Machine Learning Deficiency Predictions from Low-Results Web Queries
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Solution Overview
Problem
Conventional systems fail to accurately identify digital items not present in databases, waste computing resources with low-relevance responses, and lack flexibility in generating query responses due to rigid reliance on static digital lists, leading to inefficiencies and inflexibilities.
Innovation Solution
A deficiency identification system using machine learning models to extract digital signals from low-results queries, generate item deficiency predictions, and provide user interfaces for dynamically expanding digital item lists based on demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems rely on static digital lists to generate query responses, then system simplicity is maintained, but accuracy and flexibility deteriorate because digital lists fail to reflect all pertinent digital content
Solution Approach 1:
The patent transforms the static digital list into a dynamic system that automatically updates based on machine learning predictions. The system continuously monitors query patterns and adjusts the digital list accordingly, allowing it to adapt to changing content requirements without manual intervention, thus improving accuracy while maintaining operational simplicity.
Solution Approach 2:
The system implements a feedback loop where query responses are analyzed to identify missing content, which then triggers automatic updates to the digital list. This closed-loop mechanism ensures the system learns from its performance and continuously improves accuracy by incorporating newly identified pertinent content back into the digital list.
2Productivity
If conventional systems repeatedly provide low-relevance responses when digital lists lack pertinent items, then resource consumption increases, but response generation continues without improvement
Solution Approach 1:
The system performs preliminary analysis of query patterns and digital list contents to predict missing pertinent content before actual queries are processed. By proactively identifying and adding missing items to the digital list based on predicted demand, the system prevents the generation of low-relevance responses and avoids wasting computing resources on futile search operations.
Solution Approach 2:
The system automatically identifies deficiencies in its own digital list and performs self-correction by adding missing pertinent content without external intervention. This self-service capability eliminates the need for manual list updates and prevents the continuous waste of resources on repeated failed query responses, thereby improving overall efficiency.
3Ease of operation
If conventional systems require multiple separate user interfaces to monitor queries, responses, and digital item lists, then system functionality is comprehensive, but ease of operation deteriorates
Solution Approach 1:
The patent consolidates multiple separate user interfaces into a single unified interface that provides comprehensive monitoring and control of queries, responses, and digital list status. This merged interface displays all relevant information in an integrated view, allowing users to manage the entire system through one interface rather than navigating between multiple separate interfaces, thereby significantly improving ease of operation.
4Adaptability or versatility
If conventional systems use rigid static digital lists, then data management simplicity is maintained, but adaptability deteriorates because lists cannot reflect real-time demand changes
Solution Approach 1:
The system transforms the rigid static digital list into a dynamic data structure that automatically adapts to real-time demand changes. Machine learning models continuously analyze query patterns and automatically update the digital list to reflect current user needs, enabling the system to adapt flexibly to changing conditions without requiring complex manual data management processes.
Solution Approach 2:
The digital list performs self-updates based on automated analysis of query patterns and identified content deficiencies. The system autonomously determines what content to add or remove from the digital list without requiring manual intervention, thereby achieving high adaptability while keeping data management complexity low through automated self-service mechanisms.
Data Source
AI summary
Methods, systems, and non-transitory computer readable media are disclosed for utilizing machine learning models to extract digital signals from low-results web queries and generate item demand deficiency predictions for digital item lists corresponding to websites. In one or more embodiments, the deficiency identification system identifies a low-results query submitted by client devices navigating a website. The deficiency identification system generates features for the low-results query and the digital item list to generate a deficiency prediction relative to demand indicated by the low-results query. In some embodiments, the deficiency identification system utilizes a deficiency prediction model to process the extracted signals and generate a deficiency confidence score corresponding to the low-results query. Based on the deficiency confidence score, the deficiency identification systemd can generate and provide demand notifications via one or more graphical user interfaces.