Methods and systems are provided for facilitating identification of fillable regions and/or data associated therewith. In embodiments, a candidate fillable region indicating a region in a form that is a candidate for being fillable is obtained. Textual context indicating text from the form and spatial context indicating positions of the text within the form are also obtained. Fillable region data associated with the candidate fillable region is generated, via a machine learning model, using the candidate fillable region, the textual context, and the spatial context. Thereafter, a fillable form is generated using the fillable region data, the fillable form having one or more fillable regions for accepting input.

The present disclosure relates to a system and method for automatically obtaining and processing logistics requests is provided. Embodiments include automatically identifying, using a processor, a logistics request from a logistics request receiver. In response to automatically identifying, embodiments include extracting information from the logistics request and providing the extracted information to an automated quoting validator. Embodiments also include automatically generating at least one quote for the request at the automated quoting validator based upon, at least in part, one or more user defined parameters.

The present disclosure relates to a system and method for automatically obtaining and processing logistics requests is provided. Embodiments include automatically identifying, using a processor, a logistics request from a logistics request receiver. In response to automatically identifying, embodiments include extracting information from the logistics request and providing the extracted information to an automated quoting validator. Embodiments also include automatically generating at least one quote for the request at the automated quoting validator based upon, at least in part, one or more user defined parameters.

A system, method and computer-readable medium are provided to enable digital bank endorsement. A digital image of a back side of a check may be placed in a computer memory. Appropriate coordinates for a bank endorsement may be determined. A bank endorsement may be automatically generated. The digital image may then be electronically altered by overlaying, merging, or rendering text of the generated bank endorsement. A modified digital image may be combined with an image of the front side of the check and stored and/or exported to check clearing operations.

A system, method and computer-readable medium are provided to enable digital bank endorsement. A digital image of a back side of a check may be placed in a computer memory. Appropriate coordinates for a bank endorsement may be determined. A bank endorsement may be automatically generated. The digital image may then be electronically altered by overlaying, merging, or rendering text of the generated bank endorsement. A modified digital image may be combined with an image of the front side of the check and stored and/or exported to check clearing operations.

Interfaces and systems are provided for harvesting ground truth from forms to be used in training models based on key-value pairings in the forms and to later use the trained models to identify related key-value pairings in new forms. Initially, forms are identified and clustered to identify a subset of forms to label with the key-value pairings. Users provide input to identify keys to use in labeling and then select/highlight text from forms that are presented concurrently with the keys in order to associate the highlighted text with the key(s) as the corresponding key-value pairing(s). After labeling the forms with the key-value pairings, the key-value pairing data is used as ground truth for training a model to independently identify the key-value pairing(s) in new forms. Once trained, the model is used to identify the key-value pairing(s) in new forms.

Interfaces and systems are provided for harvesting ground truth from forms to be used in training models based on key-value pairings in the forms and to later use the trained models to identify related key-value pairings in new forms. Initially, forms are identified and clustered to identify a subset of forms to label with the key-value pairings. Users provide input to identify keys to use in labeling and then select/highlight text from forms that are presented concurrently with the keys in order to associate the highlighted text with the key(s) as the corresponding key-value pairing(s). After labeling the forms with the key-value pairings, the key-value pairing data is used as ground truth for training a model to independently identify the key-value pairing(s) in new forms. Once trained, the model is used to identify the key-value pairing(s) in new forms.

There is provided a method and server for estimating an uncertainty parameter of a sequence of computer-implemented models comprising at least one machine learning algorithm (MLA). A set of labelled digital documents is received, which is to be processed by the sequence of models. For a given model of the sequence of models, at least one of a respective set of input features, a respective set of model-specific features and a respective set of output features are received. The set of predictions output by the sequence of models is received. A second MLA is trained to estimate uncertainty of the sequence of models based on the set of labelled digital documents, and the at least one of the respective set of input features, the respective set of model-specific features, the respective set of output features, and the set of predictions.

Techniques are described including displaying an interface associated with a computing device, the interface being configured to present a form including a text entry box, receiving data from an activity information pipeline associated with a self-reporting service, the activity information pipeline having a source of activity information associated with a tool configured to geotag the activity information and another source of associated with a mobile sensor configured to capture other activity information geotagged by the tool, the other activity information being processed by the self-reporting service to deprioritize a suggestion over another suggestion, displaying, in response to selection of the text entry box, a suggestion indicative of a journey and identified as prioritized and presented proximate to the center of the interface of the display device, and dynamically adjusting the suggestion while prioritizing display of the suggestion over a generic suggestion.

Techniques are described including displaying an interface associated with a computing device, the interface being configured to present a form including a text entry box, receiving data from an activity information pipeline associated with a self-reporting service, the activity information pipeline having a source of activity information associated with a tool configured to geotag the activity information and another source of associated with a mobile sensor configured to capture other activity information geotagged by the tool, the other activity information being processed by the self-reporting service to deprioritize a suggestion over another suggestion, displaying, in response to selection of the text entry box, a suggestion indicative of a journey and identified as prioritized and presented proximate to the center of the interface of the display device, and dynamically adjusting the suggestion while prioritizing display of the suggestion over a generic suggestion.