A method for annotating digital conversations is implemented on a first computing device and includes: presenting participant posts from the digital conversation on a display screen of the first computing device, detecting input of at least an indication of an annotation associated with at least one of the participant posts, and presenting the annotation as superimposed on the at least one of the associated participant posts.

Technologies for smart bookmark synchronization may include a smart bookmark device, a bookmark server, and one or more client computing devices. The smart bookmark device transmits an identification of a book to the bookmark server, which determines a format of the book based on the identification. The smart bookmark device captures sensor data indicative of the current reading position of a user in the book and transmits the sensor data to the bookmark server. The sensor data may include image data of a current page in the book. The bookmark server determines bookmark data indicative of the current reading position based on the sensor data. The bookmark server transmits the bookmark data to the smart bookmark device and, in some embodiments, to the client computing devices and/or applications. The client computing devices may synchronize playback of a digital media book with the bookmark data. Other embodiments are described and claimed.

A layered visualization element system includes a processor coupled to a bus, a display device, and a data store including artificial lift evaluation results and executable computer-readable instructions, the processor configured to access the artificial lift evaluation results and to create on the display a multi-layered graphical representation having a first layer depicting in a matrix format of cells an overview of the evaluation results, at least a portion of cells graphically representing results of an evaluation of an artificial lift type to one or more rules, a second layer depicting one or more details of the evaluation associated with the one or more results graphically represented in a user-selected cell element, and a third layer providing textual details of a feasibility score representing an analysis based on the evaluation results. A non-transitory computer-readable medium and a method for displaying artificial lift evaluations are disclosed.

Various systems and methods may aggregate content from one or more poll results database/sources, social media platforms, content sites, and/or other sources. For polling data, each category of results may correspond to direct responses to polling questions. For example, a question may be posed to respondents “Do you have a favorable or unfavorable impression of <Entity>?” in which “<Entity>” corresponds to an entity for which a brand score is being generated. The responses may include the categories such as: “Very Favorable,” “Somewhat Favorable,” “Somewhat Unfavorable,” “Very Unfavorable,” “Never Heard Of,” “Heard Of, but No Opinion.” For non-polling data, the system may parse the content (e.g., words or phrases, graphics such as “emoji”, comments, etc.) to categorize the non-polling data into one of the above categories, which may correspond to a polling category. Brand scores may be generated based on the polling data and/or the non-polling data.

A method and apparatus for performing template-aware document editing is provided. Template-aware document editing may include identifying a document template, the document template including a plurality of rules, generating a document based on the document template, the document including a plurality of document tokens, wherein generating the document includes generating a document tree based on the plurality of rules, altering the document tree in response to user input based on the plurality of rules, and storing or transmitting the document.

Disclosed is a computer method of facilitating drafting of legislation. The method includes receiving a subject indicator from a legislator device. Further, the method includes analyzing the subject indicator. Further, the method includes retrieving a plurality of suggested legal constructs from a storage device based on the analyzing of the subject indicator. Further, the method includes transmitting the plurality of suggested legal constructs to the legislator device. Moreover, the method includes receiving a plurality of selected legal constructs from the legislator device. Further, the method includes analyzing the plurality of selected legal constructs based on a plurality of rules. Further, the method includes generating at least one suggested legislation based on the analyzing of the plurality of selected legal constructs. Further, the method includes storing the at least one suggested legislation in the storage device. Moreover, the method includes transmitting the at least one suggested legislation to the legislator device.

Disclosed is a method of provisioning electronic forms based on natural language. The method includes receiving, using a communication device, a natural language input from a builder device, wherein the natural language input represents one or both of a requested data and a presented data associated with a legal process. Further, the method includes analyzing, using a processing device, the natural language input. Yet further, the method includes generating, using the processing device, an electronic form based on the analyzing of the natural language input, wherein the electronic form comprises an input field configured to receive the requested data and an output field configured to present the presented data. Moreover, the method includes storing, using a storage device, the electronic form. Furthermore, the method includes transmitting, using the communication device, the electronic form to one or more user devices.

Systems and methods are provided for generating classification data which is used for classifying documents. The method includes reading documents in a form of a spreadsheet; collecting cell values in each of the documents; finding one or more common cell values among the collected values; counting, for each of the common cell values, a number of the documents having the common cell value; storing, if the number of the documents is equal to or larger than a predetermined number, the common cell value as a candidate header label in a memory; calculating a distance between cell locations of the candidate header labels in each of the documents; choosing, according to the calculated distance, two or more candidate header labels among the candidate header labels for each of the documents; and storing one or more combinations of the chosen two or more candidate header labels as the classification data.

A system and method for customizing mobile-form applications via a web-based interface for distribution to a mobile device are disclosed. An example method includes providing a customizable mobile-form application template. A web-based interface is provided allowing creation of a customized mobile-form application from the customizable mobile-form application template by selecting a data field for inclusion on the customizable mobile-form application template. The customized mobile-form application may then be distributed to the mobile device.

Classification of collections of items such as words, which are called “document classification,” and more specifically explaining a classification of a document, such as a web-page or website. This can include exemplary procedure, system and/or computer-accessible medium to find explanations, as well as a framework to assess the procedure's performance. An explanation is defined as a set of words (e.g., terms, more generally) such that removing words within this set from the document changes the predicted class from the class of interest. The exemplary procedure system and/or computer-accessible medium can include a classification of web pages as containing adult content, e.g., to allow advertising on safe web pages only. The explanations can be concise and document-specific, and provide insight into the reasons for the classification decisions, into the workings of the classification models, and into the business application itself. Other exemplary aspects describe how explaining documents' classifications can assist in improving the data quality and model performance.