FIELD OF THE INVENTION

The present invention relates to a deep learning system suitable for searching data sources for specific content. In particular, the present invention relates to an unconventional machine-implemented process, leveraging a machine learning algorithm, to provide a technology that searches data sources and recognizes feature data associated with one or more predetermined classifiers.

BACKGROUND

Generally, data sources include unlimited potential for usable content. Data streams include information created and posted by users throughout a network. Common data sources include web feeds, news feeds, social media feeds, etc. Typically, other users can see the content posted on various data sources by subscribing or following a particular data stream. Additionally, the other users can search the data sources by scrolling through content or performing a standard search function.

However, these methodologies experience some shortcomings. In particular, searching one or more data sources can be limited to who a user is following and/or users can only search data feeds using traditional search methodologies not specifically designed for locating data within the data sources. Furthermore, there is a computer-centric and Internet-centric problem in existence in that the sheer volume of data being generated continuously means that it is difficult for users to find the most interesting or relevant information for their purposes. This is a classic “needle in the haystack” problem that is only getting worse by the day as more and more information is generated across data streams at ever increasing paces.

SUMMARY

There is a need for a system and method to extract specific information from a plurality of data sources in a manner that overcomes the above-identified computer-centric and Internet-centric problems. The present invention is directed toward further solutions to address this need, in addition to having other desirable characteristics. Specifically, the present invention provides an unconventional machine-implemented process, leveraging a machine learning algorithm, to provide a technology that searches data sources and can recognize feature data associated with one or more predetermined classifiers and extracts features (e.g., images, text blocks, the names of specific entities such as people, companies, products or any other class of entity type, generally referred to as named entities, etc.) from one or more data sources (e.g., social media post feeds, document sources, image repositories, etc.) based on one or more classifiers (e.g., hashtags, dictionaries of named entities, etc.) to enable dynamic updating of training sets for a machine learning algorithm (e.g., deep learning) such that it can identify specific criteria (e.g., named entities and their relationships, objects in images, etc.) of the features. Additionally, the algorithm, classifier signals and training sets can all be continuously updated to improve performance over time.

In accordance with example embodiments of the present invention, a system that automatically updates machine learning training sets and classifiers is provided. The system includes a data storage device storing a machine learning algorithm and machine learning training sets, the machine learning algorithm configured to reference the machine learning training sets. The system also includes a processor effectuating a recognition agent over at least one data source and a processor effectuating an extractor that aggregates feature data from the one or more data sources of the at least one data source based on one or more predetermined classifiers. The system further includes an input device receiving specific criteria and a processor effectuating an updater that dynamically updates the machine learning training sets and classifier dictionary based on the aggregated feature data and the specific criteria, enabling the machine learning algorithm to identify at least one feature data including the specific criteria from the aggregated feature data. The system also includes an output device transmitting the at least one feature data including the identified specific criteria. The machine learning algorithm, the machine learning training sets, and the classifier dictionary, are continuously updated to improve performance of the machine learning algorithm over time.

In accordance with aspects of the present invention, the effectuating the recognition agent over at least one data source includes the recognition agent denoting analytics in the data to aggregate feature data. The system can further include the recognition agent utilizing the machine learning algorithm to search the aggregated feature data for the at least one feature data that is associated with the predetermined classifiers and the specific criteria.

In accordance with aspects of the present invention, further includes a dictionary including the predetermined classifiers that are associated with specific criteria types. The specific criteria types can be at least one of medical, scientific, legal or news related information, and at least one feature can be at least one of an image, text, video, and audio. The predetermined classifiers can be at least one of hashtags or named entities.

In accordance with aspects of the present invention, the plurality of data sources are at least one of social media post feeds, text documents, tabular content or image repositories. The machine learning algorithm is a deep learning algorithm.

In accordance with example embodiments of the present invention, a method for automatically updating machine learning training sets and classifier dictionary is provided. The method includes storing a machine learning algorithm and machine learning training sets, the machine learning algorithm configured to reference the machine learning training sets. The method also includes a processor effectuating a recognition agent over one or more data sources of at least one data source and a processor effectuating an extractor that collects feature data from the one or more data sources of the at least one data source based on one or more predetermined classifiers. The method further includes an input device receiving specific criteria and a processor effectuating an updater that dynamically updates the machine learning training sets and classifier dictionary based on the collected feature data and the specific criteria, enabling the machine learning algorithm to identify at least one feature data including the specific criteria from the collected feature data. The method also includes an output device transmitting the at least one feature data including the identified specific criteria. The machine learning algorithm, the machine learning training sets, and the classifier dictionary, are continuously updated to improve performance of the machine learning algorithm over time.

In accordance with aspects of the present invention, the effectuating over at least one data source comprises the recognition agent denoting analytics in the data to aggregate feature data. The system can further include utilizing the machine learning algorithm to search the aggregated feature data for the at least one feature data that is associated with the predetermined classifiers and the specific criteria.

In accordance with aspects of the present invention, the system further includes a dictionary including the predetermined classifiers that are associated with specific criteria types. The specific criteria types can be at least one of medical, scientific, legal or news related information, and at least one feature can be at least one of an image, text, video, and audio. The predetermined classifiers can be at least one of hashtags or named entities.

In accordance with aspects of the present invention, the plurality of data sources are at least one of social media post feeds, text documents, tabular content or image repositories. The machine learning algorithm is a deep learning algorithm.

In accordance with example embodiments of the present invention, a method for searching for information related to posts in a plurality of data sources is provided. The method includes extracting at least one feature associated with one or more classifiers from the plurality of data sources, the one or more classifiers being identified from a classifier dictionary. The method also includes dynamically updating training sets for a machine learning algorithm and identifying specific criteria from the at least one feature utilizing the machine learning algorithm and the training sets. The method further includes aggregating the information from data sources of the plurality of data sources containing the identified specific criteria, providing the aggregated information for display in a searchable format, and updating, periodically, the training sets, the classifier dictionary, and the machine learning algorithm.

