Expert Profile Attribute Selection via Machine Learning Prediction
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Solution Overview
Problem
Traditional data management systems are unable to effectively assist experts in improving their selection rates, leading to income loss for experts, missed opportunities for users, and inefficient use of computing resources.
Innovation Solution
A method and system that utilize machine learning processes to analyze expert profile data and predict the impact of profile changes on selection rates, providing recommended adjustments to enhance visibility and selection by users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data management systems are used to manage expert profiles, then the system structure remains simple, but the selection rate of experts by users remains low and cannot be improved
Solution Approach 1:
The patent introduces an analysis model as an intermediary component between the expert profile data and the selection rate optimization. This model trained with machine learning processes acts as a mediator that analyzes profile attributes and predicts selection rates, enabling the system to improve expert selection without fundamentally restructuring the entire data management system.
Solution Approach 2:
The system enables expert profiles to self-optimize by automatically analyzing their own attributes through the analysis model and generating recommendations for improvement. The machine learning process allows the system to autonomously identify which profile changes will increase selection rates, reducing the need for manual intervention and complex external management.
2Productivity
If experts manually optimize their profiles without assistance, then the system resources remain efficiently used, but experts spend excessive time trying to improve their profiles with no guaranteed results
Solution Approach 1:
The patent implements a feedback mechanism where the analysis model provides experts with specific recommendations on how to modify their profiles based on predicted impacts on selection rates. The system analyzes current profile attributes, compares them against successful patterns learned through machine training, and feeds back actionable insights to experts, enabling rapid and effective profile optimization without trial-and-error spending of time.
Solution Approach 2:
The system performs preliminary analysis of expert profiles using the trained analysis model before experts invest significant time in optimization. By pre-analyzing profile attributes and predicting which changes will most effectively increase selection rates, the system allows experts to make informed decisions upfront, avoiding wasted time on ineffective modifications.
3Measurement precision
If users spend more time searching for suitable experts, then they can find better matches, but the overall system efficiency and resource utilization decrease
Solution Approach 1:
The patent applies preliminary action by pre-optimizing expert profiles using the machine learning-trained analysis model before users conduct their searches. The system proactively identifies and implements profile attribute improvements that will make experts more discoverable and attractive to users, thereby reducing the time users need to spend searching while maintaining or improving match quality.
Solution Approach 2:
The system utilizes parameter changes by modifying expert profile attributes based on predictions from the analysis model. The machine learning process identifies which specific profile parameters (such as expertise areas, experience levels, or service descriptions) should be changed to maximize visibility and selection rates, enabling faster and more accurate expert discovery without requiring users to spend extensive time searching.
4Measurement precision
If the analysis model is continuously retrained to improve prediction accuracy, then the selection rate predictions become more accurate, but the computing resources and processing time increase
Solution Approach 1:
The patent implements periodic action by retraining the analysis model at scheduled intervals rather than continuously. The machine learning process is triggered periodically to update the model with new data and improve prediction accuracy, while allowing the system to operate with the existing model in between training cycles. This approach maintains accurate predictions while avoiding the excessive computing resource consumption that would result from continuous retraining.
Data Source
AI summary
Methods and systems for assisting entities with improving the effectiveness of their profiles are disclosed. An example method is performed by one or more processors of a system and includes storing profile data including profiles identifying attributes associated with respective entities, obtaining a selection data vector including values each indicating a selection rate for a respective entity, generating, using a trained analysis model, selection prediction data predicting, for each respective change of a set of possible changes to a selected entity's profile, how the selection rate for the selected entity will change if the selected entity's profile is adjusted in accordance with the respective change, selecting, from the selection prediction data, one or more recommended changes likely to result in an increase in the selection rate for the selected entity, and outputting a prompt recommending that the selected entity make one or more recommended changes to the selected entity's profile.