Annotated Text Conversion for Non-Rich Display Devices
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Solution Overview
Problem
Current methods fail to efficiently convert analytical processing results into natural language sentences for devices that do not support rich-text, making it difficult to display and utilize semantic information effectively.
Innovation Solution
A system and method that processes response messages from analytical applications, parses them to select a semantic model, translates the messages into non-rich text, and annotates them for semantic meaning, allowing display on devices that only support text blobs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If analytical processing results are converted to natural language sentences with rich-text (HTML) to convey semantic meaning, then semantic information is effectively conveyed, but devices that do not support rich-text cannot display or utilize this information
Solution Approach 1:
The patent introduces an intermediary conversion layer that translates rich-text semantic representations into non-rich text formats. The system converts analytical processing results into natural language sentences and then transforms them into a format compatible with devices that do not support rich-text, thereby serving as a mediator between rich-text requirements and non-rich-text capabilities.
Solution Approach 2:
The patent changes the textual representation parameters by converting from rich-text formats (HTML, CSS styling) to non-rich text formats. This parameter transformation maintains the semantic meaning while adapting the presentation format to be compatible with simpler display devices, effectively resolving the contradiction between information fidelity and device adaptability.
2Adaptability or versatility
If analytical processing results are converted to natural language sentences for non-rich text devices, then device compatibility is achieved, but the conversion process becomes complex requiring multiple processing steps
Solution Approach 1:
The patent segments the conversion process into distinct functional modules: parsing the analytical processing results, selecting appropriate semantic models, translating into natural language, and converting to non-rich text format. This segmentation allows each step to be optimized independently and simplifies the overall complex conversion process by breaking it down into manageable components.
Solution Approach 2:
The patent performs preliminary actions by pre-processing the analytical processing results to identify and extract semantic information before the actual conversion to non-rich text. This preliminary analysis of the data structure and semantic content enables more efficient conversion and reduces the complexity of the main conversion process by preparing the data in advance.
Data Source
AI summary
A method and or system for processing a response message from an analytical application that includes receiving the response message; parsing the response message to facilitate selecting a semantic model to translate the response message; obtaining the semantic model to translate the response message; translating the response message using the semantic model; and converting the translated response message to non-rich text. The non-rich text can be annotated for semantic meaning that can be displayed for example on a “dumb” display that does not support rich-text formats.