Catalog Item Event Detection Using Neural Negativity Scoring
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Solution Overview
Problem
Retailers often fail to detect events relevant to catalog items in a timely manner, leading to potential liabilities due to the sale of items contrary to their policies, such as negative news or trending events.
Innovation Solution
Utilizing trained neural networks to analyze online sources for event data, determine negativity scores, identify relevant topics and key phrases, and associate them with catalog items, with manual review for validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional manual monitoring methods are used to detect events relevant to catalog items, then the system complexity remains low, but the detection time is delayed and liabilities increase
Solution Approach 1:
The patent replaces manual monitoring mechanisms with an automated neural network-based system. The neural network continuously analyzes news articles and online sources to detect events relevant to catalog items, eliminating the need for human reviewers and significantly reducing detection time while accepting increased system complexity through automated AI infrastructure.
Solution Approach 2:
The patent introduces a neural network as an intermediary between online news sources and catalog item management. This intermediary automatically processes and analyzes incoming information, filtering and identifying relevant events before they reach decision-makers, thereby reducing detection time while managing complexity through a dedicated processing layer.
2Productivity
If automated neural network systems are deployed for rapid event detection, then detection speed increases and liabilities reduce, but system complexity increases
Solution Approach 1:
The patent implements automated neural networks to replace manual monitoring processes, achieving rapid continuous analysis of online sources. The system processes news articles and events in real-time, automatically identifying relevance to catalog items without human intervention, thereby maximizing detection speed despite the complexity of maintaining AI infrastructure.
Solution Approach 2:
The neural network system operates autonomously, self-managing the detection and classification of events without requiring constant human oversight. The system automatically trains on new data, adjusts its parameters, and continues monitoring without interruption, maintaining high productivity while the complexity is encapsulated within the self-sufficient automated system.
3Reliability
If continuous automated monitoring is implemented, then detection accuracy improves and liabilities are reduced, but the cost and complexity of the system increase
Solution Approach 1:
The patent replaces error-prone manual monitoring with automated neural networks that provide consistent, unbiased analysis. The system continuously processes information without fatigue or distraction, maintaining high detection accuracy through automated pattern recognition and relevance assessment, accepting the complexity trade-off for improved reliability.
Solution Approach 2:
The system implements continuous feedback loops where detection results are analyzed and used to refine the neural network's performance. The system learns from false positives and negatives, automatically adjusting its detection thresholds and parameters to improve accuracy over time, managing complexity through iterative self-improvement rather than constant human tuning.
Data Source
AI summary
In some embodiments, apparatuses and methods are provided herein useful for event detection relative to catalog items available for commercial sale. In some embodiments, there is provided a system for event detection relative to catalog items available for commercial sale including an interface; a memory; and the first control circuit. The first control circuit configured to execute the computer-implemented code to: receive event data corresponding to one or more events referred to in one or more online sources; determine a corresponding score for each of the event data based on a relative level of negativity; identify one or more topics and key phrases of one or more of the event data having at least a specified score; identify one or more catalog items that have a similarity to the one or more topics and key phrases; and output an indication.