Neural Network Feedback Analysis System
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Solution Overview
Problem
In environments with multiple users accessing various resources, manually processing user feedback is time-consuming and resource-intensive, requiring significant domain-specific knowledge and often resulting in delays in addressing issues due to the unstructured and ambiguous nature of open text submissions.
Innovation Solution
A system utilizing machine learning, specifically neural networks and Natural Language Processing (NLP) models, to categorize and analyze feedback, automatically identifying actionable issues by extracting features, generating feature vectors, and determining appropriate actions, thereby reducing the need for human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual processing of user feedback is used, then domain-specific knowledge can be applied to analyze feedback accurately, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The patent introduces NLP models and machine learning systems as intermediary components between raw user feedback and human engineers. These intermediaries automatically process, categorize, and extract actionable insights from unstructured feedback text, filtering and prioritizing issues before human review. This mediator layer maintains analysis accuracy by applying domain-specific knowledge through trained models while dramatically reducing the time engineers spend on manual feedback processing.
Solution Approach 2:
The patent replaces the mechanical system of manual human feedback analysis with an automated information processing system based on NLP and machine learning. Instead of engineers directly reading and analyzing unstructured feedback text, the system uses computational models to automatically parse, categorize, and extract meaningful insights, substituting human cognitive labor with automated processing while preserving analytical accuracy.
2Adaptability or versatility
If open text feedback submissions are allowed, then users can provide detailed and varied feedback, but the unstructured nature makes processing difficult and ambiguous
Solution Approach 1:
The patent applies segmentation by breaking down the complex task of processing unstructured open-text feedback into distinct manageable components: text preprocessing, feature extraction, NLP model analysis, categorization, and actionable issue identification. Each component handles a specific aspect of the processing pipeline, transforming the monolithic complex task into modular operations that can be independently optimized and maintained.
Solution Approach 2:
The patent changes the parameters of feedback representation by transforming unstructured text into structured feature vectors and categorized data. The system applies various NLP techniques to convert free-text feedback into standardized formats including extracted entities, sentiment scores, issue categories, and priority levels, changing the data parameters from ambiguous text to quantifiable structured information that is easier to process and analyze.
3Measurement precision
If human engineers review all feedback manually, then comprehensive analysis can be performed, but it prevents efficient and accurate analysis at scale
Solution Approach 1:
The patent implements partial action by having NLP models and machine learning systems perform the initial screening, categorization, and prioritization of feedback, handling the majority of processing automatically. Human engineers then focus only on reviewing and validating the most critical or ambiguous cases identified by the automated system. This partial automation approach maintains high analysis accuracy for complex cases while dramatically increasing overall processing throughput.
Solution Approach 2:
The patent uses copying by creating structured representations and feature vectors that replicate the essential information from unstructured feedback text. These copied structured formats preserve the meaningful content while enabling automated processing, allowing the system to maintain analytical accuracy by preserving key information in a machine-readable format that can be efficiently processed and reviewed.
Data Source
AI summary
Apparatuses, systems, and techniques are presented to process communications in a computing environment. In at least one embodiment, one or more neural networks are used to generate, based at least in part upon one or more communications received from one or more users of one or more applications, a textual description of one or more actions to be taken regarding a performance of the one or more applications.