Training Program Benchmarking System Using Anonymized Activity Data
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Solution Overview
Problem
Companies face challenges in evaluating and improving their training programs due to a lack of actionable insights, absence of standardized data structures, and inadequate systems for benchmarking performance, which affects customer satisfaction and financial performance.
Innovation Solution
A computer-implemented system for flexibly benchmarking training programs using user-defined criteria, generating quantitative benchmark scores, and providing insights to training program managers and curriculum developers, based on anonymized user activity data and machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If training programs are implemented to improve customer satisfaction and financial performance, then business effectiveness is improved, but the complexity of managing and evaluating training programs increases
Solution Approach 1:
The patent introduces a benchmarking system as an intermediary between training programs and business outcomes. This system collects data from multiple sources (LMS, CRM, finance systems), processes it through standardized frameworks, and generates actionable insights without requiring direct manual analysis by training managers, thus reducing management complexity while maintaining effectiveness evaluation capability
Solution Approach 2:
The patent replaces manual, ad-hoc evaluation methods with an automated computer-implemented system that uses machine learning algorithms and predefined benchmarks. The system automatically compares training metrics against industry standards and generates recommendations, substituting complex manual analysis with computational processes that are more scalable and less error-prone
2Measurement precision
If detailed benchmarking data is collected to evaluate training program effectiveness, then measurement precision is improved, but data privacy and security risks increase
Solution Approach 1:
The patent extracts and anonymizes personally identifiable information from the collected data before analysis. The system works with aggregated, de-identified data points that maintain statistical validity for benchmarking while removing individual identifiers, thus achieving precise measurement without compromising individual privacy or increasing security risks from sensitive data storage
Solution Approach 2:
The patent introduces data anonymization and aggregation as intermediary steps between data collection and analysis. These intermediate processing layers transform raw sensitive data into sanitized, aggregate statistics that can be analyzed for insights while preventing re-identification of individuals, thus mediating between the need for detailed measurement and privacy protection
3Adaptability or versatility
If standardized benchmarking frameworks are implemented to enable comparison with industry standards, then adaptability is improved, but the complexity of data structure standardization increases
Solution Approach 1:
The patent creates a universal benchmarking framework that can accommodate multiple training program types, industries, and organizational sizes through a single standardized data collection system. The framework uses configurable parameters and modular components that can be adapted to different contexts without requiring separate evaluation systems, thus achieving versatility while maintaining structural simplicity through standardization
Data Source
AI summary
Training programs (e.g., for training a user to use a company's products) may be flexibly benchmarked by first identifying user interactions with one or more of the training programs and converting the user interactions into user activity data objects associated with user identifiers. The user activity data may then be made anonymous by removing the user identifiers from the data before examining the data. The anonymized user activity data may then be aggregated with respect to each of the training programs. A benchmark model for evaluating the training programs may then be determined based on a flexible benchmark schema of selectable benchmark metrics for evaluating aspects of the training programs. Benchmarks may then be calculated for each of the training programs based on the aggregated user activity data and the benchmark model. The benchmarks may then be displayed and/or analyzed to generate insights or suggestions for improving the training programs.