Venkat Rangan

According to various embodiments, described herein are systems and methods for training machine learning (ML) models to generate real-time scores to predict the probabilities of task completion.

Systems and methods are provided for analyzing a history of meetings , the attendees , date of occurrence , and other content to determine the value of the meetings and the attendees . The importance of people and the meetings they attend can be derived based on patterns of attendees . In one embodiment , the meta - data of meetings and the attendees can be used to determine value without requiring time - consuming manual steps or manual evaluation of people and their titles . A graph of… Show more

Systems and methods are provided for analyzing a history of meetings , the attendees , date of occurrence , and other content to determine the value of the meetings and the attendees . The importance of people and the meetings they attend can be derived based on patterns of attendees . In one embodiment , the meta - data of meetings and the attendees can be used to determine value without requiring time - consuming manual steps or manual evaluation of people and their titles . A graph of meetings and its attendees can be generated and used by one or more automated software agents to place value to the content of the meeting , its agenda , and other meeting collateral such as meeting briefs / attachments of meetings . Accordingly , embodiments dramatically reduce the need for human examination of meeting history . Show less

A server computing system determines a plurality of statistics for a plurality of test documents, determines a number of false negatives for a corpus of documents based on one or more of the plurality of statistics for the plurality of test documents. The classification of a document of the corpus of documents is a false negative if classification of the document by a classification model is negative and classification of the document by a user is positive. The server computing system… Show more

A server computing system determines a plurality of statistics for a plurality of test documents, determines a number of false negatives for a corpus of documents based on one or more of the plurality of statistics for the plurality of test documents. The classification of a document of the corpus of documents is a false negative if classification of the document by a classification model is negative and classification of the document by a user is positive. The server computing system calculates an effectiveness of an information retrieval system on a corpus of documents based on the number of false negatives for the corpus of documents.
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A predictive coding system updates a plurality of training documents for an untrained classification model based on a plurality of additional documents. The plurality of additional documents are selected from a plurality of unlabeled documents based on a decision hyperplane associated with a first trained classification model. The predictive coding system provides the updated plurality of training documents to the untrained classification model to cause the untrained classification model to be… Show more

A predictive coding system updates a plurality of training documents for an untrained classification model based on a plurality of additional documents. The plurality of additional documents are selected from a plurality of unlabeled documents based on a decision hyperplane associated with a first trained classification model. The predictive coding system provides the updated plurality of training documents to the untrained classification model to cause the untrained classification model to be retrained and to cause a second trained classification model to be generated. Show less

Techniques are provided for automatic sampling evaluation. An automatic sampling evaluation system enables users to evaluate convergence of one or more search processes. For example, given a set of searches that were validated by human review, a system can implement a retrieval process that samples one or more non-retrieved collections. Each individual document's similarity in the one or more non-retrieved collections is automatically evaluated to other documents in any retrieved sets. Given a… Show more

Techniques are provided for automatic sampling evaluation. An automatic sampling evaluation system enables users to evaluate convergence of one or more search processes. For example, given a set of searches that were validated by human review, a system can implement a retrieval process that samples one or more non-retrieved collections. Each individual document's similarity in the one or more non-retrieved collections is automatically evaluated to other documents in any retrieved sets. Given a goal of achieving a high recall, documents with high similarity can then be analyzed for additional noun phrases that may be used for a next iteration of a search. Convergence can be expected if the information gain in the new feedback loop is less than previous iterations, and if the additional documents identified are below a certain threshold document count. Show less

In various embodiments, a semantic space associated with a corpus of electronically stored information (ESI) may be created and used for concept searches. Documents (and any other objects in the ESI, in general) may be represented as vectors in the semantic space. Vectors may correspond to identifiers, such as, for example, indexed terms. The semantic space for a corpus of ESI can be used in information filtering, information retrieval, indexing, and relevancy rankings.