Spam Call Classification via Social Network Graph Aggregation
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Solution Overview
Problem
Existing telephone systems lack effective mechanisms for identifying and managing spam calls, leading to user frustration and wasted time in dealing with unwanted communications.
Innovation Solution
A computer-implemented method and system that classifies incoming calls as spam based on user-submitted classifications, using a server system to identify and flag calls from numbers marked as spam by other users, allowing users to interact with spam calls differently, such as blocking or playing pre-recorded messages, and providing visual or auditory alerts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a server system collects and processes spam classifications from multiple users to identify spam calls, then the accuracy of spam detection is improved, but the system complexity and processing time increase
Solution Approach 1:
The system divides the spam detection task into segments: individual users classify calls they receive, the server aggregates these classifications, and the processed results are distributed back to users. This segmentation allows the system to leverage collective user input without requiring any single user to perform complex analysis, thereby improving detection accuracy while keeping individual device complexity low.
Solution Approach 2:
The server system acts as an intermediary that collects, processes, and distributes spam classifications. It mediates between individual users' local classifications and the global spam detection need, aggregating data from multiple users and returning processed results. This intermediary approach enables accurate spam detection through collective intelligence while abstracting the complexity from individual user devices.
2Adaptability or versatility
If the system provides multiple interaction options for spam calls (block, pre-recorded message, etc.), then user control and experience are improved, but the device complexity increases
Solution Approach 1:
The system dynamically adjusts the interaction options presented to users based on the spam classification results. When a call is identified as spam, the system automatically enables specialized interaction modes (block, pre-recorded message) that are not available for normal calls. This dynamic adaptation provides users with versatile control options only when needed, avoiding the complexity of always-presenting all options for every call type.
3Reliability
If the system processes and stores spam classifications from a large number of users, then the reliability of spam identification is improved, but the data storage and processing requirements increase
Solution Approach 1:
The system merges spam classification data from multiple users into a centralized database on the server. By combining individual user classifications into a collective dataset, the system achieves reliable spam identification through aggregated evidence. This merging approach allows the system to leverage the reliability benefits of multiple user inputs while centralizing storage requirements on the server rather than requiring each user device to store extensive classification data.
Data Source
AI summary
In general, the subject matter described in this specification can be embodied in methods, systems, and program products for identifying telephone spam. An indication of an incoming telephone call and a calling device telephone number for the incoming telephone call is received. An indication that the calling device telephone number has been determined to be a source of telephone spam based on identifications by one or more users, other than a user of a mobile telephone, that the calling device telephone number is a source of telephone spam is received. A secondary alert is output to the user of the mobile telephone that is different than a default alert. The default alert is output to the user of the mobile telephone if the calling device telephone number is not determined to be a source of telephone spam.