Document Search Ranking for In-Editor Citation Retrieval
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Solution Overview
Problem
Existing word processing applications fail to integrate seamlessly with source documents, making it difficult and resource-intensive for authors to locate and cite relevant documents, especially when quotations or assertions appear across multiple documents.
Innovation Solution
A system utilizing a machine-learning model to identify and rank documents based on similarity to a user-input text string, generating an ordered list of document types and facilitating the insertion of citations within a word processing environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If authors use existing word processing applications to draft documents, then writing can proceed independently of source documents, but authors must operate multiple additional software applications and computing devices to locate and cite source documents, increasing operational complexity and time consumption
Solution Approach 1:
The patent combines the word processing application with integrated source document search and citation capabilities into a single unified system. The system merges document editing functions with semantic search, machine learning-based document type classification, and automated citation generation, eliminating the need to switch between multiple applications and computing devices.
Solution Approach 2:
The word processing application is enhanced with multi-functional capabilities including real-time semantic search across source documents, machine learning model integration for document type prediction, automated citation insertion, and cross-document referencing. This universal system performs multiple functions that previously required separate specialized applications.
2Productivity
If authors manually search for source documents using multiple applications, then they can locate factual sources, but the process becomes difficult and resource-intensive especially when quotations appear in several different documents
Solution Approach 1:
The system performs preliminary actions by pre-classifying source documents into different document types using machine learning models before the author needs to cite them. When an author selects text for citation, the system has already organized source documents by type and can immediately retrieve relevant citations without requiring the author to manually search through multiple documents and applications.
Solution Approach 2:
The patent replaces manual mechanical searching and citation processes with automated machine learning-based semantic search and document classification systems. Instead of authors manually browsing through multiple documents and applications, the system automatically analyzes the selected text, predicts the appropriate document type, and retrieves relevant citations using semantic similarity matching and trained classification models.
3Measurement precision
If the system generates an ordered list of document types using machine learning, then document ranking becomes more accurate based on relevance, but the system complexity increases due to integration of multiple processing components
Solution Approach 1:
The patent segments the complex document ranking task into distinct functional modules: a machine learning model that predicts document types based on selected text, a semantic search component that finds matching source documents, and a ranking algorithm that combines document type predictions with similarity scores. Each module handles a specific aspect of the ranking process, making the overall system more manageable despite its complexity.
Solution Approach 2:
The system introduces an intermediary machine learning model that acts as a mediator between the selected text and the document search results. This model predicts document types and provides guidance to the search algorithm, improving ranking accuracy by bridging the gap between raw text selection and sophisticated document retrieval without requiring direct complex integration between all system components.
Data Source
AI summary
Provided are systems, methods, and computer program products for searching a plurality of documents based on a text string. The system includes at least one processor programmed or configured to identify a plurality of documents including a plurality of document types, each document of the plurality of documents including a document type, receive a text string based on user input, generate, with a machine-learning model, an ordered list of document types based the text string, search the plurality of documents for the text string to identify a subset of documents based on similarity between the text string and each document of the subset of documents, rank the subset of documents based at least partially on the similarity, a document type of each document of the subset of documents, and the ordered list of document types, and generate a graphical user interface based on the ranked list of documents.