Getac Technology Corporation

Whp Workflow Solutions, Inc.

Ryan Kucera

Thomas Guzik

Muhammad Adeel

A content management system (CMS) manages content for trained machine learning (ML) models. The CMS may develop first, second, and third trained ML models from corresponding datasets, to output respective values of dependent variables derived from data underlying the datasets as independent variables, the respective outputs having statistical confidences in the accuracy of their predictions. The third dataset results from combining the first and second datasets, and the third trained ML model is derived from training on the third dataset. The datasets and ML models are stored in a data store, with the trained ML models associated with respective datasets, the datasets with respective underlying data, the trained ML models with respective statistical confidences and corresponding thresholds, and the trained ML models with metadata indicating independent and dependent variables. The datasets and ML models can be versioned and the provenance of the datasets tracked for future ML modeling.

Content management system for trained machine learning models

This disclosure describes techniques for creating a universal schema with default fields that support sensor formats of different devices. In one example, the universal schema supports substantial equivalents between data fields in different sensor formats. Further, a sensor format may be configured to support inheritance and aggregation of sensor formats in prior devices. Accordingly, the mapping of sensor formats that supports inheritance and aggregation in the universal schema may provide several advantages such as capturing a mapping of substantive equivalents between the fields in different sensor formats.

Hierarchical data ingestion in a universal schema

A Network Operation Center may receive video data, sensor data and third-party data for a situation that a police officer or security service personnel has been called to. Using the video data, a sentiment analysis engine may generate a sentiment data file that contains the sentiment of at least one individual involved in the situation. Using the video data, sensor data, third party data and the sentiment data file, the sentiment analysis engine may generate a safety quality value for the situation. Subsequently, the safety quality value is compared to a predetermined sentiment value to establish a safety rating and confidence interval for the situation. Furthermore, the sentiment analysis engine may generate a situational awareness file, that contains the safety rating and confidence interval, and route it to the field computing device of the officer for evaluation and implementation.

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Columbia Heights, MN, United States of America

Ryan Kucera