A system and method for creating a dynamic electronic form are described. The system may include one or more processors that cause the system to perform create an electronic form with one or more data entry fields. The system may also obtain access to a plurality of datasets, where each dataset may include multiple entry fields and integrate at least one identified dataset with the electronic form. The system may further suggest at least one data input in the data entry field based on information input in the data entry field by a user. The data entry input suggested may be sourced from the identified dataset integrated to the electronic form.

A system and method for creating a dynamic electronic form are described. The system may include one or more processors that cause the system to perform create an electronic form with one or more data entry fields. The system may also obtain access to a plurality of datasets, where each dataset may include multiple entry fields and integrate at least one identified dataset with the electronic form. The system may further suggest at least one data input in the data entry field based on information input in the data entry field by a user. The data entry input suggested may be sourced from the identified dataset integrated to the electronic form.

Approaches presented herein enable identification of routine communication content. More specifically, a communication between one or more users is received. Words or phrases in the communication that are contained in a database of words or phrases related to misconduct are identified. The identified words or phrases are removed from the communication to create a set of remaining words. The set of remaining words are analyzed to predict the likelihood of the removed words or phrases appearing in the communication, such that a confidence level of the prediction is determined. In response to the determined confidence level being high, the identified words or phrases in the communication are classified as routine.

A method predicts custom fields from text. Transaction text is normalized from transaction data to generate normalized text. A field prediction and a type prediction are selected using prediction data and the normalized text. The prediction data is generated using a machine learning model trained to identify field predictions from free form text. The field prediction and the type prediction are presented to a client device. In response to user input from the client device, the transaction data is updated with the field prediction.

A method predicts custom fields from text. Transaction text is normalized from transaction data to generate normalized text. A field prediction and a type prediction are selected using prediction data and the normalized text. The prediction data is generated using a machine learning model trained to identify field predictions from free form text. The field prediction and the type prediction are presented to a client device. In response to user input from the client device, the transaction data is updated with the field prediction.

A content management system obtains at least a portion of a meeting transcript based on an audio stream of a meeting attended by a plurality of users, the meeting transcript obtained in an ongoing manner as words are uttered during the meeting. The content management system detects text entered by a user of the plurality of users into a content item during the meeting. The content management system matches the detected text to at least part of the at least the portion of the meeting transcript. The content management system provides the at least part of the at least the portion of the meeting transcript to the user as a suggested subsequent text.

A content management system obtains at least a portion of a meeting transcript based on an audio stream of a meeting attended by a plurality of users, the meeting transcript obtained in an ongoing manner as words are uttered during the meeting. The content management system detects text entered by a user of the plurality of users into a content item during the meeting. The content management system matches the detected text to at least part of the at least the portion of the meeting transcript. The content management system provides the at least part of the at least the portion of the meeting transcript to the user as a suggested subsequent text.

The present disclosure provides a signal processing device that includes: a signal collector configured to obtain an image to be processed, a signal collector configured to collect an input signal, an instruction converter configured to convert the signal into an image processing instruction according to a target signal instruction conversion model, an image processor configured to edit the image to be processed according to the image processing instruction and a target image processing model to obtain a result image. Examples taught in the present disclosure implements a user command to process images, which saves users' time spent in learning image processing software prior to image processing and improves user experience.

The present disclosure provides a signal processing device that includes: a signal collector configured to obtain an image to be processed, a signal collector configured to collect an input signal, an instruction converter configured to convert the signal into an image processing instruction according to a target signal instruction conversion model, an image processor configured to edit the image to be processed according to the image processing instruction and a target image processing model to obtain a result image. Examples taught in the present disclosure implements a user command to process images, which saves users' time spent in learning image processing software prior to image processing and improves user experience.

A system and method are disclosed for creating solution design blueprints. A solution design blueprint is a machine-readable data structure that includes a conceptual design model for an application framework. A user interface is configured to receive a plain language textual request from a user that describes a desired application or solution to a problem. Artificial Intelligence is leveraged to fit the textual request to semantic data models and map elements of the textual request to components of a design library. The resulting solution design blueprint can be presented to a user, and the user interface can be used to provide feedback related to the solution design blueprint that can be utilized to update machine learning algorithms and/or neural networks. In some embodiments, the solution design blueprint can be converted to an application framework that is provided to the use in an integrated development environment of the user interface.