A method of executing a sequence of tasks includes receiving a natural language input indicative of the sequence of tasks. The natural language input may include a first task and a plurality of possible next tasks for the first task. The tasks may each be associated with a playback performance skeleton, indicative of a series of actions to be carried out on a web page. The series of action may have been generated, ahead of time, from a recorded performance of a similar task. The first task may be arranged to be performed. Then, based on a result of the performance of the first task, a successive task from among a plurality of possible next tasks associated with the result of performance of the first task may be selected. The successive task may then be arranged to be performed.

A method of executing a sequence of tasks includes receiving a natural language input indicative of the sequence of tasks. The natural language input may include a first task and a plurality of possible next tasks for the first task. The tasks may each be associated with a playback performance skeleton, indicative of a series of actions to be carried out on a web page. The series of action may have been generated, ahead of time, from a recorded performance of a similar task. The first task may be arranged to be performed. Then, based on a result of the performance of the first task, a successive task from among a plurality of possible next tasks associated with the result of performance of the first task may be selected. The successive task may then be arranged to be performed.

Implementations set forth herein relate to providing selectable autofill suggestions, which correspond to application actions that are at least partially fulfilled using server command data—prior to a user selecting a particular selectable autofill suggestion. Proactively fulfilling command data in this way mitigates latency between user selection of a suggestion and fulfillment of a particular action. Initially, a partial input can be processed to generate autofill suggestions, which can be communicated to a server device for further processing. The autofill suggestions can also be rendered for selection at a touch display interface, thereby allowing a user to select one of the autofill suggestions. As command fulfillment data is provided by the server, the command fulfillment data can be available to a corresponding application(s) in order that any corresponding actions can be at least partially fulfilled prior to user selection.

Implementations set forth herein relate to providing selectable autofill suggestions, which correspond to application actions that are at least partially fulfilled using server command data—prior to a user selecting a particular selectable autofill suggestion. Proactively fulfilling command data in this way mitigates latency between user selection of a suggestion and fulfillment of a particular action. Initially, a partial input can be processed to generate autofill suggestions, which can be communicated to a server device for further processing. The autofill suggestions can also be rendered for selection at a touch display interface, thereby allowing a user to select one of the autofill suggestions. As command fulfillment data is provided by the server, the command fulfillment data can be available to a corresponding application(s) in order that any corresponding actions can be at least partially fulfilled prior to user selection.

A dataset comprising text-based messages can be accessed. Tokens for words and punctuation marks contained in the text-based messages can be generated. Each token corresponds to one word or one punctuation mark. A vector representation for each of a plurality of the tokens can be generated using natural language processing. A sequence of tokens corresponding to the text-based message can be generated for each of a plurality of the text-based messages in the dataset. Ones of the tokens that represent punctuation marks can be identified. An artificial neural network can be trained to predict use of the punctuation marks in sentence structures. The training uses the generated sequence of tokens and the vector representations for the tokens, in the sequence of tokens, that represent the punctuation marks.

Described are methods, systems and computer readable media for dynamic updating of query result displays.

Described are methods, systems and computer readable media for dynamic updating of query result displays.

A translation support device includes a storage device and a control device. The storage device stores therein an original document file in which original document data is recorded, and a translated document file in which translated document data, representing a translated document translated from an original document represented by the original document data, is recorded. The control device includes a processor, and acts as a detector and a generator, when the processor executes a control program. The detector detects, through comparison between the original document file and the translated document file, a same portion contained in common in both of the files, as an untranslated portion. The generator generates untranslated portion information indicating the untranslated portion.

A translation support device includes a storage device and a control device. The storage device stores therein an original document file in which original document data is recorded, and a translated document file in which translated document data, representing a translated document translated from an original document represented by the original document data, is recorded. The control device includes a processor, and acts as a detector and a generator, when the processor executes a control program. The detector detects, through comparison between the original document file and the translated document file, a same portion contained in common in both of the files, as an untranslated portion. The generator generates untranslated portion information indicating the untranslated portion.

Embodiments described include a method for providing context-aware input across network applications. The method can include establishing a plurality of sessions for a user with a plurality of network applications via an embedded browser within a client application. The method can include storing input activities of the user across the plurality of network applications to a data storage. The method can include detecting input focus on an input element of a network application of the plurality of network applications, the input element having a type of data to be inputted. The method can include identifying one or more inputs of the type of data used in another network application of the plurality of network applications stored in the data storage. The method can include providing the one or more inputs from another network application as selectable input for the input element of the network application.