Abstract: Aspects of the present disclosure relate to systems and methods for detecting emerging events. In various examples, a method for detecting emerging events includes obtaining communication data associated with communication between multiple sources, segmenting communication data into multiple segments, determining whether a data segment belongs to a familiar topic or none, and generating a notification when a familiar topic is mentioned for more or less than a mention prediction. Additionally, or alternatively, a notification may be generated when an unfamiliar topic emerges from a set of unfamiliar data segments if an associated segment count exceeds a critical mass threshold. To determine whether a data segment belongs to a familiar topic, the data segment may be transformed into a feature vector and mapped onto a feature space, where a distance-based similarity score may be determined.

Abstract: Systems and methods are provided for cataloging and retrieving data. The systems access data stored at a data storage and determine its data type. When a data type is unknown to the systems, the systems generate configuration data for data ingress and validate the data in a test environment. Once the data ingress succeeds in the testing environment, the systems transform data format to a known format, itemize parts of data for cataloging, extract the parts of data, and generates metadata associated with the data. The systems store both the metadata and the extracted data a final data store. A local data server with a web server includes a database of metadata for locally determining locations of data needed to generate a response to a data query. Metadata includes labels associated with itemized data stored in the final data store.

Abstract: Disclosed herein are computer-implemented devices, systems, and methods of sanitizing a transcript. In an example, such a method includes selecting a transcript to be sanitized, identifying potential redactions to be made in the transcript, and redacting the transcript at the potential redactions to sanitize the transcript. The potential redactions are identifiable via a multi-pass process that includes: generating initial redactions to be made based on surrounding context within the transcript; generating matching redactions to be made based on the initial redactions, and generating character redactions to be made based on at least one of the initial redactions and the matching redactions.

Abstract: Systems and methods are provided for generating quality scores associated with a contact (e.g., a telephonic call including an agent) and with agents. In particular, the disclosed technology determines types of frames of content of the contact into a speech and/or a noise, the noise further classified into a standard noise and a non-standard noise. A frame type determiner determines a type of a frame based on a waveform analysis and/or use of speech and noise models that are trained through machine learning. The standard noise includes noise that is expected and consistent across contacts and agents (e.g., a hold music). The non-standard noise includes a noise that is unexpected in occasion and audio sources (e.g., a barking dog, a siren from street, and the like). The disclosed technology enables assessing contacts and agents based on issues associated with remote working environment that vary among agents.

Abstract: Aspects of the present disclosure relate to systems and methods for detecting emerging events. In various examples, a method for detecting emerging events includes obtaining communication data associated with communication between multiple sources, segmenting communication data into multiple segments, determining whether a data segment belongs to a familiar topic or none, and generating a notification when a familiar topic is mentioned for more or less than a mention prediction. Additionally, or alternatively, a notification may be generated when an unfamiliar topic emerges from a set of unfamiliar data segments if an associated segment count exceeds a critical mass threshold. To determine whether a data segment belongs to a familiar topic, the data segment may be transformed into a feature vector and mapped onto a feature space, where a distance-based similarity score may be determined.

Abstract: Systems and methods are provided for determining a rapport score for a contact. Data associated with a contact may include an audio recording, a transcript, metadata, and/or other contact data collected during or generated after a contact. One or more rapport models are applied to the contact data to generate rapport metrics that capture one aspect of the rapport during a contact. Rapport metrics can be compared to target rapport metrics to determine whether the rapport metric indicates positive rapport during the contact. From rapport metrics, a rapport score can be generated that indicates the overall rapport for the contact. Rapport metrics, rapport scores, and other information associated with a contact can be provided in a manner that allows for useful evaluation of whether contact participants developed positive rapport during a contact and/or a series of contacts over time.

Abstract: The present disclosure relates to generating additional insight data for search results. Individual search results may be obtained and used to generate a parse tree for the search result data. A dependency graph may then be generated using the parse tree, where the dependency graph includes a root node that corresponds to a query parameter. A scoring or weighting algorithm is applied to the dependency graph to generate and aggregate scores for nodes in the dependency graph relative to the root node. The generated scores or weights are used to generate insight data for the search results.

Abstract: Systems and methods are provided for generating call sentiment associated with a call. The call includes one or more utterances. An utterance includes one or more sentences. A sentence includes one or more words. The disclosed technology iteratively generates sentiment values associated with sentences based on sentiment associated with words in the sentences, sentiment values associated with utterances based on sentence sentiment, and the call sentiment. Determining sentiment includes use of one or more a trained neural network for predicting sentiment and weighted average of sentiment values associated sentences and utterances for aggregating sentiment values. The disclosed technology generates a sentiment momentum that trends sentiment that evolves over time during the call. A speaker sentiment indicates sentiment associated with a speaker who makes utterance during the call.

Abstract: Systems and methods are provided for determining calls and contacts associated with calls that are contextually similar based on content of the calls. Data associated with a call include one or more utterances made by speakers and a set of values indicating relevance between content of the call and topic categories of the call. The disclosed technology generates a topic vector associated with a call and/or respective speakers of the call. The topic vector includes a multidimensional vector where each dimension corresponds to a topic category. The disclosed technology determines calls that are contextually similar by comparing angular distances between topic vectors. A search query receiver receives a search query that queries contacts and calls that are contextually similar to a given call and/or a speaker. The disclosed technology identifies calls with topic vectors that are within a predetermined angular distance.

Abstract: Disclosed herein are computer-implemented devices, systems, and methods for sanitizing an audio recording of a conversation. In an example, such a computer-implemented method can include selecting a transcript of the conversation. The transcript can include a plurality of redaction markers within the audio recording. The plurality of redaction markers can be identifiable via one or more redaction marker start and/or end points. The computer-implemented method can include identifying a first redaction marker and a second redaction marker of the one or more redaction markers. The first redaction marker can be different from the second redaction marker. In this regard, a redaction segment is formable between the first and second redaction markers. The computer-implemented method can include selecting the audio recording. The computer-implemented method can include redacting the audio recording between the first redaction marker and the second redaction marker.

Abstract: Disclosed herein are computer-implemented methods for intelligent phrase generation. Example methods include acquiring a bulk data input that includes one or more seed phrases that are requested for derivation, inputting the bulk data input into a model, and returning an arrangement result to a user. The model is configured to determine one or more derivative phrases from each of the seed phrases in the bulk data input, each of the one or more derivative phrases corresponding to a respective seed phrase. The model is configured to determine one or more arrangements with which to arrange each of the derivative phrases in the one or more derivative phrases. The model is configured to determine a characteristic of the respective seed phrase, the one or more arrangement corresponding to the characteristic of the respective seed phrase.

Abstract: Disclosed herein are devices, systems, and computer-implemented methods for intelligent conversational intent detection. Example methods include acquiring a conversational transcript input that is requested for intent detection, inputting the conversational transcript input into a model configured to decipher a conversational intent segment, and returning to a user the conversational intent segment. The conversational transcript input can include one or more conversational transcript segments. The conversational intent segment can correspond to which of the one or more conversational transcript segments is likely to indicate a conversational intent of the conversational transcript input based on the intent detection.