Systems and methods for Documentation Through Gleaning Content [extract content (information) from various sources. collect gradually and bit by bit.] with an enhanced, easy to use and intuitive user interface experience. This is the system to glean the content such as text, image, audio, video in bit by bit from various sources such as web pages, document viewers, word of mouth, SMS, email, internet messenger, social medias etc., and tagged [labeled] to the document/topic in shorter amount of time. At any point of time one or more gleaned content(s) are compiled as a single document without the need of an editor. The team of users can use any type of devices to collaborate, review and publish the document.

Techniques for generating plug-in application recipe (PIAR) extensions are disclosed. A PIAR management application discovers a particular data type within one or more data values for a particular field of a plug-in application, where the particular data type is (a) different from a data type of the particular field as reported by the plug-in application and (b) narrower than the data type of the particular field while complying with the data type of the particular field. The PIAR management application identifies one or more mappings between (a) the particular data type and (b) one or more data types for fields accepted by actions of plug-in applications. The PIAR management application presents a user interface including one or more candidate PIAR extensions based on the mapping(s). Based on a user selection of a candidate PAIR extension, the PIAR management application executes a PIAR that includes the selected PIAR extension.

Techniques for generating plug-in application recipe (PIAR) extensions are disclosed. A PIAR management application discovers a particular data type within one or more data values for a particular field of a plug-in application, where the particular data type is (a) different from a data type of the particular field as reported by the plug-in application and (b) narrower than the data type of the particular field while complying with the data type of the particular field. The PIAR management application identifies one or more mappings between (a) the particular data type and (b) one or more data types for fields accepted by actions of plug-in applications. The PIAR management application presents a user interface including one or more candidate PIAR extensions based on the mapping(s). Based on a user selection of a candidate PAIR extension, the PIAR management application executes a PIAR that includes the selected PIAR extension.

Methods, systems, and computer program products for contrasting document-embedded structured data and generating summaries thereof are provided herein. A computer-implemented method includes extracting two or more tables from two or more input documents, wherein each of the two or more input documents comprises structured data and unstructured data; normalizing the two or more extracted tables using one or more alignment techniques; determining at least one of (i) one or more differences and (ii) one or more similarities across the two or more extracted tables by performing a comparison of the two or more normalized tables; deriving one or more insights from the comparison by applying at least one analytical model to the at least one of the one or more determined differences and one or more determined similarities; and outputting at least a portion of the one or more insights to at least one user.

Methods, systems, and computer program products for contrasting document-embedded structured data and generating summaries thereof are provided herein. A computer-implemented method includes extracting two or more tables from two or more input documents, wherein each of the two or more input documents comprises structured data and unstructured data; normalizing the two or more extracted tables using one or more alignment techniques; determining at least one of (i) one or more differences and (ii) one or more similarities across the two or more extracted tables by performing a comparison of the two or more normalized tables; deriving one or more insights from the comparison by applying at least one analytical model to the at least one of the one or more determined differences and one or more determined similarities; and outputting at least a portion of the one or more insights to at least one user.

System, apparatus and method for managing knowledge generated from technical data are disclosed. The method comprising receiving a user query for technical data stored as a knowledge base (842A) on a knowledge-based system (842); determining, by an inference engine (822), a contextual relevance between the user query and the knowledge base (842A), wherein the knowledge base (842A) comprises a query-able framework of the technical data including processed textual sections and indexed images; identifying textual sections and images of the knowledge base (842A) associated with the user query based on the contextual relevance; determining, by the inference engine (822), relevancy of the identified textual sections and indexed images based on frequency of terms in the query with respect to the identified textual sections and the indexed images; and generating, by the inference engine (822), a response (818A) to the user query including extracted textual sections and indexed images having a relevancy score that exceeds a threshold.

An apparatus for decoding and rendering or otherwise processing, manipulating, or outputting a variety of digital data is provided. The apparatus may include a browser engine configured to receive first encoded digital data. The apparatus may include a browser extension configured to intercept the first encoded digital data transmitted in response to a first request from the browser engine. The browser extension may be configured to intercept a first decoder written in a platform-independent syntax for decoding at least a portion of the first encoded digital data. The browser extension may be configured to point the browser engine to one or more local storage locations of the first encoded digital data and the first decoder.

An apparatus for decoding and rendering or otherwise processing, manipulating, or outputting a variety of digital data is provided. The apparatus may include a browser engine configured to receive first encoded digital data. The apparatus may include a browser extension configured to intercept the first encoded digital data transmitted in response to a first request from the browser engine. The browser extension may be configured to intercept a first decoder written in a platform-independent syntax for decoding at least a portion of the first encoded digital data. The browser extension may be configured to point the browser engine to one or more local storage locations of the first encoded digital data and the first decoder.

Aspects of the present invention disclose a method for detecting textual inputs of a user of a social media application and derives personality characteristic insights of a user. The method includes one or more processors identifying a textual data entry to an interactive internet-based application. The method further includes determining a tone of the textual data entry. The method further includes identifying a posting account corresponding to the textual data entry. The method further includes generating a personality profile corresponding to the posting account based on the textual data entry associated with the posting account. The method further includes determining a context of the textual data entry based on semantic features of the textual data entry. The method further includes classifying the personality profile corresponding to the posting account. The method further includes performing a defined action that prevents engagement between the posting account and the interactive internet-based application.

Aspects of the present invention disclose a method for detecting textual inputs of a user of a social media application and derives personality characteristic insights of a user. The method includes one or more processors identifying a textual data entry to an interactive internet-based application. The method further includes determining a tone of the textual data entry. The method further includes identifying a posting account corresponding to the textual data entry. The method further includes generating a personality profile corresponding to the posting account based on the textual data entry associated with the posting account. The method further includes determining a context of the textual data entry based on semantic features of the textual data entry. The method further includes classifying the personality profile corresponding to the posting account. The method further includes performing a defined action that prevents engagement between the posting account and the interactive internet-based application.