Aspects of the disclosure provide for mechanisms for identification of table partitions in documents using neural networks. A method of the disclosure includes obtaining a plurality of symbol sequences of a document having at least one table, determining a plurality of vectors representative of symbol sequences having at least one alphanumeric character or a table graphics element, processing the plurality of vectors using a first neural network to obtain a plurality of recalculated vectors, determining an association between a first recalculated vector and a second recalculated vector, wherein the first recalculated vector is representative of an alphanumeric sequence and the second recalculated vector is associated with a table partition, and determining, based on the association between the first recalculated vector and the second recalculated vector, an association between the alphanumeric sequence and the table partition.

A chart creation tool is provided that operatively, specifies chart layouts interactively in lieu of programmatically specifying data transformations. Operatively, the herein described systems and methods can convert user-specified layouts into mathematical constraints, and a constraint solver is employed to draw the resulting chart. An illustrative user interface is provided that can allow a user to input data representative of one or more chart layouts through the interaction with one or more illustrative controls of the user interface. Responsive to such interactions (i.e., data input), one or more mathematical constraints can be calculated/generated from the inputted user-specified one or more chart layout input data. Illustratively, the chart layout can comprise a reusable layout.

A chart creation tool is provided that operatively, specifies chart layouts interactively in lieu of programmatically specifying data transformations. Operatively, the herein described systems and methods can convert user-specified layouts into mathematical constraints, and a constraint solver is employed to draw the resulting chart. An illustrative user interface is provided that can allow a user to input data representative of one or more chart layouts through the interaction with one or more illustrative controls of the user interface. Responsive to such interactions (i.e., data input), one or more mathematical constraints can be calculated/generated from the inputted user-specified one or more chart layout input data. Illustratively, the chart layout can comprise a reusable layout.

Presented herein are systems and methods for populating electronic documents, and, in particular, automatically filling columns in a spreadsheet, using a machine learning module. In certain embodiments, the machine learning module comprises natural language processing (NLP) software, for example, Question Answer (QA) software.

Presented herein are systems and methods for populating electronic documents, and, in particular, automatically filling columns in a spreadsheet, using a machine learning module. In certain embodiments, the machine learning module comprises natural language processing (NLP) software, for example, Question Answer (QA) software.

Natural language content can be provided by multiple and various sources. Once in text format, such content may be provided to various systems for further processing to identify one or more propositions. The relationship between each proposition may be identified and ordered according to the identified relationships. A visual display may be generated to illustrate the identified propositions and relationship, as well as identify any propositions that may be missing, unsupported, or other characteristic thereof.

Natural language content can be provided by multiple and various sources. Once in text format, such content may be provided to various systems for further processing to identify one or more propositions. The relationship between each proposition may be identified and ordered according to the identified relationships. A visual display may be generated to illustrate the identified propositions and relationship, as well as identify any propositions that may be missing, unsupported, or other characteristic thereof.

Methods, systems, and devices supporting a data model for emissions analysis are described. Some database systems may store emissions data and support a sustainability application. The sustainability application may display reports that track and analyze data related to carbon emissions. In some cases, underlying data for a report is missing from the database system. The system may support extrapolation techniques to estimate the missing data and aggregate the underlying data—including the extrapolated values—according to a data schema of the database to calculate fields in a report. In some cases, a single data record may be used to generate multiple reports. The system may send one or more results to a user device for display in a user interface (e.g., in one or more dashboards). Additionally or alternatively, the system can display underlying calculations (e.g., report calculations, extrapolation calculations, etc.) in the user interface to support auditing activities.

Described are methods, systems and computer readable media for GUI control elements and associated processing methods.

Described are methods, systems and computer readable media for GUI control elements and associated processing methods.