Collaborative Content Recommendation Engine for Document Authoring
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Solution Overview
Problem
Users of document authoring applications often face difficulties in finding relevant content from their previous documents, as the lists of recent files provided may not be relevant, requiring manual searching through folders.
Innovation Solution
A system and method that utilizes a content recommendation engine to identify and suggest relevant documents based on user feedback, using machine learning models to analyze metadata, keywords, and user behavior, and presents these suggestions in a collaborative content sharing board, allowing users to select and share documents for feedback, which adjusts the order and relevance of suggested content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a list of recent files is provided in the document authoring application, then users can access previously generated documents, but the list may not be relevant to the current document being authored, requiring manual searching
Solution Approach 1:
The system implements feedback loops where user interactions with suggested content (selections, views, edits) are continuously monitored and used to refine future recommendations. The collaborative platform collects feedback from multiple users about content relevance, and this feedback is fed back into the recommendation engine to improve the accuracy of suggested content lists, resolving the contradiction between automation and relevance.
Solution Approach 2:
The recommendation engine automatically generates and updates suggested content lists based on analysis of the current document being authored, user preferences, and collaborative platform data. Instead of requiring users to manually search through folders, the system self-services by proactively presenting relevant content, thereby improving both productivity and ease of operation simultaneously.
2Productivity
If manual searching through folders is required to find relevant content, then all documents are accessible, but users spend excessive time searching and productivity decreases
Solution Approach 1:
The system performs preliminary actions by pre-analyzing and pre-ranking potential relevant content before the user needs it. The recommendation engine pre-processes document metadata, keywords, and collaborative platform data to generate ready-to-present suggested content lists, eliminating the need for users to perform time-consuming manual searches and thus reducing time loss while maintaining high productivity.
Solution Approach 2:
The patent replaces the mechanical manual searching process with an automated intelligent recommendation system. Instead of users manually navigating folders and evaluating documents, the system uses machine learning models and collaborative data to automatically identify and present relevant content, substituting human effort with automated computational processes that significantly reduce search time while maintaining or improving content retrieval effectiveness.
3Measurement precision
If a collaborative content sharing board is implemented with real-time feedback, then recommendation accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the complex recommendation functionality into distinct modular components: a document analysis module that processes current document content, a collaborative platform module that collects user feedback, a machine learning model module that generates recommendations, and a user interface module that presents suggestions. This segmentation allows each component to be developed, maintained, and scaled independently, managing overall system complexity while enabling high measurement precision through specialized processing in each module.
Solution Approach 2:
The patent introduces intermediary layers between different system components to manage complexity. An API layer mediates between the document authoring application and the collaborative platform, standardizing data exchange. A recommendation engine intermediary processes raw collaborative feedback and transforms it into refined content suggestions. These intermediaries buffer complexity, allowing the system to achieve high recommendation accuracy without requiring all components to be tightly coupled or overly complex.
Data Source
AI summary
A system and method for summarizing suggested content and sharing the summarized suggested content is described. In one aspect, a computer-implemented method includes performing an analysis of text of a document, searching a document library for content elements and documents based on the analysis of the text, identifying candidate documents and candidate content based on the searching, presenting a list of candidate documents or candidate content with the document authoring application, receiving a selection of a candidate document or candidate content from the list in the document authoring application, and providing the selected candidate document to a collaborative content sharing platform, the collaborative content sharing platform configured to generate a graphical user interface that displays a list of shared documents, the shared documents includes candidate documents selected by one or more users of a group of users that share access to the collaborative content sharing platform.