Text Unit Replacement via Contextual Annotation
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Solution Overview
Problem
Existing document retrieval tools are inefficient for finding similar documents or sub-components like sentences or paragraphs, leading to wastage of human and computational resources, especially when searching for text to match specific styles or objectives in large document corpora.
Innovation Solution
A method and system that analyze an electronic input document to identify text units and contextual information, generating annotations for predictive characteristics, and then suggest replacement texts from a corpus of documents based on these annotations, optimizing the retrieval process by evaluating candidate texts for suitability and presenting the best matches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional document retrieval tools are used to search for similar documents or sub-components in a large corpus, then the search can be performed manually, but the process becomes laborious and time-consuming
Solution Approach 1:
The patent replaces manual mechanical searching with an automated computer-based system that uses machine learning models and algorithms to automatically retrieve and evaluate documents based on user input, eliminating the need for manual browsing and comparison of documents
Solution Approach 2:
The system enables self-service document retrieval by automatically analyzing user input, generating search queries, retrieving relevant documents from the corpus, and presenting results without requiring manual intervention in the search process
2Reliability
If manual evaluation of retrieved documents is performed to assess quality or suitability, then the retrieval process can be completed, but human resources are wasted in the evaluation process
Solution Approach 1:
The patent replaces manual human evaluation with automated machine learning models that compute document quality and suitability scores, substituting human cognitive effort with computational algorithms that can process and evaluate documents efficiently
Solution Approach 2:
The system introduces an intermediary automated evaluation layer between document retrieval and final selection, where machine learning models act as mediators to assess document quality and rank results, reducing the need for direct human evaluation
3Adaptability or versatility
If existing document retrieval tools are used for searching particular sub-components like sentences or paragraphs, then the search can be performed, but the tools are not optimized for this type of searching
Solution Approach 1:
The patent segments documents into various levels including full documents, sections, paragraphs, and sentences, allowing the system to retrieve and evaluate specific sub-components rather than requiring retrieval of entire documents, thereby improving search efficiency for targeted information
Solution Approach 2:
The system dynamically adapts its retrieval strategy based on the user's needs, adjusting the granularity of retrieval from full documents to specific sub-components like sentences or paragraphs, optimizing the search process for different types of queries
4Reliability
If authors explore the organization's document corpus to find previously-written documents similar to their new document, then they can maintain consistent style, but the exploration process is difficult and time consuming
Solution Approach 1:
The patent replaces manual exploration and comparison of documents for style consistency with automated machine learning models that analyze and compare writing styles, tone, and formatting characteristics, automatically identifying documents that match the desired style without manual intervention
Solution Approach 2:
The system provides feedback to authors by automatically comparing their draft documents against the organization's corpus, identifying style inconsistencies and suggesting improvements based on analysis of previously written documents that match the intended style and format
Data Source
AI summary
An electronic input document presented on a display of a client is examined to identify a text unit in the electronic input document and contextual information about the input document. A set of annotations for the text unit and the input document are determined responsive to the contextual information for the text unit. Responsive to the set of annotations, a set of candidate texts are identified from a corpus of documents that can replace the text unit. The candidate texts are evaluated in the set of candidate texts to identify a subset of the set of candidate texts as a set of replacement texts for the text unit. At least one replacement text from the set of replacement texts is presented on the display of the client.