AI Document Generation Using Historical Interaction Vectors
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Solution Overview
Problem
Existing automated document creation tools, even those using basic AI, fail to produce customized digital documents that are accurate, grammatically correct, and tailored to specific recipients, often appearing disjointed and not easily understood, lacking the ability to leverage historical user interaction data for intelligent document generation.
Innovation Solution
A system that analyzes historical user interactions and corresponding digital documents to train interaction classification and language generation models, using tokenization, encoding, and multidimensional vectors to generate customized digital documents without relying on standard templates, employing advanced AI techniques like neural networks and transformer-based models for accurate and contextually correct output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual document generation is used, then document accuracy and customization are improved, but time consumption and efficiency deteriorate
Solution Approach 1:
The system enables automated self-service document generation by training AI models on historical user interactions and corresponding documents. The trained models automatically generate customized documents from new user interactions without human intervention, achieving both high accuracy through learned patterns and high efficiency through automation.
2Productivity
If basic AI techniques are used for automated document creation, then productivity is improved, but document quality and natural language understanding deteriorate
Solution Approach 1:
The system transforms the basic AI approach by changing key parameters: using transformer-based models with self-attention mechanisms instead of simple sequence-to-sequence models, training on paired historical data to learn document structures and language patterns, and employing beam search decoding for improved generation quality. These parameter changes enable the system to produce high-quality, natural-sounding documents while maintaining automated efficiency.
3Productivity
If template-based document generation is used, then document generation speed is improved, but document naturalness and coherence deteriorate
Solution Approach 1:
Instead of copying from fixed templates, the system learns document patterns by copying from historical user interactions and their corresponding documents. The trained models generate documents that follow natural structures and language patterns learned from historical data, producing coherent and natural-sounding documents without relying on rigid templates.
4Manufacturing precision
If historical data is leveraged for training, then document customization and accuracy are improved, but system complexity and training time increase
Solution Approach 1:
The system performs preliminary action by pre-training models on historical user interactions and corresponding documents before deployment. This offline training phase captures document patterns, structures, and language styles, enabling the system to generate accurate customized documents without requiring complex real-time processing or manual configuration during actual document generation.
Data Source
AI summary
Methods and apparatuses are described in which unstructured computer text is analyzed for generation of customized digital documents. A server tokenizes and encodes historical user interactions and historical digital documents into multidimensional vectors. The server trains an interaction classification model using the multidimensional vectors as input to generate a classification for an input user interaction, and trains a language generation model using the multidimensional vectors as input to generate a customized digital document based upon an input user interaction. The server receives a new user interaction and encodes the new user interaction into a new multidimensional vector. The server executes the trained interaction classification model using the new vector as input to generate a digital document classification. The server executes the trained language generation model using the new vector and the classification as input to generate a customized digital document.