Systems and methods are described for generating learning roadmaps from unstructured information. The systems and methods may provide for extracting a plurality of named entities from one or more corpora of information, constructing a graph based on the named entities, inducing a subgraph from the graph based on a target named entity, wherein the subgraph includes a subset of the named entities, ordering the subset of the named entities based on the subgraph, and generating a learning roadmap for the target named entity based on the ordering.

Methods and apparatuses are provided for sharing content between an electronic device and an external device. A web document including pieces of content is received. At least one piece of content executable in the external device is determined from among the pieces of content based on corresponding types of the pieces of content and function information of the external device. Information on the at least one piece of content is transmitted to the external device.

In an aspect, a method includes: receiving, via a communications module and from a computing device, a signal comprising image data representing a first document; automatically analyzing text in the first document based on stored classification data to identify a first parameter from the text in the first document; comparing the first parameter to a second parameter, the second parameter being obtained from a data store and being associated with a second document; determining annotation data based on the comparison, the annotation data determined based on the first parameter and the second parameter; and providing a signal that includes an instruction to cause the annotation data to be overlaid on a display of the computing device, the instruction including marker data identifying a location associated with the first document and influencing a location of the annotation in the display.

In an embodiment, an image reception system is communicatively coupled to an image analysis system and is configured to receive a digital image and analyze the pixels of the digital image to determine one or more regions in the digital image. For each region in the one or more regions in the digital image, the image analysis system recognizes the content in the region. A document creation system communicatively coupled to the image analysis system is configured to create a digital document based on the recognized content for the one or more regions. In some embodiments, the image analysis system is further configured to analyze the digital image to detect one or more of the following: region markers, tables, headers.

Techniques described herein relate to facilitating end-to-end multilingual communications with automated assistants. In various implementations, speech recognition output may be generated based on voice input in a first language. A first language intent may be identified based on the speech recognition output and fulfilled in order to generate a first natural language output candidate in the first language. At least part of the speech recognition output may be translated to a second language to generate an at least partial translation, which may then be used to identify a second language intent that is fulfilled to generate a second natural language output candidate in the second language. Scores may be determined for the first and second natural language output candidates, and based on the scores, a natural language output may be selected for presentation.

An example computer-implemented method embodying the disclosed technology comprises digitally storing a plurality of digital objects comprising first type digital objects and second type digital objects, each digital object comprising an electronic digital representation of natural language text, and certain first type digital objects each being associated with exactly one second type digital object; programmatically generating a corpus of digital documents from the plurality of digital objects; programmatically embedding the electronic digital representation of natural language text of each first type digital object not associated with one of the second type digital objects, thereby generating a second set of vectors; programmatically determining, for each of the second set of vectors, a corresponding set of nearest neighbor vectors from the first set of vectors; programmatically generating recommendation data indicating potential new associations between first type digital objects and second type digital objects; and displaying a representation of the recommendation data.

Systems and methods involving a hub platform, communication network, and memory configured for processing data involving time-stamped/time-sensitive aspects and other features are disclosed. In one example, an illustrative system may comprise a hub computer platform and associated computing components configured to generate a plurality of portals including at least first and second portals, including aspects such as automatically updating information displayed therein in real-time between portals, automatically attaching and/or processing timestamps and identifier information that are attached to orders upon receipt and acceptance thereof, generating and/or processing information of an order file, generating, updating and/or interactively displaying various tabular and/or graphical information such as order information that is automatically processed based on timestamps and/or other information, and/or generating other GUI features that, for example, may graphically display and automatically update level-of-involvement information.

Techniques for triggering an action associated with a task items are provided. A task item associated with a triggering criterion is provided. The triggering criterion requires an occurrence of any of a plurality of communication events. The plurality of communication events includes a telephone call. In some implementations, at least two of the plurality of communication events are of different communication types. In some implementations, the different types are telephone calls, emails, and text messages. The occurrence of a first communication event of the plurality of communication events is detected. It is determined the triggering criterion is satisfied. In response to the determining, a notification associated with the task item is caused to be presented to a user of the electronic device.

Techniques for triggering an action associated with a task items are provided. A task item associated with a triggering criterion is provided. The triggering criterion requires an occurrence of any of a plurality of communication events. The plurality of communication events includes a telephone call. In some implementations, at least two of the plurality of communication events are of different communication types. In some implementations, the different types are telephone calls, emails, and text messages. The occurrence of a first communication event of the plurality of communication events is detected. It is determined the triggering criterion is satisfied. In response to the determining, a notification associated with the task item is caused to be presented to a user of the electronic device.

Embodiments herein describe an automated process for identifying tasks that can be converted from primarily being driven by a human agent into bot-driven RPAs. This disclosure describes techniques for automatically generating RPAs by monitoring the actions of the agent and identifying corresponding events. These events can be performed on unstructured data sources and structured data sources. The events are clustered into different tasks where pattern detection techniques are used to identify common (or overlapping) events and causal relationships between the events. Using these causal relationships, the system can map events performed on unstructured data sources to events performed on structured data source. The system can then generate respective bot-RPAs for the tasks. The system can monitor, in real-time, data received from unstructured and/or structured data sources and when it identifies data for one of the bot-RPAs, automatically execute the RPA to perform the task.