Spam Caller Identification via Analytical Engine
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Solution Overview
Problem
Mobile phone users face disruptions, potential fraud, and wasted time and money due to spam calls, which existing technologies have difficulty in effectively identifying and blocking.
Innovation Solution
A system comprising an analytical engine, content delivery network, and spammer API that analyzes call data and metadata to identify potential spam callers, using machine learning algorithms and external data sources to classify numbers and provide a spammer score, with a call blocker component to prevent unwanted calls.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional spam call blocking methods are used, then some spam calls are blocked, but they cannot effectively identify and block all spam callers due to evolving spammer behaviors
Solution Approach 1:
The system dynamically adapts to changing spammer behaviors by continuously collecting call data, updating machine learning models, and refreshing the spammer score database. The analytical engine processes new call data periodically to identify emerging spam patterns and update classification algorithms, ensuring the system remains effective against evolving spam tactics.
Solution Approach 2:
The system implements feedback mechanisms by analyzing call outcomes and user interactions to continuously improve spam detection accuracy. Call data including answered calls, missed calls, and user-reported spam is fed back into the machine learning models to refine spammer identification and update the spammer score database for future blocking decisions.
2Measurement precision
If machine learning algorithms analyze call data to identify spam callers, then spam detection accuracy improves, but system complexity increases
Solution Approach 1:
The system introduces an analytical engine as an intermediary component that handles the complex machine learning operations separately from the core telephony system. This engine collects call data, processes it through machine learning algorithms, and outputs spammer scores that are then used by the call blocking component, isolating complexity to a dedicated module.
Solution Approach 2:
The system segments the spam detection functionality into distinct components: call data collection, machine learning analysis, spammer score database, and call blocking. Each component performs a specific function, allowing the complex machine learning operations to be isolated and managed separately from the simpler call routing and blocking functions.
3Loss of time
If the system blocks calls based on spammer scores, then user time and money are saved, but air time and network resources are consumed by data collection and analysis
Solution Approach 1:
The system performs preliminary analysis by pre-calculating spammer scores for phone numbers before actual calls are made. The analytical engine continuously analyzes call data and updates the spammer score database in advance, so that when a call attempt occurs, the blocking decision can be made immediately based on pre-computed scores rather than analyzing data in real-time.
Data Source
AI summary
Phone numbers can be identified as being associated with spam callers. Data associated with previously identified spammer phone numbers is analyzed to obtain one or more input parameters for a classification engine. The classification engine uses the input parameters to identify one or more phone numbers in data associated with currently active phone numbers as being associated with spam callers. The one or more phone numbers identified as being associated with spam callers can be provided to call blockers of end-user telephone devices.