Abstract: In order to achieve a music providing system capable of controlling the behavioral state of a non-human animal using music, this music providing system for a non-human animal is provided with: a state information acquisition unit for acquiring state information relating to the motion state of an animal of interest; a state estimation processing unit for estimating the current behavioral state of the animal of interest from the state information; a target state storage unit for storing information relating to a target behavioral state for the animal of interest; a sound source storage unit for storing multiple music information pieces; a music information selection unit for detecting the degree of divergence of the current behavioral state from the target behavioral state and selecting one specific music information piece on the basis of the multiple music information pieces stored in the sound source storage unit; and a music information output unit for outputting the specific music information by wireless co

Abstract: A method and apparatus for automatically generating a meeting summary is disclosed herein. Meeting audio is recorded and converted into a text-based transcript. Handwritten meeting notes are converted into notes text. The transcript and notes text are correlated to provide correlated meeting text. Meeting topics are determined from the correlated meeting text. A meeting summary is generated from the meeting topics.

Abstract: A system, method, and computer program product to accurately route a call is disclosed, by providing a resource interface having one or more interface elements mapping a resource to a call based on current calls and predicted calls, determining a dynamic handling profile from a plurality of dynamic handling profiles that include call information which maps to a resource to provide a ruleset for handling one or more types of call requests, identifying a routing rule comprising one or more routing rules of the ruleset based on caller information, receiving an activation of a resource interface element of the resource interface to initiate a call routing request to forward an assigned call to the resource, and controlling the call routing request according to the routing rule of the dynamic handling profile to perform a call function associated with the assigned call.

Abstract: A method for controlling an electronic apparatus is disclosed. The method for controlling an electronic apparatus includes detecting a voice input of a user; based on detecting the voice input of the user, performing voice recognition with respect to the voice input of the user; outputting information corresponding to a result of the voice recognition; identifying a type of ambient noise detected by the electronic apparatus; and changing an output state of the information corresponding to the result of the voice recognition based on the type of the ambient noise.

Abstract: The present disclosure provides a technical solution related to establishing a characterized chatbot with personality. On one hand, a corpus database matched with a specific character may be established, and a generated regular/conventional response messages may be converted into a characterized response messages with character's features during conversation, so that the conversation style of a chatbot may be characterized with a certain character's features. On the other hand, a chatbot may select a response message with a specific emotion by using an emotion conversation table corresponding to a personality when generating the response message against a content of a conversation input by a user, so that a chatbot may be characterized and have certain personality.

Abstract: In an audio signal processing method, a processing device obtains a current position relationship between a sound source and a listener. The processing device then obtains a current audio rendering function based on the current position relationship. When the current position relationship is different from a stored previous position relationship, the processing device adjusts an initial gain of the current audio rendering function based on the current position relationship and the previous position relationship, to obtain an adjusted gain of the current audio rendering function. The processing device then obtains an adjusted audio rendering function based on the current audio rendering function and the adjusted gain, and generates a current output signal based on a current input signal and the adjusted audio rendering function.

Abstract: Systems and methods are configured for predictive prompt generation for an interaction between a party and an automated prompt system. In various embodiments, metadata is received on the interaction and provided as input to a multi-label predictive model to generate interaction probabilities for a plurality of prompt information data objects. Each probability generated by the predictive model provides a likelihood a particular information data object needs to be provided to the first party during the interaction. Accordingly, one or more of the prompt information data objects are identified based on the probability of each piece found in the one or more prompt information data objects that satisfy a set threshold and one or more notifications are provided so that the automated prompt system provides at least one of the prompt information data objects to the first party during the interaction.

Abstract: In some embodiments, a knowledge graph generation system extracts noun-phrases from sentences of a knowledge corpora and determines the relations between the noun-phrases based on a relation classifier that is configured to predict a relation between a pair of entities without restricting the entities to a set of named entities. The knowledge graph generation system further generates a sub-graph for each of the sentences based on the noun-phrases and the determined relations. Nodes or entities of the sub-graph represent the non-phrases in the sentence and edges represent the relations between the noun-phrases connected by the respective edges. The knowledge graph generation system merges the sub-graphs to generate the knowledge graph for the knowledge corpora.

Abstract: A processor-implemented method for detecting an information operations campaign includes retrieving a first neural network language model including a natural language model trained on a first dataset. The first neural network language model is modified, via transfer learning and based on a second dataset, to produce a second neural network language model. Social media post data associated with a social media post is received, and features are extracted from the social media post data. The features are tokenized to produce at least one token including a value. A prediction score for the at least one token is generated using the trained neural network language model. If the prediction score exceeds a threshold value, a threat warning including a representation associated with at least one of the social media post or an account associated with the social media post is generated.

Abstract: Certain aspects of the present disclosure provide techniques for processing natural language utterances in a knowledge graph. An example method generally includes receiving a long-tail query comprising a natural language utterance from a user of an application. Operands and operators are extracted from the natural language utterance using a natural language model. Operands may be mapped to nodes in a knowledge graph, the nodes representing values calculated from data input into the application, and operators may be mapped to operations to be performed on data extracted from the knowledge graph. The functions associated with the operators are executed using data extracted from the nodes in the knowledge graph associated with the operands to generate a query result. The query result is returned as a response to the received long-tail query.

Abstract: Techniques for performing conversation recovery of a system/user exchange are described. In response to determining that an action responsive to a user input cannot be performed, a system may determine a topic to recommend to a user. The topic may be unrelated to the original substance of the user input. The system may have access to various data representing a context in which a user provides an input to the system. The system may use these inputs and various data at runtime to make a determination regarding whether a user should be recommended a topic, as well as what that topic should be. The system may cause a question be output to the user, with the question asking the user about the topic, for example whether the user would like a song played, whether the user would like to hear information about a particular individual (e.g., artist), whether the user would like to know about a particular skill (e.g.

Abstract: A method of training a conversational computing system. The method includes providing an initial query to a machine learning model previously trained to generate actions for responding to a query. Based on the initial query, a plurality of candidate actions are received from the machine learning model and presented via a training interface. Based on input provided by the human annotator, a selected candidate action is recognized and associated with the initial query in a labeled data example.

Abstract: Method starts with a processor receiving configuration settings including an identified task, a relationship data, and a criticality value. Processor initializes a communication session with an agent client device. The communication session is between a virtual caller associated with the system and the agent client device. Processor then processes an audio signal of the communication session to generate an agent utterance and generates a transcribed agent utterance based on the agent utterance using a speech-to-text processor. Processor generates a virtual caller utterance using a task-specific virtual caller neural network associated with the identified task. The virtual caller utterance can be generated based on the transcribed agent utterance. Processor then causes the virtual caller utterance to be played back in the communication session to the agent client device. Other embodiments are disclosed herein.

Abstract: Implementations include receiving, by a semantic translation service of a ML platform, an output data set from a ML model, the output data set including a predicted value and a set of metrics, determining, by the semantic translation service, a numerical value based on at least two or more metrics in the set of metrics, selecting, by the semantic translation service, a semantic result by mapping the numerical value to a projection including two or more semantic values, and transmitting, by the ML platform, a prediction result at least partially including the semantic result for selective display to a user using one or more user interfaces (UIs).

Abstract: A method may include obtaining an indicator that a first device is in a location of a second device and in response to obtaining the indicator, sending a redirect request to a communication service provider of the first device to direct, to the second device, incoming communication requests handled by the communication service provider that are directed to the first device. The method may further include after sending the redirect request and after a communication request to a communication session is directed to the first device, obtaining, at the second device, a communication indication to participate in the communication session. The method may further include directing audio of the communication session to a transcription system and obtaining, at the second device, the transcription of the audio from the transcription system. The method may also include presenting, by the second device, the audio and the transcription.

Abstract: A virtual assistant server receives a web request such as a HTTP request with one or more call parameters corresponding to a call redirected from an interactive voice response server. The virtual assistant server inputs the received one or more call parameters to a predictive model, which identifies, based on the one or more call parameters, an intelligent communication mode to route the redirected call to. Subsequently, the virtual assistant server routes the redirected call to the intelligent communication mode.

Abstract: Techniques are disclosed relating to natural language processing. In some embodiments, a computer system receives unlabeled content. In some embodiments, the computer system embeds, using a machine learning model, the unlabeled content, where the embedding generates an unlabeled vector. In some embodiments, the computer system determines, from a plurality of labeled vectors stored in a vector index, a first set of labeled vectors that match the unlabeled vector, where the first set of labeled vectors are generated from a set of labeled content stored in a database. In some embodiments, the computer system assigns a new label to the unlabeled content, where the new label is selected from the first set of labeled vectors. In some embodiments, the computer system stores the newly labeled content in the database. The disclosed techniques may advantageously provide for automatically labeling content based on its semantic rather than its syntactic meaning.

Abstract: Call preparation engine for customer relationship management (“CRM”) is presented. Example embodiments of the present invention include invoking an intelligence assistant to retrieve lead details, customer information, and insights for use during a call between a tele-agent and a customer; administering tele-agent call preparation notes for use during the call between the tele-agent and the customer; and displaying, through a call preparation cockpit, the lead details, customer information, insights and tele-agent call preparation notes.

Abstract: In some implementations, a device may obtain data indicating a client activity, and may determine predictive level scores corresponding to predictive level options associated with the client activity. The device may transmit, to a client device, a selected predictive level option having a highest predictive level score. The device may receive, from the client device, a client query based on a rejection of the selected predictive level option, and may determine intent level scores corresponding to intent level options associated with the client query. The device may identify a selected intent level option having a highest intent level score and may initiate client experience(s) associated with the selected intent level option. The predictive level scores and/or the intent level scores may be determined based on historical training data associated with combinations of client activities, predictive level options, client queries, intent level options, client experiences, and associated success scores.

Abstract: A system for providing active callback management using callback objects and contact information integration, utilizing a cloud callback system comprising at least a profile manager, callback manager, interaction manager, media server, and environment analyzer, allowing users to call businesses, agents in contact centers, or other users who are connected to a cloud callback system, and, failing to connect to the individual they called, allow for an automatic callback object to be created, whereby the two users may be automatically called and bridged together at a time when both users are available.