Machine Learning Prediction System for Software Object Optimization
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Solution Overview
Problem
Generating software objects that cater to multiple features and competitive objectives between users and entities is technologically challenging due to the complexity of user profiles and historical data compatibility.
Innovation Solution
A computer-based prediction system utilizing a categorization machine learning model and an optimization machine learning model to predict and optimize software objects based on user profiles, demographics, and historical data, balancing competitive interests between users and entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple features and competitive objectives are considered in software object generation, then user acceptance and compatibility improve, but system complexity and difficulty of generation increase
Solution Approach 1:
The patent segments the software object generation process into distinct machine learning models: a categorization model for predicting user profile aspects and an optimization model for generating software objects. This segmentation allows each model to focus on specific tasks, reducing overall system complexity while maintaining high adaptability through specialized processing for different user needs and competitive objectives.
Solution Approach 2:
The patent introduces machine learning models as intermediary components between user profile data and software object generation. These models act as mediators that process complex user data, predict relevant aspects, and transform them into optimized software objects, thereby managing the complexity of multi-feature generation while improving compatibility and user acceptance.
2Adaptability or versatility
If user profiles and historical data are processed to predict software objects, then customization and optimization improve, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by using the categorization model to predict user profile aspects before the main software object generation process. This preliminary prediction of relevant user characteristics allows the optimization model to focus computational resources only on generating objects for the predicted aspects, significantly reducing processing time while maintaining high customization levels.
Solution Approach 2:
The patent applies partial action by processing only the most relevant aspects of user profiles and historical data that the categorization model identifies as important. Rather than processing all available data uniformly, the system selectively processes partial data relevant to predicted user needs, reducing computational time while maintaining adequate customization quality.
Data Source
AI summary
At least some embodiments are directed to a prediction system of software objects. The prediction system predicts a first aspect of a user profile utilizing a categorization machine learning model and a user activity profile, the user profile and the user activity profile are associated with a user. The user activity profile comprises a plurality of values associated with demographics and historical activity data of the user. The prediction system predicts a software object associated with the user profile utilizing an optimization machine learning model, the first aspect of the user profile, and a second aspect of the user profile. The software object is optimized with respect to at least one competitive interest between the user associated with the user profile and an entity associated with the software object. The prediction system outputs the software object to a client computing device of the user.