Abstract: Detecting, reporting and reducing unsolicited calls in a telecommunications network. Reporting an unsolicited call to a network operator of a telecommunications network. Receiving a message at a user equipment, UE, 500 of the telecommunications network. The message comprises: a unique identifier corresponding to a call to the UE 500 that has been initiated, and one or more pre-defined responses. Receiving at the UE 500 a selected a response of the one or more predefined responses indicating that the call is an unsolicited call. Sending a report to the network operator, the report comprising the unique identifier and the selected response.

Abstract: A system, article, and method of automatic context-bound domain-specific speech recognition uses general language models.

Abstract: An example apparatus for detecting intent in voiced audio includes a receiver to receive one or more word sequence hypotheses related to a voiced audio and a dynamic vocabulary. The apparatus also includes a natural language understander (NLU) to detect an intent and recognize a property related to the intent based on the word sequence hypothesis and the dynamic vocabulary. The apparatus further includes a transmitter to transmit the detected intent and recognized associated property to an application.

Abstract: An example apparatus for routing audio streams includes an audio receiver to receive audio from a microphone. The apparatus also includes a classifier to semantically generate a result set based on the audio. The apparatus further includes a scheduler to select a spoken language understanding (SLU) engine based on the result set. The apparatus includes a router to route the audio to the selected SLU engine.

Abstract: Techniques are disclosed for client-side analysis of audio samples to identify one or more characteristics associated with captured audio. The client-side analysis may then allow a user device, e.g., a smart phone, laptop computer, in-car infotainment system, and so on, to provide the one or more identified characteristics as configuration data to a voice recognition service at or shortly after connection with the same. In turn, the voice recognition service may load one or more recognition components, e.g., language models and/or application modules/engines, based on the received configuration data. Thus, latency may be reduced based on the voice recognition engine having “hints” that allow components to be loaded without necessarily having to process audio samples first. The reduction of latency may reduce processing time relative to other approaches to voice recognitions systems that exclusively perform server-side context recognition/classification.

Abstract: Techniques are disclosed for client-side analysis of audio samples to identify one or more characteristics associated with captured audio. The client-side analysis may then allow a user device, e.g., a smart phone, laptop computer, in-car infotainment system, and so on, to provide the one or more identified characteristics as configuration data to a voice recognition service at or shortly after connection with the same. In turn, the voice recognition service may load one or more recognition components, e.g., language models and/or application modules/engines, based on the received configuration data. Thus, latency may be reduced based on the voice recognition engine having “hints” that allow components to be loaded without necessarily having to process audio samples first. The reduction of latency may reduce processing time relative to other approaches to voice recognitions systems that exclusively perform server-side context recognition/classification.

Abstract: In Internet Protocol communications based on mobile station number (MSISDN) such as Rich Communication Services (RCS), the operator in a region which should be used to route services to a user device addressed by a mobile station number can be determined by recording which operator is the source of any capabilities exchange or signed presence subscription messages relating to the mobile station number which are received. The identified operator identities can then be stored in association with the respective mobile station numbers in a routing database. Messages to be sent to mobile station numbers can then be routed based on respective stored operator identities associated with the mobile station numbers in the routing database.

Abstract: An example apparatus for routing audio streams includes an audio receiver to receive audio from a microphone. The apparatus also includes a classifier to semantically generate a result set based on the audio. The apparatus further includes a scheduler to select a spoken language understanding (SLU) engine based on the result set. The apparatus includes a router to route the audio to the selected SLU engine.

Abstract: An example apparatus for detecting intent in voiced audio includes a receiver to receive one or more word sequence hypotheses related to a voiced audio and a dynamic vocabulary. The apparatus also includes a natural language understander (NLU) to detect an intent and recognize a property related to the intent based on the word sequence hypothesis and the dynamic vocabulary. The apparatus further includes a transmitter to transmit the detected intent and recognized associated property to an application.

Abstract: A data processing method performed in a group messaging server of a mobile telecommunications network, the method comprising the steps of receiving an incoming message at the group messaging server from a remote user terminal, the message being for distribution to a group of users; storing details of the incoming message in a messaging log, the details comprising a message identifier received with the incoming message; identifying at least one recipient from the group of users to which the incoming message should be transmitted; transmitting an outgoing message to each of the at least one recipient, wherein the outgoing message comprises at least part of the incoming message and the message identifier; storing details of each outgoing message in the message log, the details comprising the message identifier; and processing the message log to identify groups of messages, wherein the groups of messages are identified by having a common message identifier, and generating charging data based on the groups of mess

Abstract: A data processing method performed in a group messaging server of a mobile telecommunications network, the method comprising the steps of receiving an incoming message at the group messaging server from a remote user terminal, the message being for distribution to a group of users; storing details of the incoming message in a messaging log, the details comprising a message identifier received with the incoming message; identifying at least one recipient from the group of users to which the incoming message should be transmitted; transmitting an outgoing message to each of the at least one recipient, wherein the outgoing message comprises at least part of the incoming message and the message identifier; storing details of each outgoing message in the message log, the details comprising the message identifier; and processing the message log to identify groups of messages, wherein the groups of messages are identified by having a common message identifier, and generating charging data based on the groups of mess

Abstract: In Internet Protocol communications based on mobile station number (MSISDN) such as Rich Communication Services (RCS), the operator in a region which should be used to route services to a user device addressed by a mobile station number can be determined by recording which operator is the source of any capabilities exchange or signed presence subscription messages relating to the mobile station number which are received. The identified operator identities can then be stored in association with the respective mobile station numbers in a routing database. Messages to be sent to mobile station numbers can then be routed based on respective stored operator identities associated with the mobile station numbers in the routing database.