Systems and methods disclosed herein provide context-based application suggestions to a user in real time. A user device can identify a keyword displayed in an application, such as an email application. The user device can request a card from a connector external to the user device. The connector can identify an application that relates to the keyword and determine a current installation status for the application with respect to the user device. The connector can query a management server at which the user device is enrolled to request the installation status. If the application is not installed on the user device, the connector can instruct the user device to prompt the user to install the application. If the application is installed, the connector can instruct the user device to prompt the user to launch the installed application.

Embodiments described herein provide visual-and-language (V+L) systems and methods for learning vision and language representations. Specifically, a method may comprise receiving a training dataset comprising a plurality of image samples and a plurality of text samples; encoding the plurality of image samples into a plurality of encoded image samples and the plurality of text samples into a plurality of encoded text samples; computing a first loss objective based on the plurality of encoded image samples and the plurality of encoded text samples; encoding a first subset of the plurality of encoded image samples and a second subset of the plurality of encoded text samples into a plurality of encoded image-text samples; computing a second loss objective based on the plurality of encoded image-text samples; and updating the V+L model based at least in part on the first loss objective and the second loss objective.

Aspects of the disclosure relate to processing asset transfers. A computing platform may receive an asset transfer request, and may modify a distributed ledger to include a new block corresponding to the asset transfer request. The computing platform may identify, for each of a plurality of consensus methods, a subset of existing blocks that, when used to perform the corresponding consensus method, uses a minimum number of the existing blocks to perform the corresponding consensus method. The computing platform may store, in the distributed ledger, block combinations having the minimum number for each of the plurality of consensus methods, and may execute, using each corresponding identified subset of the existing blocks, each of the plurality of consensus methods. Based on establishing the consensus, the computing platform may direct an event processing platform to process the asset transfer request, which may cause the event processing platform to process the request.

The disclosed embodiments describe methods, systems, and apparatuses for determining user intent. A method is disclosed comprising obtaining a session text of a user; calculating, by the processor, a feature vector based on the session text; determining probabilities that the session text belongs to a plurality of intent labels, the probabilities calculated using a multi-level hierarchal intent classification model, the intent labels assigned to levels in the multi-level hierarchal intent classification model; and assigning a user intent to the session text based on the probabilities.

A technique manages collaborative web sessions (CWS). The technique receives graphical content of a CWS. The technique translates a set of portions of the graphical content into text output. The technique provides the text output to a set of text application services. The set of text application services associate the text output with the CWS.

Disclosed in some examples are methods, systems, devices, and machine-readable mediums that provide an ability for an entity to independently commence, advance, and complete a resource allocation offer in a matter of minutes as opposed to weeks or months after an automated resource pre-committal process. The system, using and incorporating machine learning techniques and algorithms, may have several phases, including a setup phase, resource pre-committal phase, an import phase, a processing phase, a verification phase, a resource allocation offer phase, and a resource allocation phase in which the system allocates resources to a vendor.

A method of sorting baggage at an airport, the method comprising: acquiring a digital image of an identification label for identifying a piece of baggage, the label including an identification code designed to correspond to a set of items of data from a first database compiled as the baggage is being checked in; the method further comprising the following steps: retrieving information that is printed on the label from the image; comparing the information with data that is recorded in a local second database at the airport before the label is printed, the second database being distinct from the first database, the data from the second database being representative of a list of flights to or from the airport; and, in response to a match being detected, transmitting data representative of a flight number corresponding to the piece of baggage to a conveyor system.

One or more highlights of a video stream may be identified. The highlights may be segments of a video stream, such as a broadcast of a sporting event, that are of particular interest to one or more users. According to one method, at least a portion of the video stream may be stored. The portion of the video stream may be compared with templates of a template database to identify the one or more highlights. Each highlight may be a subset of the video stream that is deemed likely to match the one or more templates. The highlights, an identifier that identifies each of the highlights within the video stream, and/or metadata pertaining particularly to the one or more highlights may be stored to facilitate playback of the highlights for the users.

A computer-implemented method of transcribing an audio stream can include transcribing the audio stream using a first transcribing instance having a first predetermined transcription size that is smaller than the total length of the audio stream. The first transcribing instance can provide a plurality of consecutive first transcribed text data snippets of the audio stream and the size of the first transcribed text data snippets can respectively corresponding to the first predetermined transcription size. The audio stream can also be transcribed using at least a second transcribing instance having a second predetermined transcription size that is smaller than the length of the audio stream. The second transcribing instance can provide a plurality of consecutive second transcribed text data snippets each corresponding to the second predetermined transcription size.

An auto-rotation module having a single-layer neural network on a user device can convert a document image to a monochrome image having black and white pixels and segment the monochrome image into bounding boxes, each bounding box defining a connected segment of black pixels in the monochrome image. The auto-rotation module can determine textual snippets from the bounding boxes and prepare them into input images for the single-layer neural network. The single-layer neural network is trained to process each input image, recognize a correct orientation, and output a set of results for each input image. Each result indicates a probability associated with a particular orientation. The auto-rotation module can examine the results, determine what degree of rotation is needed to achieve a correct orientation of the document image, and automatically rotate the document image by the degree of rotation needed to achieve the correct orientation of the document image.