Collaborative Publishing Workflow With AI Feedback for Long-Form Consistency
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Solution Overview
Problem
Conventional generative language models require significant human intervention for output review, revision, and training, especially for generating long-form documents at scale, and struggle with inconsistent outputs and limited input parameters, making them resource-intensive and inefficient for large-scale content generation.
Innovation Solution
A generative collaborative publishing system that includes a prompt generation subsystem, content generation subsystem, pre-publication feedback subsystem, and post-publication feedback subsystem to automate and refine the content generation process, reducing the need for human intervention and improving output quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional generative language models are used for large-scale content generation, then content volume can be increased, but human intervention for review and revision increases significantly
Solution Approach 1:
The patent implements automated feedback loops where AI reviewers analyze generated content and provide corrections, which are then used to re-generate improved versions. This closed-loop feedback system replaces manual human review with automated AI-based quality assessment and iterative refinement, maintaining high productivity while reducing human intervention.
Solution Approach 2:
The patent introduces an AI-based intermediary review system that acts as a mediator between content generation and final publication. This intermediary layer automatically filters, evaluates, and refines generated content using trained language models, preventing the need for direct human review of every piece of content while ensuring quality standards are met.
2Length of moving object
If conventional generative language models are used for long-form document generation, then document length can be increased, but output consistency deteriorates
Solution Approach 1:
The patent divides long-form document generation into multiple sequential segments or sections, where each segment is generated and reviewed independently. This segmentation approach maintains consistency within each section while allowing the overall document to achieve substantial length, preventing the degradation of quality that occurs in single-pass long-form generation.
Solution Approach 2:
The patent implements preliminary action by pre-training language models on specific writing styles, formats, and quality standards before generation. This pre-conditioning of the model ensures that subsequent long-form generation maintains consistent tone, style, and quality throughout the document, rather than drifting over length.
3Device complexity
If conventional generative language models are used with limited input parameters, then system complexity can be reduced, but manufacturing precision of content quality deteriorates
Solution Approach 1:
The patent implements self-service by enabling the language model to automatically adjust and optimize its own generation parameters based on feedback from automated review systems. The model learns from its outputs and iteratively improves quality without requiring complex external parameter tuning, maintaining simple system architecture while achieving high content quality precision.
Data Source
AI summary
Embodiments of the disclosed technologies include identifying a user of a network as a possible contributor of digital content to a document that is published via the network; identifying at least two different channels on the network that are each capable of sending, to the user, an invitation for the user to contribute to the document; for each of the at least two different channels, determining respective channel usage data, where the channel usage data includes, for a channel of the at least two different channels, historical data relating to use of the channel by the user to interact with content; for each of the at least two different channels, computing respective channel affinity scores based on the respective channel usage data, where a channel affinity score includes, for a channel of the at least two different channels, an estimate of a likelihood of the user contributing to the document through the channel; based on the respective channel affinity scores, selecting an optimal channel from the at least two different channels; and sending the invitation to the user to contribute to the document through the optimal channel.