Predicting spreadsheet properties

A method comprising:

• receiving a spreadsheet that is based on a template, the spreadsheet including a plurality of cells;
• applying a selected template-specific abstraction to the spreadsheet, wherein the selected template-specific abstraction is selected from a plurality of template-specific abstractions for a corresponding plurality of templates for spreadsheets, and wherein the selected template-specific abstraction defines one or more features of a cell set including one or more cells of the plurality of cells to form an abstracted representation of the spreadsheet, wherein forming the abstracted representation of the spreadsheet comprises replacing content of each cell of the cell set with an abstraction of the content replaced based upon the selected template-specific abstraction, the abstraction of the content comprising one or more symbols that represent a feature of the content replaced;
• forming, for the cell set, an input vector for a machine-learning prediction function from the abstracted representation of the spreadsheet, the machine-learning prediction function configured to output a prediction of one or more properties of the cell set based on the input vector, wherein the machine-learning prediction function is previously trained based on a plurality of previously-created spreadsheets;
• providing the input vector to the machine-learning prediction function;
• and
• outputting the prediction from the machine-learning prediction function.

A device comprising:

• a logic machine including one or more processors;
• and
• a data-holding machine having instructions executable by the logic machine to:
• receive a plurality of previously-created spreadsheets, each previously-created spreadsheet of the plurality of previously-created spreadsheets including a plurality of cell sets, each cell set of the plurality of cell sets including one or more cells;
• for the plurality of previously-created spreadsheets, select a selected template-specific abstraction from a plurality of template-specific abstractions for a corresponding plurality of templates for spreadsheets, wherein the selected template-specific abstraction relates to a structure and properties of the plurality of previously-created spreadsheets and wherein the selected template-specific abstraction defines one or more features of each cell set of the plurality of cell sets, and, for each spreadsheet of the plurality of previously-created spreadsheets, apply the selected template-specific abstraction to form an abstracted representation of the spreadsheet, wherein forming the abstracted representation of the spreadsheet comprises replacing content of each cell of each cell set with an abstraction of the content replaced based upon the selected template-specific abstraction, the abstraction of the content comprising one or more symbols that represent a feature of the content replaced;
• form a set of training vectors for a machine-learning model from the abstracted representation of each spreadsheet of the plurality of previously-created spreadsheets, each training vector of the set of training vectors being formed for a corresponding cell set, wherein the machine-learning model is configured to produce a machine-learning prediction function based on the set of training vectors, wherein the machine-learning prediction function is configured to receive an input vector for a cell set of a spreadsheet as input and output a prediction of one or more properties of the cell set based on the input vector;
• train the machine-learning model with the set of training vectors to produce the machine-learning prediction function;
• and
• output the machine-learning prediction function.