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.

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: A computer-implement process generally pertains to ephemeral chat messaging, and in particular, maintaining context of a conversation between a BOT and a user from one webpage of a web browser to another webpage of the web browser. The computer-implemented process includes adding a message to a chat widget. The chat widget is displayed within the webpage of the web browser and the message being added is from the other webpage that the user responded to. The computer-implemented process also includes discarding one or more previous messages in the chat widget that are not responded to by the user, allowing the conversation between the BOT and the user to persist while retaining the context of the conversation from the other webpage.

Abstract: Large batches of social media communications may be automatically annotated. This provides techniques to create large labeled datasets without the assistance of human labelers. For instance, social media communications may be fetched and annotated as actionable or noise for a given account (e.g., a brand handle on Twitter®) without human review. Social media communications from users who are attempting to engage with a brand or entity on a social media platform may be annotated as actionable, whereas other communications may be labeled as noise.

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 process for displaying one or more message channels may include arranging the one or more message channels for a user of a computing system to view. Each of the one or more message channels may include a message channel icon, a message channel name, a message, a timestamp of the message, and a badge number.

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: Large batches of social media communications may be automatically annotated. This provides techniques to create large labeled datasets without the assistance of human labelers. For instance, social media communications may be fetched and annotated as actionable or noise for a given account (e.g., a brand handle on Twitter®) without human review. Social media communications from users who are attempting to engage with a brand or entity on a social media platform may be annotated as actionable, whereas other communications may be labeled as noise.