Systems, methods and computer program products for using searchable aggregate indices associated with non-aggregated value storage. In one method, a search system stores metadata values for each of a plurality of objects in a storage unit. The metadata values are stored in corresponding value storage locations that are associated with an identifiable metadata fields. An aggregate index is provided which includes a dictionary of terms that are contained in metadata values associated with a designated set of the metadata fields. The aggregate index is searched for one or more specific search terms, and one or more of the metadata values are retrieved from the value storage locations in response to the search, where the individual metadata fields associated with the retrieved metadata values are identified.

A method and system provide the ability to manage entities of a marketing domain model in a multi-state workflow. Multiple entities are acquired in a content hub. Each entity is a set of data that belongs together as one and includes properties that describe entity details. Relations are created between the multiple entities to give meaning to the marketing domain model. A solution graph is generated that represents all of the multiple entities (nodes) and relations (edges). Inside the solution graph, a state workflow can be created for each node. Nodes can be linked to a state and there are transitions between the states. Multiple non-linear state workflows can be orchestrated by an overall waterfall-based workflow (that is linear and time duration based. A graphical user interface enables management of and renders a representation of the multiple entities, the solution graph, and the workflows.

Direct storage loading may be used to add data to a database. New data may be added to a database, using nodes different than a database engine to access a database. The addition of the new data may be assigned to different nodes. The nodes may obtain the data and store the data to storage locations according allocated space in the database by the database engine. The new data can then be made available for access at the database engine.

Methods, systems and computer program products for resolving multiple magnitudes assigned to a target vector are disclosed. A target vector that includes one or more target vector dimensions is received. One of the target vector dimensions is processed to determine a total number of magnitudes assigned to the processed target vector dimension. Also, a source vector that includes one or more source vector dimensions is received. The received source vector is processed to determine a total number of features associated with the source vector. When it is detected that the total number of magnitudes assigned to the processed target vector dimension exceeds one, one of the assigned magnitudes is selected based on one of the determined features associated with the source vector.

Disclosed are an artificial intelligence (AI) system using a machine learning algorithm such as deep learning, and an application thereof. The present disclosure provides an electronic device comprising: an input unit for receiving content data; a memory for storing information on the content data; an audio output unit for outputting the content data; and a processor, which acquires a plurality of data keywords by analyzing the inputted content data, matches and stores time stamps, of the content data, respectively corresponding to the plurality of acquired keywords, based on a user command being inputted, searches for a data keyword corresponding to the inputted user command among the stored data keywords, and plays the content data based on the time stamp corresponding to the searched data keyword.

In an aspect, a system for generating a virtual assistant in a messaging user interface, the system comprising a computing device designed and configured to initiate a virtual message user interface between a user client device and the computing device; receive a user message entered by a user into the virtual message user interface; select a conversation profile for the virtual assistant as a function of the user message, wherein the conversation profile comprises behavior that the user uses to communicate; prioritize an agenda action of a plurality of agenda actions contained within a user agenda list as a function of the user message; and generate a response to the user message comprising the prioritized agenda action, wherein the response is further generated as a function of the selected conversation profile.

A method is provided that stores, in a second memory, an index structure including, for each given word from among words included in documents, a group of document IDs of documents including the given word. The method stores an index structure subset in a main memory which is faster than secondary memory. The method acquires a keyword and identifies any documents including the keyword. The method finds top-K frequent words among the words included in the identified documents by: identifying, for each given group in descending order of the number of the documents IDs therein, the number of documents IDs of the identified documents in the given group, from the subset when the number of document IDs in the given group is within the range, and from the index structure otherwise; and presenting words of top-K groups with a largest amount of the document IDs identified.

A solution is proposed for ranking documents belonging to two different domains. A corresponding method comprises generating a descriptor for each of the documents; the descriptor comprises corresponding values and confidence indexes of multiple properties (of the corresponding document); the documents of a domain are ranked with respect to a document of another domain according to a comparison of their descriptors. A computer program product for performing the method are also proposed. Moreover, a computing system for implementing the method is proposed.

The disclosure herein describes providing responses to natural language queries associated with transcripts at least by searching multiple indexes. A transcript associated with a communication among a plurality of speakers is obtained, wherein sets of artifact sections are identified in the transcript. A set of section indexes is generated from the transcript based on artifact type definitions. A natural language query associated with the transcript is analyzed using a natural language model and query metadata of the analyzed natural language query is obtained. At least one section index of the set of section indexes is selected based on the obtained query metadata and that selected at least one section index is searched. A response to the natural language query is provided including result data from the searched at least one search index, wherein the result data includes a reference to an artifact section referenced by the searched section index(es).

Systems, methods, and computer-readable media for providing entity relation extraction across sentences in a document using distant supervision are disclosed. A computing device can receive an input, such as a document comprising a plurality of sentences. The computing device can identify syntactic and/or semantic links between words in a sentence and/or between words in different sentences, and extract relationships between entities throughout the document. A knowledge base (e.g., a table, chart, database etc.) of entity relations based on the extracted relationships can be populated. An output of the populated knowledge base can be used by a classifier to identify additional relationships between entities in various documents. Machine learning can be applied to train the classifier to predict relations between entities. The classifier can be trained using known entity relations, syntactic links and/or semantic links.