20250335070A1Intelligent people-centric predictions in a collaborative environment

Publication Date: 2025.10.30 GOOGLE LLC

AI summary

A method includes predicting one or more collaborators for a first user among other users that are associated with electronic documents hosted by the cloud-based content management platform. A pending action corresponding to an electronic document and directed to the first user by a second user of the one or more predicted collaborators is identified. A response of the first user to the pending action is predicted by identifying one or more action attributes of the pending action, and generating, based on the one or more action attributes, information identifying i) a predicted response by the first user to the pending action, and 2) a likelihood the first user will respond to the pending action using the predicted response. Upon generating the information, a user interface (UI) identifying the predicted response to the pending action is provided for presentation at a client device of the first user.