US 12,111,815 B2Granted

Correlating event data across multiple data streams to identify compatible distributed data files with which to integrate data at various networked computing devices

AssigneeSightly Enterprises, Inc.

InventorsAdam Eric Katz, Aman Raghuvanshi, Adam Jarrell Smith, Jacob Maximillian Miesner

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Abstract

Various embodiments relate generally to data science and data analysis, computer software and systems, to provide a platform to facilitate updating compatible distributed data files, among other things, and, more specifically, to a computing and data platform that implements logic to facilitate correlation of event data via analysis of electronic messages, including executable instructions and content, etc., via a cross-stream data processor application configured to, for example, update or modify one or more compatible distributed data files automatically. In some examples, a method may include activating APIs to receive via a message throughput data pipe different data streams, extracting features from data using the APIs, identifying event-related data across data sources, correlating the event-related data to form data representing an even, classifying event-related data into a state classification, determining compatible data at data sources, identifying compatible data, and transmitting integration data to integrate with a data source.

Claims20

Dependent claims

A method comprising:

receiving different data streams via a message throughput data pipe, the different data streams being associated with multiple data sources each associated with a processor and memory, the data including at least a portion of executable instructions;
extracting features from one or portions of data or the executable instructions to form extracted features including units of data representing one or more text data, image data, and audio data, wherein extracting the features includes forming an event composite value (“ECV”) based on adjusted weighting factors;
analyzing a subset of data representing the extracted features to determine event data by executing an instruction to implement one or more natural language processors, one or more image recognition processors, and one or more audio recognition processors to determine the event data by processing the subset of data representing the extracted features, the subset of data being determined based on an algorithm configured to identify cosine similarity, wherein the algorithm uses the cosine similarity to determine similarity between the subset of data representing the extracted features;
generating data representing an event based on the subset of data representing the extracted features as a function of the adjusted weighting factors;
identifying data representing a subset of the multiple data sources associated with the event;
calculating compatibility of a subset of integration data at a cross-stream data processor with distributed data files at the subset of the multiple data sources;
implementing one or more cross-stream data processors configured to communicate event-related data across the multiple data sources;
and
transmitting the subset of integration data to integrate with at least one of the subsets of the multiple data sources, at least one of the multiple data sources including a target data file configured to function in response to integration with at least a portion of the integration.

6 more elements

A system comprising:

a data store configured to receive streams of data via a network into an application computing platform;
and
a processor configured to execute instructions to implement an application configured to:
receive different data streams via a message throughput data pipe, the different data streams from multiple data sources each associated with a processor and memory, the data including at least a portion of executable instructions;
extract features from one or portions of data or the executable instructions to form extracted features including units of data representing one or more text data, image data, and audio data, wherein extracting the features includes forming an event composite value (“ECV”) based on adjusted weighting factors;
analyze a subset of data representing the extracted features to determine event data by executing an instruction to implement one or more natural language processors, one or more image recognition processors, and one or more audio recognition processors to determine the event data by processing the subset of data representing the extracted features, the subset of data being determined based on an algorithm configured to identify cosine similarity, wherein the algorithm uses the cosine similarity to determine similarity between the subset of data representing the extracted features;
generate data representing an event based on the subset of data representing the extracted features as a function of the adjusted weighting factors;
identify data representing a subset of the multiple data sources associated with the event;
calculate compatibility of a subset of integration data at the processor including a cross-stream data processor with distributed data files at the subset of the multiple data sources;
implement one or more cross-stream data processors configured to communicate event-related data across the multiple data sources;
and
transmit the subset of integration data to integrate with at least one of the subsets of the multiple data sources, at least one of the multiple data sources including a target data file configured to function in response to integration with at least a portion of the integration.

9 more elements

Dependencies are inferred from the claim text.