Abstract: In embodiments, an apparatus includes a user data interpretation circuit structured to interpret user data corresponding to a first user; a bootstrap circuit structured to: match the first user to a second user via querying one or more databases based at least in part on the user data; retrieve historical time sequence data associated with the second user via querying the one or more databases; extract a time sequence trend from the historical time sequence data; identify a portion of the historical time sequence data corresponding to the extracted time sequence trend; and generate, based at least in part on the identified portion, time sequence data corresponding to the first user; and a time sequence data provisioning circuit structured to transmit the time sequence data.

Abstract: A computer-implemented method of training machine learning models for making simulated estimations is provided. The method includes collecting, from a database, a set of historical data, applying one or more transformations to the set of historical data to create a set of model features, and separating the set of model features into one or more pools, each pool comprising one or more model features of the set that are homogeneous with respect to a common value. the method further includes, for each pool, dynamically creating a training set that includes the one or more sets of model features of the pool and at least some of the historical data. The method further includes, for each training set, training a machine learning model on the training set.

Abstract: A system for providing intelligent part of speech processing of complex natural language is disclosed. The system identifies a multiword concept from an input and replaces the multiword concept with a token to be tagged as a desired part of speech. The system passes the modified text including the token to a part-of-speech tagger to tag each word in the text with the appropriate part-of-speech. The system may replace the token with the original text that the token was utilized to replace so that the original intent of the text is evident. The system may analyze the tagged text to generate analyses and interpretations associated with the input. When multiple multiword concepts are identified, the system may evaluate them by computing scores for each of the multiword concepts that may be replaced with tokens, for each of the modified texts including the tokens, or for any interpretations and analyses thereof.

Abstract: An apparatus for extended horizon scheduling that includes a time-sequence interpretation circuit, a target interpretation circuit, a horizon target analysis circuit, and a promotive action provisioning circuit. The time-sequence interpretation circuit interprets time-sequence data, the target interpretation circuit interprets target data, and the time-sequence trend analysis circuit extracts a trend from the time-sequence data. The horizon target analysis circuit: determines whether the extracted trend furthers or impedes a target defined, in part, by the target data, and responsive to a determination that the extracted trend impedes the target, generates a promotive action command value structured to trigger an adjustment to the time-sequence data. The adjustment is structured to mitigate the extracted trend from impeding the target. The promotive action provisioning circuit transmits the promotive action command value.

Abstract: Networks, methods, and apparatus to ensure schedule conformance are described. An example apparatus may include a schedule interpretation circuit to interpret schedule data, and a warden circuit to generate scores for the schedule data with respect to schedule properties. The warden circuit retrieves baseline values corresponding to schedule properties and determines whether the schedule data is out of alignment with the baseline value for any of the schedule properties. If so, a corrective action circuit generates a corrective action command value to trigger an adjustment to the schedule data to effect a change to a schedule property that is out of alignment. A corrective action provisioning circuit transmits the corrective action command value.

Abstract: A system for autonomously delivering software features is disclosed. The system parses data obtained from a variety of sources, and extracts source concepts from the parsed data to generate models for inclusion in a set of agglomerated models. Over time, additional data from the variety of sources may be utilized to update the set of agglomerated models. The updated agglomerated models may be analyzed by the system to determine whether new features and/or functionality may be added to an application under evaluation by the system. In the event that new features and/or functionality may be added to the application under evaluation, the system may automatically generate code corresponding to the new features and/or functionality and incorporate that features and/or functionality into the application under evaluation.

Abstract: A system for discovering semantic relationships in computer programs is disclosed. In particular, the system may synergistically identify and validate semantic relationships, concepts, and groupings associated with data elements within a static or dynamic, time varying, source input. The system may utilize feature extractors to extract features from the input and reasoners to develop associations using data from multiple feature set types, and, can thus generate reliable, robust, and complete sets of semantic relationships from the input. The system may generate hypotheses associated with the relationships, concepts, and groupings, and validate the hypotheses by testing an application under evaluation by the system and observing the outputs generated from the testing.

Abstract: A system for providing intelligent part of speech processing of complex natural language is disclosed. The system identifies a multiword concept from an input and replaces the multiword concept with a token to be tagged as a desired part of speech. The system passes the modified text including the token to a part-of-speech tagger to tag each word in the text with the appropriate part-of-speech. The system may replace the token with the original text that the token was utilized to replace so that the original intent of the text is evident. The system may analyze the tagged text to generate analyses and interpretations associated with the input. When multiple multiword concepts are identified, the system may evaluate them by computing scores for each of the multiword concepts that may be replaced with tokens, for each of the modified texts including the tokens, or for any interpretations and analyses thereof.

Abstract: A system for providing intelligent part of speech processing of complex natural language is disclosed. The system identifies a multiword concept from an input and replaces the multiword concept with a token to be tagged as a desired part of speech. The system passes the modified text including the token to a part-of-speech tagger to tag each word in the text with the appropriate part-of-speech. The system may replace the token with the original text that the token was utilized to replace so that the original intent of the text is evident. The system may analyze the tagged text to generate analyses and interpretations associated with the input. When multiple multiword concepts are identified, the system may evaluate them by computing scores for each of the multiword concepts that may be replaced with tokens, for each of the modified texts including the tokens, or for any interpretations and analyses thereof.