Context-Aware Caller ID Using ML Completion Time Prediction
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Solution Overview
Problem
Existing technologies face challenges in determining and integrating contextual data across multiple parties involved in transactions, leading to inefficiencies and disconnection in communication and data processing.
Innovation Solution
A computer-based system utilizing machine learning techniques to curate and integrate user-specific entity activity data for contextual displaying, including training a model to predict activity completion times and enhance caller ID information with contextual augmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models and contextual data integration are implemented, then measurement precision and reliability of transaction data are improved, but device complexity and difficulty of detecting and measuring increase
Solution Approach 1:
The patent introduces a server as an intermediary component that hosts the machine learning model and mediates between client devices and the complex predictive analytics system. The server receives activity data from clients, processes it through the ML model, and returns completion time predictions, thereby shielding clients from the complexity of the ML infrastructure while maintaining high measurement precision
Solution Approach 2:
The system is segmented into distinct components: client devices that collect and send activity data, a server that hosts the ML model and performs predictions, and the ML model itself that generates completion time estimates. This segmentation allows each component to be optimized independently, reducing overall system complexity while maintaining prediction precision
2Reliability
If contextual information from multiple parties is integrated, then reliability and accuracy of transaction data are improved, but loss of time and device complexity increase
Solution Approach 1:
The system performs preliminary actions by continuously collecting and pre-processing activity data from multiple parties before transactions occur. The ML model is pre-trained on historical data, enabling it to rapidly generate completion time predictions during actual transactions without requiring time-consuming data integration at the moment of need
Solution Approach 2:
The machine learning model operates autonomously to integrate and analyze contextual data from multiple parties without requiring manual intervention. The system self-manages the complex task of reconciling and synthesizing disparate data sources, reducing both processing time and operational complexity
Data Source
AI summary
Systems and methods of context-aware caller identification via machine learning techniques are disclosed. In one embodiment, an exemplary computer-implemented method may comprise: obtaining a trained activity completion time estimation machine learning model that determines activity completion time prediction data for an activity of an entity; receiving, from a first computing device of a user, current entity-specific device-executed user activity data of a current entity-specific user activity associated with a user and an entity; receiving from a second computing device associated with the particular entity, current user-specific entity activity data associated with a current user-specific entity activity, related to the current entity-specific user activity; utilizing the trained activity completion time prediction machine learning model to determine current user-specific entity activity completion time prediction data for the current user-specific entity activity; and determining a current displaying context to notify the user of the current user-specific entity activity completion time prediction.