Search Accuracy via ML Weighting in Print Support Systems
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Solution Overview
Problem
Current data storage facility search systems rely on human-provided content ratings, which are subjective and lack context, leading to inaccurate search results due to biases and unclear weighting of ratings.
Innovation Solution
A distributed computing system that utilizes machine learning methodologies to improve search results by generating weight values for solution factors based on correlation with input data and search terms, updating a solution index to prioritize accurate solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If human-provided content ratings are used to rank search results, then user feedback is incorporated into the search system, but search accuracy deteriorates due to subjectivity and bias in human ratings
Solution Approach 1:
The patent replaces the mechanical system of human rating provision with an automated machine learning system that analyzes session records, exit statuses, and solution effectiveness objectively. This substitution eliminates human subjectivity and bias while maintaining the ability to incorporate user feedback through automated analysis of actual solution outcomes.
Solution Approach 2:
The patent introduces an intermediary machine learning model that acts as a mediator between user interactions and search result ranking. Instead of directly using human ratings, the system uses this intermediary to process session records and exit statuses, transforming raw user behavior data into objective effectiveness scores that drive ranking decisions.
2Ease of operation
If simple indicator methods are used for user content ratings, then ease of operation is improved, but measurement precision deteriorates due to lack of context and subjective bias
Solution Approach 1:
The patent implements a self-service system where the machine learning model automatically analyzes session records and exit statuses without requiring users to manually provide ratings. The system serves itself by extracting effectiveness signals from natural user behavior and solution outcomes, eliminating the need for users to engage in rating activities while maintaining high measurement precision.
Solution Approach 2:
The patent establishes a feedback loop where exit statuses and session outcomes automatically feed into the machine learning model, which then updates solution effectiveness scores. This continuous feedback mechanism replaces manual rating indicators with automated behavioral feedback, maintaining ease of operation while dramatically improving measurement precision through objective outcome analysis.
3Measurement precision
If machine learning methodologies are implemented to generate objective weight values, then search accuracy is improved, but device complexity increases
Solution Approach 1:
The patent makes the machine learning model multi-functional by having it perform multiple tasks: analyzing session records, determining solution effectiveness, generating weight values, and updating search rankings. This universal approach consolidates what could be multiple separate complex systems into a single model, improving search accuracy while limiting the increase in overall system complexity.
Solution Approach 2:
The patent changes key parameters of the search system by transitioning from static human-provided ratings to dynamic machine-generated weight values. The machine learning model adjusts these parameters (effectiveness scores and weights) based on analyzed data, enabling adaptive search accuracy improvement without requiring fundamentally complex system architecture changes.
Data Source
AI summary
A distributed computing system for managing solutions for print device support requests includes a solution data store and a solution processing system. The system receives a support request via the user interface that includes input data and search terms that pertain to an issue with a print device. The system uses the input data and the search terms to generate a search query, and queries the solution data store using the search query to identify a list of possible solutions for the support request. The system receives a selection of the possible solutions. For each selection, the system generates a session record for the support request, correlates the possible solution associated with the selection with the input data and search terms, generates factors based on the correlation, and uses the generated factors to update fields of a solution index in the data store for the possible solution.