Methods, systems, and apparatus, including computer programs encoded on computer storage media, for announcing and detecting automated conversation are disclosed. One of the methods includes initiating, over a natural language communication channel, a conversation with a communication participant using a natural language communication method that includes a dialogue of natural language communications. The communication participant is determined to be automated using a pre-defined adaptive interactive protocol that specifies natural language linguistic transformations defined in a sequence. The conversation can be transitioned to a communication method that is different form the natural language communication method in response to determining that the communication participant is automated.

Embodiments discussed herein relate to automatic communication management, such as automated closure of a communication in circumstances where establishment of the communication is accidental, particularly from mobile terminals. A method of use of automatic communication management is disclosed. The method can include a triggering, following establishment of a communication between at least a communication terminal of the user and at least one other communication terminal, an activated automatic communication management which enables triggering of a closure of the established communication in progress as a function of the detected vocal sound level on at least one audio stream of the established communication in progress. In this way, if establishment of the communication is detected as being unintentional because of the vocal sound level of the communication, the communication will be closed.

Systems and methods for network-controlled scam/robocall handling are described. When an incoming call for a user device is received, a user may elect to add the originating number of the incoming call to a block list or a report list at the network level. Future calls from the originating number, if placed on the block list, are then blocked by the network from being received by the user device. Numbers on the report list may be moved by the user from the report list to the block list, and numbers on the block list may be moved from the block list to the report list. Also, the user may request additional information in order to determine whether to add the originating number to the block list or the report list.

A system may include a processor that may execute computer-executable instructions that cause the processor to receive caller information regarding an incoming communication from a caller and receive a request from a user to route the incoming communication to a virtual assistant application. The virtual assistant application is configured to interact with the caller and determine whether the caller is associated a fraudulent caller activity stored on databases accessible by the processor. The processor may then receive an indication from the virtual assistant application that the caller is associated with the fraudulent caller activity and forward the incoming communication to another party in response to receiving the indication.

Methods and systems for detecting an answering machine/voicemail system using a machine learning model are provided herein. In some embodiments, a method for detecting an answering machine/voicemail system using a machine learning model comprises receiving an audio stream from a telecommunication session; parsing the audio stream into a plurality of audio files; converting each of the plurality of audio files into an image; inputting each of the converted images into the machine learning model; receiving a prediction from the machine learning model; sending an indication that an answering machine/voicemail system is detected when the received prediction is a beep.

Methods and apparatus are described for a telephony server screening voice calls. In one embodiment, the telephony server receives, form an originating device, an incoming call to be routed to a receiving device. The server answers the incoming call to establish a communication link with the originating device. The server transmits, via the communication link, a challenge audio signal containing an audio message for playback by the originating device. The server receives, via the communication link, a response from the originating device, and, in response to authenticating the response, routes the incoming call to the receiving device. Other embodiments are also described and claimed.

A technique for detecting a signal tone in an audio signal is disclosed. A determination is made as to whether a peak modulation frequency in the audio signal is in a specific range or not to obtain a determination result. A measure regarding a modulation spectrum of the audio signal is calculated. The measure is calculated based on at least components of the modulation spectrum above a specific limit of modulation frequency. By using the determination result and the measure regarding the modulation spectrum, a judgement is done as to whether the audio signal contains a signal tone or not.

A system determines if a call participant of a call between the call participant and a voice response system is a human or a machine. Responsive to determining that the call participant is a human, an emotional state of the call participant is determined. Environmental information of an environment associated with the call participant is receiving. A receptiveness level of the call participant is determined based upon the emotional state and the environmental information. A message to the call participant is determined based upon the receptiveness level and one or more machine-learning models.

A call challenger can receive a user input from a called party identity to opt-in to a call challenge service, and a second user input of a keyword. When the call challenger receives a call directed to a user equipment of the called party identity, the call challenger can prompt the calling party to provide an audible response. In response to a receipt of the audible response, the call challenger can convert the audible response to a text. The call challenger can compare the text with the keyword to determine if there is a sufficient match. In response to the determining the output of the comparing does not satisfy a threshold match score, the call challenger can prevent the call from connecting with the user equipment.

An innovative method is implemented to determine a robocall and blocks the incoming communication deemed to be a robocall. The method leverages blockchain's shared storage, memory, and ability to transact all information across a network and independently verified and stored on the immutable blockchain. The method takes advantage high-speed cellular network to process each communication with high-speed. Further, the method integrates blockchain encryption, swarm intelligence (SI), artificial intelligence (AI) and machine learning (ML) algorithms, a telecommunication expert knowledge graph (TEKG), and real-time parsing of records to block robocalls and reduce connection delays. All modules can evolve and update themselves with each use of the present invention through various SI, AI, and ML technologies. Additionally, the method includes a localized call-filtering feature based on state and federal laws to ensure the blocking of calls that are prohibited by either federal or state governments thereby facilitating recovery of damages.