Adobe, Inc.

Arvind Heda

Harpreet Singh

Wei Zhang

Shiladitya Bose

In embodiments of iterative detection of forms-usage patterns, a data analytics application can be implemented to receive analytics data associated with one or more data forms that each include data-entry fields displayed in a user interface, where the data-entry fields are designed for data entry, such as by a user of a computing device. The data analytics application can determine data-entry problems with the data-entry fields of the data forms based on the analytics data, as well as identify a critical data-entry problem with a data-entry field of a data form, the critical data-entry problem being identified as one of the determined data-entry problems. A distribution scale can be generated and displayed to depict the determined data-entry problems along with the critical data-entry problem. The data form can also be displayed in a preview mode with the analytics data displayed on the data form itself to indicate the data-entry problems.

Iterative detection of forms-usage patterns

Systems and methods provide for classification and ranking of features for a hierarchical dataset. A hierarchical schema of features from the dataset is accessed. A hierarchical rank is assigned to each feature based on its schema level in the hierarchical schema. Additionally, a semantic rank is assigned to each feature using a semantic model having ranked semantic contexts. The semantic rank of a feature is assigned by identifying a semantic context of the feature and assigning the rank of the semantic context as the semantic rank of the feature. A rank is computed for each feature as a function of its hierarchical rank and semantic rank.

Unified classification and ranking strategy

The present invention provides recommendation of top scoring form fragments to a form designer. A plurality of form fragments may be stored in a form repository, each form fragment including user-defined form fragment attributes, and analytics data for the form fragments may be collected over a period of time to calculate a performance score for each of the form fragments. When an author searches for a form fragment, at least one matching form fragment from the form repository may be obtained based on the search query or criteria inputted by the author using natural language processing (NLP). The matching form fragments may be ordered based on the performance score, where higher performing form fragments are listed first. The ordered form fragments may be displayed on a device associated with the author so that the form fragments may be used by the author when authoring forms.

Recommending form fragments

Improved authoring techniques enable digital forms to be configured so as to facilitate subsequent analysis of how specific consumer segments interact with such forms. As a form author defines and manipulates the fields that comprise a form, selected fields can be designated as dimensions across which metrics can be analyzed. Depending on the particular type of data being collected, author-designated dimensions are optionally transformed into more meaningful categories. For instance, a 'household income' field can be transformed into lower- and higher-income segments. If the form author wishes to later analyze metrics such as interaction time and errors encountered, consumers can be segmented on the basis of such dimensions. For example, the form author may understand which income segment requires the most time to complete a form. In general, this provides information with respect to how certain segments interact with a digital form, thereby enabling form authors to improve consumer experience.

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Ghaziabad, India

Arvind Heda