Set-Top Box Ambiance Controller with ML Prediction
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Solution Overview
Problem
Current connected home technologies lack an efficient method to automatically control ambiance settings based on user-requested content, such as video programs, and integrate notifications effectively, failing to provide a seamless user experience.
Innovation Solution
A method and device that utilize machine learning algorithms to predict ambiance settings by analyzing electronic program guide data and user preferences, optimizing these predictions based on user feedback, and controlling IoT devices like lighting, thermostats, and sound systems, while also managing notifications like reminders and greetings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If machine learning algorithms are used to predict ambiance settings, then automation and personalization are improved, but device complexity and computational requirements increase
Solution Approach 1:
The system performs preliminary actions by collecting training data from multiple sources (electronic program guide data, IoT device settings, user feedback) and pre-processing it before actual use. The machine learning model is trained in advance on this curated dataset, so that when ambiance prediction is needed, the system can quickly apply the pre-trained model without heavy real-time computation, thus achieving automation while managing complexity.
2Measurement precision
If multiple data sources are integrated for training the model, then prediction accuracy is improved, but data processing complexity and time increase
Solution Approach 1:
The system performs data integration and model training in advance as a preliminary action. Training data is collected from multiple sources (electronic program guide data, IoT device settings, user feedback) and pre-processed before the actual ambiance prediction is needed. This allows the system to achieve high accuracy through comprehensive data integration while minimizing real-time processing time.
Solution Approach 2:
The machine learning model continuously improves itself by incorporating user feedback and modified settings as new training data. The system automatically re-trains and optimizes the model using this feedback, enabling self-service improvement of prediction accuracy without requiring manual intervention or extensive additional processing time.
3Measurement precision
If the model is continuously optimized using user feedback, then prediction accuracy is improved, but computational resources and processing time increase
Solution Approach 1:
The system implements self-service optimization where the machine learning model automatically incorporates user feedback and modified settings to improve itself. The set-top box monitors when users manually adjust IoT device settings and uses this feedback data to re-train and optimize the prediction model. This continuous self-improvement enhances accuracy while managing computational resources efficiently by only processing feedback data when actually provided by users.
Data Source
AI summary
Exemplary embodiments are directed to a device and method for controlling ambiance based on user-requested content. The device receives training data, user content requests, and user network device modification data. The device ascertains a content type for a user content request, performs analytics on the training data to create a model to predict ambiance settings, predicts ambiance settings for the content type using the model, evaluates the accuracy of the model and performs optimizations to the model to improve the predictions of the ambiance settings. The device controls the operating settings of one or more network devices based on the predicted ambiance settings. Moreover, exemplary embodiments are directed to controlling the enabling of display notifications, controlling reminder notifications, and controlling greeting notifications using a face identifier and notification settings.