Abstract: Predicting churn from a multi-product data using sequential modeling includes collecting, by a journey module, data from a plurality of platforms to build a journey defined by one or more events, and creating, by a sequential deep learning (DL) model, relationship data identifying between events in a chronological order, capturing temporal dependencies present in the data. This also includes correlating, by a churn prediction module, sequences of events to a churn event, and identifying when the churn event is going to occur based on pattern learnt to differentiate between “churn” and “non-churn” journeys.

Abstract: In order to predict an optimized send time for one or more end users in an email campaign, one or more times in a day are identified for sending the email campaign to the one or more end users. The email campaign may then be transmitted at one of the one or more times identified to the one or more end users.

Abstract: Real-time automated grouping of associated alerts into a single incident uses a machine learning (ML) framework. The framework includes learning alert-vectors and a n-dimensional representation in a vector space to determine a frequency of occurrence and co-occurrence patterns of repeat data. The framework includes applying a cosine-similarity or vector similarity metrics to determine the frequency of the occurrence and co-occurrence patterns in the repeat data, and grouping the repeated data based on the learning of the learning of the alert-vectors and the n-dimensional representation and the applying of the cosine-similar or vector similarity metrics.

Abstract: A method of managing connections to one or more servers in a multi-tenancy computing environment is provided. At a multi-tenancy connection manager responsible for managing connections for multiple tenants of the multi-tenancy computing environment, a plurality of connection pools is configured, each for use by a respective one of the multiple tenants. A connection request is received from a tenant of the multiple tenants. The tenant is identified based on the connection request. Management rules for managing a connection pool that is associated with the identified tenant are obtained. It is determined whether the management rules permit execution of the connection request. In response to a positive determination, the connection request is executed. In response to a negative determination, the connection request is throttled, to preserve and maintain a quality of service provided.

Abstract: Extracting key-value attributes from unstructured data includes a parser receiving a first incident comprising key-value attributes and a second incident comprising key-value attributes, and parsing the first incident for the key-value attributes and the second incident for key-value attributes. A machine learning (ML) model perform a pairwise comparison of one or more common key-value attributes associated with the first incident and the second incident. A cosine similarity module computes a cosine similarity between the one or more common key-value attributes to generate a score for each of the one or more common key-value attributes associated with the first incident and the second incident, and generates a final score between the first incident and the second incident by averaging all cosine similarity scores computed for each of the one or more common key-value attributes.

Abstract: Lead pooling and ranking includes implementing a pooling technique enhancing and optimizing lead scoring machine learning techniques for a set of leads. Lead pooling and ranking also includes generating a lead score for each configured rule, and combining one or more machine learning (ML) scores and one or more rules based scores to create a unitary score for each corresponding lead in the set of leads. Lead pooling and ranking further includes generating a rank and rating for each lead in the set of leads.

Abstract: Finding accurate prediction objectives includes building, by a framework application, a data pool for each of a plurality of prediction objectives. A plurality of machine learning (ML) models is trained for each data pool, and each of the plurality of ML models is combined for each data pool. One or more accurate objectives are identified and selected on the basis of a performance of the combined plurality of ML models.

Abstract: Automatic extractions of situations includes creating a situation image includes accessing a conversation between a first user and a second user, and generating an abstract knowledge graph at one or more textual levels. The method also includes generating one or more manifests by pruning the abstract knowledge graph and segmenting the pruned abstract knowledge graph. The method further includes converting the one or more manifests into the situation image.

Abstract: Incremental clustering of similar or related messages that otherwise requires limited use of memory for the purpose of increasing scalability. Incremental clustering includes receiving, by a machine learning (ML) engine, an incoming message from an application programming interface (API) server, and scanning, by the ML engine, a plurality of clusters for one or more messages similar to that of the incoming message. Incremental clustering also includes identifying, by a clustering engine, a cluster from the plurality of clusters. The identified cluster includes the one or more messages similar to that of the incoming message. Incremental clustering further includes assigning, by the clustering engine, the incoming message to the identified cluster.

Abstract: To collaborate in context, an online messaging platform is launched for a user to communicate by way of electronic messaging with one or more other users. The online messaging platform is launched in response to a user selecting text. One or more additional users are added to the online messaging platform, when the user tags the one or more additional users. Exchange of electronic messaging is facilitated, via the online messaging platform, between the user and the one or more additional users in regard to the selected text.

Abstract: A multi-tenant load balancing system that includes artificial intelligence based algorithm to dynamically route requests from one or more channels to an agent best suited to process the request. The AI based algorithm routes the request based on company's business goals, agent attributes, and channel attributes. The AI based algorithm also predicts agent availability.

Abstract: A computer-implemented method for deduplicating records includes generating a block comprising of a group of records. The method also includes creating one or more record pairs from the block, and calculating one or more features based on one or more fields within the one or more record pairs. The method further includes inputting the one or more features into a machine language trained model to predict a probability score. The probability score indicates whether two records are duplicates. The method also includes storing the probability score as links between two vertices in a graph, and displaying one or more duplicate records by querying the graph.

Abstract: The present disclosure relates to a method of operating a database system. The database system comprises: a database; a first compute node comprising a first database proxy; and a second compute node comprising a second database proxy. The method comprises receiving and processing, at the first database proxy, a first plurality of access requests to access the database; receiving and processing, at the second database proxy, a second plurality of database access requests to access the database; monitoring for a failure event associated with the first database proxy; and, in response to the monitoring indicating a failure event, initiating a failover procedure between the first database proxy and the second database proxy. The failover procedure comprises: redirecting the first plurality of access requests to the second database proxy; and processing, at the second database proxy, the first plurality of access requests.

Abstract: Automatic extractions of situations includes creating a situation image includes accessing a conversation between a first user and a second user, and generating an abstract knowledge graph at one or more textual levels. The method also includes generating one or more manifests by pruning the abstract knowledge graph and segmenting the pruned abstract knowledge graph. The method further includes converting the one or more manifests into the situation image.

Abstract: A process for annotating a video in real-time on a mobile device. The process may include creating one or more markers, allowing a user of the mobile device to annotate the video while one or more users within a group of users are annotating the streaming video in real-time. The process may include receiving a selection from the user for which he or she seeks to annotate within the video. The process further includes displaying a text box for a frame or range of frames selected by the user seeks for annotation, and receiving a submitted text box from the user and propagating the annotations within the submitted text box to one or more users within the group in real-time.

Abstract: Incremental clustering of similar or related messages that otherwise requires limited use of memory for the purpose of increasing scalability. Incremental clustering includes receiving, by a machine learning (ML) engine, an incoming message from an application programming interface (API) server, and scanning, by the ML engine, a plurality of clusters for one or more messages similar to that of the incoming message. Incremental clustering also includes identifying, by a clustering engine, a cluster from the plurality of clusters. The identified cluster includes the one or more messages similar to that of the incoming message. Incremental clustering further includes assigning, by the clustering engine, the incoming message to the identified cluster.

Abstract: Incremental clustering of similar or related messages that otherwise requires limited use of memory for the purpose of increasing scalability. Incremental clustering includes receiving, by a machine learning (ML) engine, an incoming message from an application programming interface (API) server, and scanning, by the ML engine, a plurality of clusters for one or more messages similar to that of the incoming message. Incremental clustering also includes identifying, by a clustering engine, a cluster from the plurality of clusters. The identified cluster includes the one or more messages similar to that of the incoming message. Incremental clustering further includes assigning, by the clustering engine, the incoming message to the identified cluster.

Abstract: A noise reduction and smart ticketing application for social media-based communication systems may identify social media-based communications from users who are attempting to engage with a brand or entity on a social media platform as actionable, and distinguish other communications as noise. The noise reduction and smart ticketing system may use machine learning to determine which social media communications are actionable for a given company or other organization, and generates tickets for actionable communications. Actionable communications may include, but are not limited to, technical support issues, inquiries about a product release date, grievances, incidents, suggestions to improve service, critiques of company policies, etc. Non-actionable communications (i.e., “noise”) may include, but are not limited to, suggestions to other users, promotions, coupons, offers, marketing campaigns, affiliate marketing, statements that a user is attending an event, etc.

Abstract: Incremental clustering of similar or related messages that otherwise requires limited use of memory for the purpose of increasing scalability. Incremental clustering includes receiving, by a machine learning (ML) engine, an incoming message from an application programming interface (API) server, and scanning, by the ML engine, a plurality of clusters for one or more messages similar to that of the incoming message. Incremental clustering also includes identifying, by a clustering engine, a cluster from the plurality of clusters. The identified cluster includes the one or more messages similar to that of the incoming message. Incremental clustering further includes assigning, by the clustering engine, the incoming message to the identified cluster.

Abstract: A computer-implemented method for deduplicating records includes generating a block comprising of a group of records. The method also includes creating one or more record pairs from the block, and calculating one or more features based on one or more fields within the one or more record pairs. The method further includes inputting the one or more features into a machine language trained model to predict a probability score. The probability score indicates whether two records are duplicates. The method also includes storing the probability score as links between two vertices in a graph, and displaying one or more duplicate records by querying the graph.