Abstract: The present invention relates to communications methods and apparatus dynamically detecting and/or mitigating anomalies in communications systems/networks. An exemplary method embodiment includes the steps of: (i) storing a set of N key performance indicator (KPI) models; (ii) associating each of a plurality of recurring time slots of a recurring time frame on a per group member basis with one of the N KPI models wherein the associating including associating a first group member of a first group and a first recurring time slot with a first one of the N models, the first one of the N models being a first model; (iii) receiving event data for the first group member for a first time period; and (iv) determining based on the first model if a key performance indicator value for the first group member and first time period is anomalous.

Abstract: Systems and methods for providing call verification to prevent voice phishing, comprising: receiving a first API call, wherein the first API call is a call request from a service provider to establish a call with a client device associated with a client of the service provider; verifying an identity of the service provider, wherein verifying the identity of the service provider comprises: generating a second API call; transmitting the second API to a verification system; and receiving verification of the service provider from the verification system; transmitting a notification to the client device that includes an indication that an incoming call is from a verified service provider; verifying the identity of the client; and establishing the call between the service provider and the client after both the identity of the service provider and the identity of the client are verified.

Abstract: A method that detects unwanted calls based on information shared between a first telecommunications network provided by a first service provider and a second service provider providing a second telecommunications network including receiving, by a data-processing system, a request to provide a telecommunications service to the first telecommunications network, the request being received by the first telecommunications network from a user of the first telecommunications network through a first gateway, and wherein the request comprises a calling party identification that identified a calling party and a called party identification that identified a called party.

Abstract: An example method of assigning a telephone number to a user account in a communications system is provided. The method includes obtaining a mnemonic seed and obtaining a subset of telephone numbers filtered from a global telephone number pool based on the mnemonic seed. The alphanumeric representation of each telephone number in the subset is within a threshold Hamming distance of the mnemonic seed. The method further includes receiving an indication of a selected telephone number from the subset and assigning the selected telephone number to the user account.

Abstract: A system comprising a call center, contact system test engine, plurality of networks, at least one remote server, at least one remote database, which allows users to automate call center testing and combine synchronized results for agent-customer calls (AX-CX test synchronization). The system is further accomplished with methods, using different techniques for synchronizing the testing for both customer and agent results, and producing these test results in a mobile dashboard and web-accessible dashboard.

Abstract: A method may include obtaining first audio data of a first communication session between a first and second device and during the first communication session, obtaining a first text string that is a transcription of the first audio data and training a model of an automatic speech recognition system using the first text string and the first audio data. The method may further include in response to completion of the training, deleting the first audio data and the first text string and after deleting the first audio data and the first text string, obtaining second audio data of a second communication session between a third and fourth device and during the second communication session obtaining a second text string that is a transcription of the second audio data and further training the model of the automatic speech recognition system using the second text string and the second audio data.

Abstract: Techniques for performing entity resolution as part of natural language understanding processing are described. During offline operations, a system may convert text (representing entities known to the system) into audio of various languages. The languages into which the text is converted may depend on the location where the entity is likely to be spoken by users of the system. At runtime, the system processes a user input using text-based entity resolution. If text-based entity resolution fails, the system may identify user speech corresponding to an entity to be resolved, and attempt to phonetically match the user speech to the audio of the known entities. Results of the phonetic entity resolution may then used by downstream components, such as skills.

Abstract: An electronic apparatus is disclosed. The electronic apparatus includes a communicator; and a processor configured to select a plurality of different words respectively corresponding to a plurality of characters based on occurrence of a password input event, and to transmit the plurality of selected words to a terminal device through the communicator, and based on a user voice including at least one of the plurality of selected words being received, perform password authentication based on a character corresponding to the at least one of the plurality of selected words.

Abstract: Embodiments for managing voice commands by one or more processors are described. The receiving of a voice command from an individual is detected. An action associated with the voice command is caused to be at least temporarily prevented from being executed based on at least one data source associated with the individual.

Abstract: A conversation may be monitored in real time using a trained machine learning model. This real-time monitoring may detect attributes of a conversation, such as a conversation type, a state of a conversation, as well as other attributes that help specify a context of a conversation. Contextually appropriate behavioral targets may be provided by machine learning model to an agent participating in a conversation. In some embodiments, these “behavioral targets” are identified by applying a set of rules to the contemporaneously identified conversation attributes. The behavioral targets may be defined in advance prior to the start of a conversation. In this way, the machine learning model may be trained to associate particular behavioral target(s) with one or more conversation attributes (or collections of attributes). This facilitates the real-time monitoring of a conversation and contemporaneous guidance of an agent with machine-identified behavioral targets.

Abstract: A conversation may be monitored in real time using a trained machine learning model. This real-time monitoring may detect attributes of a conversation, such as a conversation type, a state of a conversation, as well as other attributes that help specify a context of a conversation. Contextually appropriate behavioral targets may be provided by machine learning model to an agent participating in a conversation. In some embodiments, these “behavioral targets” are identified by applying a set of rules to the contemporaneously identified conversation attributes. The behavioral targets may be defined in advance prior to the start of a conversation. In this way, the machine learning model may be trained to associate particular behavioral target(s) with one or more conversation attributes (or collections of attributes). This facilitates the real-time monitoring of a conversation and contemporaneous guidance of an agent with machine-identified behavioral targets.

Abstract: Systems, methods, and computer program products are provided for selecting and initiating playing of audio conversations on mobile computing devices. For example, a method comprises determining a first user accesses a mobile application on a mobile device associated with the first user; selecting an audio conversation for the first user, wherein the audio conversation involves at least a second user, wherein the audio conversation is selected for the first user based on at least one of first user information associated with the first user, second user information associated with the second user, or conversation information associated with the audio conversation; and initiating playing of the audio conversation on the mobile application on the mobile device.

Abstract: While a teleconference is occurring, data of a teleconference is analyzed to determine first participant data associated with a first speaker and second participant data associated with a second speaker. At a different application, addition of a first speaker indicator and a second speaker indicator to a text entry of a user is caused, the first speaker indicator added concurrently with identification that the first speaker is speaking and the second speaker indicator added concurrently with identification that the second speaker is speaking. At the different application, addition of key information to a text entry of a user is caused, the key information comprising participant data associated with the first speaker and second participant data associated with the second speaker.

Abstract: The present application includes a method and system for multi-channel interaction. A communication session is initiated between a customer service representative (CSR) and an end user. Multi-channel communication is used between the end user and the CSR. The multi-channel communication includes at least voice and data. Information is presented to the end user via a user interface, and the user can confirm the accuracy of the information using the user interface.

Abstract: Methods for optimizing the routing of customer communications include receiving a customer communication; identifying a customer associated with the customer communication; accessing a profile of the identified customer to determine customer data; receiving normalized customer metric scores for a plurality of customer metrics; identifying available agents; accessing a profile of each available agent to determine agent data; predicting interaction outcome metric values for a plurality of customer metrics based on the customer data and the agent data; normalizing the predicted interaction outcome metric values; calculating, in real-time, an aggregate agent-customer pairing score for each available agent; selecting a responding agent from the available agents with the highest aggregate agent-customer pairing score; and providing a routing recommendation to a communication distributor to route the customer communication to the responding agent with the highest aggregate agent-customer pairing score.

Abstract: Vehicle systems and methods are provided for capturing audio during operation for subsequent presentation and analysis. One method involves obtaining a plurality of audio segments via an audio input device onboard an aircraft, classifying each audio segment of the plurality of audio segments into one or more of a plurality of topic categories based at least in part on the content of the respective audio segment, and providing a graphical user interface (GUI) display depicting the plurality of audio segments in a time-ordered sequence. The GUI display includes GUI elements for selectively removing subsets of audio segments classified into particular topic categories from the time-ordered sequence.

Abstract: Embodiments are provided for intelligent communication message completion in a computing system by a processor. A plurality of contextual factors associated with a communication dialog may be determined and learned. A communication message may be automatically completed according to the plurality of contextual factors associated with the communication dialog.

Abstract: Methods and systems are provided for the AI-based computer-aided persuasion system (CAPS). The CAPS obtains inputs from both the target and the agent for an object, dynamically generates persuasion references based on analysis of the input. The CAPS obtains content output by analyzing the agent audio stream and the target audio stream using a recurrent network (RNN) model, obtains sentiment classifiers based on a convolutional neural network (CNN) model, updates a conversation matrix, and generates a persuasion reference based on the updated conversation matrix. The persuasion reference is based on an acceptance likelihood result generated from the conversation matrix using the RNN model. The CAPS further generates a target profile using CNN with input of target Big Data, wherein the target profile includes one or more objects, and wherein the agent is selected based on the generated profile and one or more selected objects.

Abstract: A method, a device, a system for analyzing comment data about a target are provided. The method includes acquiring the comment data about the target in accordance with a link to the target; and analyzing the comment data and determining a comment topic and a comment viewpoint about the target.

Abstract: Conversation support systems and methods are operable to assist a user to more fully participate in an ongoing conversation. An exemplary embodiment receives a conversation map comprising a serially arranged plurality of conversation segments of audio information each associated with one of a plurality of conversation participants, and that includes dialogue of the ongoing conversation being spoken by the associated conversation participant; determining subject matter of a current portion of the ongoing conversation based on the dialogue; selects conversation support information that pertains to the current conversation subject; and generating a conversation queue that includes information that corresponds to the selected conversation support information, wherein the conversation queue is communicated from the conversational support system to at least one conversation queue output device that presents the conversation support information to the user.