MULTI-FUNCTION DEVICE (MFD) SYSTEM

Smart overview of the Invention

The Multi-Function Device (MFD) system is designed to provide real-time assistance to users by automatically detecting and resolving anomalies. Equipped with sensors, the MFD gathers data to monitor its operations and user interactions. The system's processor analyzes this data to identify any irregularities and uses a trained support model to determine solutions. Users receive these solutions instantly through an intuitive interface, enhancing the efficiency and user experience of the MFD.

Field and Background

This innovation pertains to networked multi-function devices that perform various tasks like printing, scanning, and faxing. Traditionally, these devices rely on remote servers for support, which can be inefficient due to the need for human intervention and complex ticketing systems. Existing support methods often result in delayed resolutions, impacting productivity and incurring additional costs. The MFD system addresses these challenges by offering automated support that leverages historical data for quick problem-solving.

System Features

Key components of the MFD system include sensors for data collection, a processor for anomaly detection and solution identification, and a user interface for delivering real-time feedback. The processor receives monitoring data comprising sensor readings and user inputs, identifies anomalies using an anomaly detection model, and determines solutions through a trained support model. This process is designed to be swift, ensuring users receive assistance without significant delays.

Embodiments and Applications

The system supports various embodiments, such as automatically executing solutions or providing instructional visuals to users. Solutions may involve ordering supplies or adjusting device settings. The system can also escalate issues to experts if necessary. By utilizing a comprehensive database of historical anomalies and solutions, the MFD system enhances its support capabilities over time. This approach not only streamlines troubleshooting but also reduces the burden on technical support personnel.

Training and Implementation

The support model is trained using historical multimodal data, including sensor readings, ticket logs, and communication records. Training involves transforming this data into a unified format for analysis while ensuring privacy through data anonymization techniques. The model learns to classify anomalies and associate them with appropriate solutions, improving its accuracy in real-world applications. This robust training methodology underpins the system's ability to deliver effective real-time assistance.