Controllable Reading Guides for Context-Aware Text Generation
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Solution Overview
Problem
Existing natural language generation systems lack the ability to generate complex language based on context, fail to provide users with control over the meaning of generated text, and are limited in operating relative to short text segments without contextual analysis.
Innovation Solution
The development of semantically infused language models that use neural networks to learn contextual relations between abstract semantic features in text, allowing for the generation of unique natural language that can express specific meaning based on user interaction and contextual analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If statistical models are used to generate text based on word probabilities, then text generation capability is achieved, but the meaning of generated text cannot be controlled and may be nonsensical
Solution Approach 1:
The patent introduces an intermediary layer between the statistical text generation model and the user interface. This intermediary includes semantic analysis components that interpret the generated text, determine its meaning, and provide controls that allow users to guide the semantic direction of generation. The system mediates between automatic generation and user control by translating user intent into constraints for the generation model.
Solution Approach 2:
The system implements feedback loops where the meaning of generated text is analyzed and fed back to the user for validation or correction. Users can provide feedback on whether the generated text conveys the intended meaning, and this feedback is used to adjust generation parameters or refine semantic constraints for subsequent generation attempts.
2Productivity
If neural networks are trained on surface-level words, then language patterns can be learned, but contextual relations between abstract semantic features cannot be captured
Solution Approach 1:
The patent extends the traditional word-level language model by adding a semantic dimension. Instead of only operating at the surface level of words, the system incorporates semantic features as an additional dimension of analysis. This allows the model to capture relationships between abstract concepts while maintaining the ability to recognize language patterns from training data.
Solution Approach 2:
The system implements a nested architecture where multiple levels of analysis are combined: surface-level word patterns are nested within semantic feature analysis, which is in turn nested within contextual relationship modeling. This hierarchical nesting allows the system to operate effectively at multiple levels of abstraction simultaneously.
3Speed
If systems operate on short text segments, then processing speed is maintained, but contextual analysis of surrounding text is not possible
Solution Approach 1:
The patent divides the text processing task into segmented operations that can be performed efficiently. The system segments text into manageable units for processing while maintaining awareness of the broader context through hierarchical analysis. This allows rapid processing of individual segments while still capturing contextual relationships across the entire document.
Solution Approach 2:
The system performs preliminary analysis of contextual relationships before generating or processing specific text segments. By pre-processing and storing semantic relationships and contextual information in advance, the system can quickly retrieve and apply this context during text generation without slowing down the main processing operations.
Data Source
AI summary
The presently disclosed embodiments may include a computer readable medium including instructions that when executed by one or more processing devices cause the one or more processing devices to perform a method. The method may include: identifying a location in an electronic document for at least one text insertion; automatically generating one or more text insertion options, based on a syntactic or semantic context of text in the electronic document before or after the identified location, and causing the one or more text insertion options to be displayed to the user; receiving, from a user, a selection of a text insertion option from among the one or more text insertion options; and causing the selected text insertion option to be included in the electronic document at a location that includes the identified location.