A system and a method creating an intelligent memory and providing contextual intelligent recommendations is provided. The invention provides extracting electronic communications data associated with active user data. Further, the invention provides performing a keyword tagging operation on conversation data present in the extracted electronic communications data based on a pre-generated keywords map. The invention provides generating a multi-relational model representative of conversation data associated with the electronic communications data in the form of graph nodes based on the keywords stored as the first tag and the second tag. The invention provides transmitting one or more electronic Recommendation Action Communication (RAC) with embedded application program interface calls based on the multi-relational model, the embedded application program interface calls enabling actions to be taken on information units via a single click.

Techniques for improved data services are provided. Upon receiving a first request from a first client, a first timeline comprising a first plurality of events for an asset indicated in the first request is generated. Upon receiving a second request from a second client, a second timeline comprising a second plurality of events is generated. A first submission for the asset is provided to the first client, comprising updating a first graphical user interface (GUI) output on a first device of the first client. Upon determining that the first client approved the first submission, a merged timeline is generated based on the first and second timelines, where the merged timeline includes the first and second pluralities of events, comprising: updating the first GUI output on the first device, and a second GUI output on a second device of the second client, to indicate the merged timeline.

A method, a computer program product, and a computer system manage meeting divergence for a meeting involving a plurality of participants to discuss a plurality of core intents. The method includes receiving a first contribution from a first one of the participants during the meeting. The method includes determining a first one of the core intents that the first contribution is associated. The method includes determining a linkage of the first contribution to at least one second, previous contribution provided during the meeting. The method includes generating a graphical representation of a progress of the meeting, the graphical representation including a first visual indicator corresponding to the first contribution and at least one second visual indicator respectively corresponding to the at least one second contribution. The first visual indicator is positioned with respect to the at least one second visual indicator to represent the linkage.

A technology trend prediction method and system are provided. The method comprises acquiring paper data, and further comprises following steps: processing the paper data to generate a candidate technology lexicon; screening the candidate technology lexicon based on mutual information; calculating an independent word forming probability of an OOV word; extracting missed words in a title using a bidirectional long short-term memory network and a conditional random field (BI-LSTM+CRF) model; predicting a technology trend. The technology trend prediction method and system provided analyzes relationship of technology changes in a high-dimensional space, and predicts a development of technology trend based on time by extracting technical features of papers through natural language processing and time sequence algorithms.

A computer based system and method for assigning queries to topics and/or visualizing or analyzing query coverage may include, using a computer processor, searching, using a set of queries, over a set of text documents, to produce for each query a set of search results for the query. Each search result may include a subset of text from a text document of the set of text documents. For each query, a query vector may be calculated based on the set of search results for the query, and for each of a set of topics describing the set of text documents, a topic vector may be calculated. A report or visualization may be generated including the set of queries and the set of topics using the topic vectors and the query vectors.

Various embodiments comprise systems and methods to determine entropy in strings generated by a data pipeline. In some examples, data monitoring circuitry monitors a data pipeline that ingests input data, processes the input data, and responsively generates and transfers a data string that comprises character groups. The data monitoring circuitry receives the data string, identifies character groups in the data string, identifies group types for the character groups, and assigns numbers to the character groups based on the group types. The data monitoring circuitry determines a probability distribution for the numbers, calculates entropy for the data string based on probability distribution, and generates an entropy histogram based on the entropy. The data monitoring circuitry compares the entropy histogram of the data string to another entropy histogram for another data string, determines a change in entropy, and reports the change in entropy.

Systems and methods for managing content provenance are provided. A network system accesses a plurality of documents. The plurality of documents is then hashed to identify one or more content features within each of the documents. In one embodiment, the hash is a MinHash. The network system compares the content features of each of the plurality of documents to determine a similarity score between each of the plurality of documents. In one embodiment, the similarly score is a Jaccard score. The network system then clusters the plurality of documents into one or more clusters based on the similarity score of each of the plurality of documents. In one embodiment, the clustering is performed using DBSCAN. DBSCAN can be iteratively performed with decreasing epsilon values to derive clusters of related but relatively dissimilar documents. The clustering information associated with the clusters are stored for use during runtime.

The subject matter described herein relates to methods, systems, and computer readable media for dynamic cluster-based search and retrieval. An example method for dynamic cluster-based search and retrieval occurs at a server. The method includes: retrieving document data for a plurality of documents related to user input; performing keyword discovery on the document data for determining term related frequency metrics and document related frequency metrics; representing the plurality of documents as a term-document matrix based on the term related frequency metrics and the document related frequency metrics; reducing, using latent semantic analysis, the dimensionality of the matrix; clustering, using a k-means clustering algorithm and the dimensionally reduced matrix, the plurality of documents into clusters; and sending presentation information to a client device for displaying visual representations of the clusters, wherein each visual representation is associated with one or more of the plurality of documents.

Systems and methods are provided herein for enabling a computing system to search and interact with service records containing natural language text to aid in the analysis of those records by: displaying in a user interface an image of a complex system; receiving, from the user interface, a high-level selection of criteria of the complex system; querying issue maps associated with individual natural language service records of a corpus of natural language service records based on the high-level selection, wherein the issue maps specify at least one term related to the complex system and a location on the complex system associated with the at least one term; and returning at least one issue map, wherein the at least one issue map returned specifies a term or location correlated to the criteria of the complex system indicated by the high-level selection.

An embodiment includes executing a querying process that returns database documents containing text associated with a database environment. The embodiment tokenizes the database documents into a series of n-gram tokens and groups the tokens into topic classes using natural language processing (NLP). The embodiment also generates a feature map by applying a convolution layer to an image that depicts a database structure graph of the database environment. The embodiment detects an architectural element in the image by applying a region proposal network (RPN) to the feature map and classifies the architectural element into one of the plurality of topic classes using a trained neural network. The embodiment renders, responsive to a user selection of the architectural element, an overlay depicting information about the architectural element using text and other architectural elements that are in a same topic class as the user-selected architectural element.