Enterprise Content Merging via Smart Organizer
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Solution Overview
Problem
Content management systems face inefficiencies in searching and organizing large numbers of content items, as they lack mechanisms to merge correlated content into single items or virtual structures, leading to time-consuming and resource-intensive searches.
Innovation Solution
The system dynamically merges correlated content items into single documents or organizes them into folios based on metadata, using a smart content organizer and file merge suggestion engine, allowing for easier access and reducing the number of items to search through.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the number of content items in the ECM system increases, then the system capacity and storage capability are improved, but the search operation becomes more complicated and time consuming
Solution Approach 1:
The patent applies merging by combining multiple correlated content items into a single virtual structure called a folio. The folio aggregates related documents, images, or other content items that share common metadata attributes (such as project name, client, or date range) into one unified searchable unit. This reduces the number of individual items the system must search through, directly addressing the time-consuming search problem while preserving the ability to access all underlying content items.
Solution Approach 2:
The patent segments content items into logical groups based on their metadata characteristics. By dividing the large collection of content items into smaller, metadata-based segments (folios), the system can process and search each segment more efficiently. The segmentation is based on common metadata values, creating manageable groups that reduce search complexity while maintaining access to the full content.
2Quantity of substance
If the number of content items in the ECM system increases, then the system capacity is improved, but the search operation becomes more resource intensive
Solution Approach 1:
The folio structure merges multiple content items into a single virtual unit, reducing the total number of items the search engine must process. This consolidation directly reduces computational resources, memory usage, and processing time required for search operations, while still providing access to all underlying content items through the folio.
Solution Approach 2:
The system performs preliminary organization of content items into folios based on their metadata before search operations are needed. This pre-grouping reduces the search space and allows the search engine to operate more efficiently on smaller, pre-organized datasets rather than processing the entire large content collection for each search query.
3Device complexity
If there are no mechanisms to merge correlated content items, then the system simplicity is maintained, but the ease of accessing correlated content deteriorates
Solution Approach 1:
The folio structure serves multiple functions simultaneously: it organizes correlated content items, provides a unified search interface, enables batch operations on related content, and maintains metadata relationships. This multi-functionality improves ease of access to correlated content without requiring multiple separate systems or complex manual processes.
Solution Approach 2:
The system automatically creates and maintains folios based on metadata attributes of content items. The folio structure self-organizes content items according to their metadata characteristics, eliminating the need for manual sorting or complex user intervention. Users simply query the folio structure, and the system automatically retrieves relevant content items based on their metadata relationships.
Data Source
AI summary
Described herein are systems and methods for providing a correlated content organization in a content management system based upon a training set. In accordance with an embodiment, the systems and methods described herein can build a training set based upon observations of received inputs to determine patterns that are used often in content merges. Once a pattern is established, the systems and methods can provide indications of proposed merges based upon the training set and rules established therefrom that fit the same, or similar (e.g., within a defined variant) of the pattern. The system can then receive an indication of whether the suggestion is accepted or rejected, and such decision can be fed back into the learning system. This way the accuracy of the content merge improves over time.