BACKGROUND

The present invention relates generally to the field of telephony, and more particularly to anti-disturb techniques in telephony.

Anti-disturb techniques often operate to automatically deny, or block, incoming calls based on a list of phone numbers to deny. Alternatively, anti-disturb techniques are applied on a network level where nuisance callers are identified and prevented from placing calls. This technique may be referred to as a reputation-based technique where it is the reputation of the caller that triggers any anti-disturb actions.

“Spam in telephony” referred to by the acronym SPIT refers to unsolicited, automatically dialed, pre-recorded phone calls often used for marketing or phishing. Advanced detection of SPIT may rely on caller-based information such as personal relationships of the caller to the phone number being called, such as with a person associated with the phone number or certain call-related statistical characteristics of the caller.

Machine learning is the scientific study of algorithms and statistical models that computer systems use to progressively improve their performance on a specific task. Machine learning algorithms build a mathematical model of sample data, known as “training data,” for performing tasks such as predicting an outcome or making decisions without being explicitly programmed to perform the particular tasks. Machine learning algorithms are often deployed for applications that do not lend themselves to individual specific instructions to perform each task, such as filtering email messages, detecting network intruders, and supporting computer vision systems. Data mining is a field of study within machine learning having a focus on exploratory data analysis through unsupervised learning.

Data mining is the process of discovering patterns in large data sets by the application of machine learning and statistical analysis. Data mining has an overall goal of extracting information from a data set and transforming the information into a comprehensible structure, such as patterns, to make predictions.

SUMMARY

In one aspect of the present invention, a method, a computer program product, and a system includes: (i) receiving an incoming call; (ii) determining a set of characteristics of the incoming call; (iii) identifying a set of common characteristics where the set of characteristics relate to response modeling data; (iv) assigning, by the processor, a call status to the incoming call based on the common characteristics; and (v) taking a responsive action corresponding to the call status. The response modeling data includes social contacts information and online activity data.

