Neural Network User Experience Detection via Frequency Data Simplification
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Solution Overview
Problem
Computer-implemented environments face challenges in accurately detecting and predicting negative user experiences in real time due to limitations in processing capacity and the inability to interpret subjective user metrics, such as facial expressions and tone of voice, which are essential for identifying and mitigating user dissatisfaction.
Innovation Solution
A method utilizing convolutional neural networks to simplify user-metric frequency data expressed as spectrograms, allowing for efficient processing and prediction of negative user experiences by transforming complex data into formats that can be analyzed by predictive neural networks, thereby overcoming resource constraints and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If typical monitoring systems process user metric data to detect negative experiences, then detection capability is provided, but processing capacity and accuracy are insufficient due to inability to handle sufficient determinative data
Solution Approach 1:
The patent segments user metric data processing into distinct frequency bands (e.g., low frequency, mid frequency, high frequency components). Each frequency band is processed separately to identify different types of user experience patterns, allowing the system to handle complex data without overwhelming processing capacity while maintaining detection accuracy.
Solution Approach 2:
The patent transforms time-series user metric data into the frequency domain using spectrogram analysis. This dimensional transformation from time-based to frequency-based representation enables the system to extract meaningful patterns that are not apparent in raw time-series data, significantly improving detection accuracy without proportionally increasing processing load.
2Reliability
If complex user metric data is processed in real-time to improve detection accuracy, then prediction capability improves, but processing complexity and resource requirements increase
Solution Approach 1:
The patent extracts only the most relevant features from complex user metric data by analyzing specific frequency bands and temporal patterns. Instead of processing all raw data, the system identifies and extracts key determinative features that indicate negative experiences, reducing processing complexity while maintaining prediction reliability.
Solution Approach 2:
The patent performs preliminary data transformation and feature extraction before main analysis. User metric data is pre-processed into spectrogram representations and frequency-domain features beforehand, which simplifies subsequent prediction operations and reduces real-time processing complexity while preserving prediction accuracy.
Data Source
AI summary
A processor may monitor frequency data related to a user metric of a user during a measurement window. The user metric may relate to the user's use of a computer implemented environment. The processor may simplify the frequency data related to the user metric, resulting in a set of simplified frequency data. The processor may input the set of simplified frequency data into a neural network. The neural network may determine a likelihood of a negative user experience for the user. The processor may alter a parameter of the first user environment based on the likelihood.