Objectifying Non-Text Content for Deep Searchability
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Solution Overview
Problem
Non-native files, such as physical documents or scanned images, often lose editability and searchability due to the loss of native format data, limiting users' ability to edit or search for non-text content like images and charts.
Innovation Solution
A method and system that objectify non-text content in non-native files by determining tags and generating metadata, allowing the creation of a new native file with enhanced editability and searchability, enabling deep searching and editing capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If non-native files (scanned images, physical documents) are used to preserve document accessibility, then document portability and archiving are improved, but editability and searchability of non-text content are lost
Solution Approach 1:
The patent introduces an intermediary processing system that converts non-native file formats into native formats with embedded metadata. This intermediary process acts as a bridge between the portable non-native format and the editable native format, allowing documents to maintain both portability and editability through format conversion and metadata enrichment
Solution Approach 2:
The patent changes the parameter of file format from non-native (scanned image) to native (editable document format) while embedding metadata parameters that preserve searchability. This parameter transformation allows the document to transition from a static image state to an editable state with retained search capabilities
2Adaptability or versatility
If non-native files (scanned images) are used to preserve document accessibility, then document portability is improved, but searchability of non-text content is lost
Solution Approach 1:
The patent performs preliminary actions by embedding metadata and composition information into the native file format during the conversion process. This preliminary enrichment of the document with searchable metadata ensures that searchability is preserved before the document is used or archived in its portable format
Solution Approach 2:
The conversion and metadata embedding process serves as an intermediary that transfers not only the visual content but also embedded searchable metadata from the non-native format to the native format, preventing loss of searchability information
3Measurement precision
If OCR software is used to recognize text in scanned documents, then text recognition is improved, but non-text objects remain unrecognizable and uneditable
Solution Approach 1:
The patent extends the functionality beyond traditional OCR text recognition to include recognition and metadata embedding for non-text objects such as images, charts, and tables. This multi-functional approach allows the same processing system to handle both text and non-text content, making the tool universally applicable to all document elements
Solution Approach 2:
The patent segments the document processing into distinct components: text recognition, non-text object identification, and metadata generation. By segmenting these functions, the system can apply specialized processing to each type of content while maintaining overall document coherence and editability
4Adaptability or versatility
If native files are converted to non-native formats to improve document sharing, then document compatibility is improved, but individual cell editing capability is lost
Solution Approach 1:
The patent changes the file format parameter from non-native to native while preserving the granular editing capability by embedding composition metadata. This parameter transformation allows the document to switch between compatibility mode and editable mode without losing individual cell editing functionality
Data Source
AI summary
A method for objectifying non-text content within a non-native file includes objectifying an object of the non-text content by determining a tag for the object, the tag defining a portion of the object in native file format, and creating an objectified object including the object and the tag. The method further includes generating, based on the objectified object, metadata including composition information for the objectified object, at least part of the composition information being text data capable of being searched by a native application for the native file, and generating a new native file including the objectified object appended with the metadata.