Simulated User Absorption Data for Sparse-Data Content Recommendations
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Solution Overview
Problem
Conventional content recommendation systems struggle to provide accurate, real-time content recommendations to individual users with limited data, and health management systems lack timely and effective prompts for encouraging positive lifestyle choices.
Innovation Solution
A computer-implemented process using machine learning and heuristic systems to generate simulated user absorption data, enabling situationally targeted content recommendations and health management directives, such as prompts and rewards, based on user profiles and real-time data capture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional content recommendation systems use large datasets from many users to generate recommendations, then recommendation accuracy is improved, but the system cannot provide accurate recommendations for individual users with limited data
Solution Approach 1:
The patent creates simulated user absorption data that copies the characteristics and patterns of real user absorption data. This simulated data is generated to mirror the statistical properties and behavioral patterns observed in real user data, allowing the system to train recommendation models as if they had access to large datasets, even when individual users provide limited real data.
Solution Approach 2:
The system performs preliminary actions by generating and storing simulated user absorption data in advance. This simulated data is prepared beforehand to supplement the limited real data available for individual users, enabling the recommendation system to function accurately from the start rather than requiring extensive data collection over time.
2Reliability
If health management systems use retrospective reporting and oversight to monitor user behavior, then user compliance can be incentivized, but the system fails to provide timely support at decision points and allows inaccurate reporting
Solution Approach 1:
The patent replaces the mechanical system of retrospective self-reporting with an automated electronic data capture system using mobile device sensors. This substitution eliminates the need for manual user input at decision points, automatically capturing real-time behavioral data such as location, motion, and contextual information to provide timely and accurate monitoring without relying on user honesty or memory.
Solution Approach 2:
The system introduces mobile device sensors and automated tracking mechanisms as intermediaries between the user and the health management system. These intermediaries continuously capture and transmit data about user behavior and context, enabling the system to provide timely, situationally-targeted support without requiring direct user input at critical decision moments.
3Productivity
If systems provide situationally targeted content recommendations in real-time, then user engagement is improved, but the system requires sophisticated data processing and analysis capabilities
Solution Approach 1:
The patent changes the parameters of the data by generating simulated user absorption data with specific statistical characteristics and patterns that mirror real user behavior. This transformation allows the system to work with pre-processed, standardized data structures that reduce the computational complexity required for real-time analysis and recommendation generation.
Data Source
AI summary
The present disclosure relates to a computer-implemented process for evaluating user activity, user preference, and/or user habit via one or more personal devices and providing precisely timed and situationally targeted content recommendations. It is an object of the present disclosure to provide a technological solution to the long felt need in small scale content recommendation systems caused by the technical problem of generating situationally targeted and user preference targeted content recommendations for users of an interactive electronic system.