User interface information related to relevant events of interest is provided. Events can occur anywhere in a document, and may or may not be relevant to a user utilizing an assistive technology (AT) application, such as a screen reader. A provider-side signaling system component determines whether raised events are relevant to the user. In some examples, when an application makes a plurality of attribute changes in a document at once, the signaling provider batches the related events as a single transaction, and generates a generalized annotation describing the changes. The signaling provider further packages the event notification, and sends the event notification to a client-side signaling system component. The signaling client receives the notification, and determines whether to alert the user of the event(s) based on verbosity settings. The AT application is enabled to interpret the event notification and alert the user in a meaningful way.

The present invention discloses a Tibetan character constituent analysis method, a Tibetan sorting method and corresponding devices, and relates to the field of natural language processing. The present invention is proposed to solve the problem that the existing Tibetan sorting methods have no universality or compatibility, which is inconvenient for the use of automatic computer Tibetan sorting. The technical solution provided by the present invention includes: S10, acquiring a Tibetan text to be analyzed; S20, using Tibetan characters in the Tibetan text as the input of a preset finite state automaton group; and S30, acquiring the constituents of the Tibetan characters according to a target finite state automaton, when the target finite state automaton in the finite state automaton group determines that the Tibetan characters in the Tibetan text are correctly spelled.

The present invention allows text analysis and routing of an outgoing message. The system intercepts outgoing messages for analysis by a TAS software module. The module assigns an analytical score to the message, then compares the score to a threshold. If the score is below the threshold, the message is transmitted to its ultimate destination. If not, the message may be routed for correction by the message's composer or quality assurance staff. After such correction, the message new analytical score is generated and compared, and, if necessary, the process repeats again.

The present invention allows text analysis and routing of an outgoing message. The system intercepts outgoing messages for analysis by a TAS software module. The module assigns an analytical score to the message, then compares the score to a threshold. If the score is below the threshold, the message is transmitted to its ultimate destination. If not, the message may be routed for correction by the message's composer or quality assurance staff. After such correction, the message new analytical score is generated and compared, and, if necessary, the process repeats again.

Generating emotionally enhanced transcription of non-textual data and an enriched visualization of transcribed data by capturing non-textual data of a speaker using bio-feedback technology, transcribing it into to a textual format, combining transcribed textual data with emotional state of the speaker to generate the emotionally enhanced transcribed textual data, and presenting emotionally enhanced transcribed textual data through an enriched visualization including color-coding transcribed textual data to identify mistakes in the transcribed data.

Generating emotionally enhanced transcription of non-textual data and an enriched visualization of transcribed data by capturing non-textual data of a speaker using bio-feedback technology, transcribing it into to a textual format, combining transcribed textual data with emotional state of the speaker to generate the emotionally enhanced transcribed textual data, and presenting emotionally enhanced transcribed textual data through an enriched visualization including color-coding transcribed textual data to identify mistakes in the transcribed data.

Various embodiments illustrated herein disclose a method that includes receiving a plurality of images from an image capturing unit. Thereafter, an image evaluation process is executed on each of plurality of sections in each of the plurality of images. The image evaluation process includes performing optical character recognition (OCR) on each of the plurality of sections in each of the plurality of images to generate text corresponding to the plurality of respective sections. Further, the image evaluation process includes querying a linguistic database to identify one or more errors in the generated text. Further, the method includes modifying one or more image characteristics of each of the plurality of images and repeating the execution of the image evaluation process on the modified plurality of images until at least the calculated statistical score is less than a pre-defined statistical score threshold.

A computer system configured for autonomous learning of entity values is provided. The computer system includes a memory that stores associations between entities and fields of response data. The computer system also includes a processor configured to receive a request to process an intent; generate a request to fulfill the intent; transmit the request to a fulfillment service; receive, from the fulfillment service, response data specifying values of the fields; identify the values of the fields within the response data; identify the entities via the associations using the fields; store, within the memory, the values of the fields as values of the entities; and retrain a natural language processor using the values of the entities.

A computer system configured for autonomous learning of entity values is provided. The computer system includes a memory that stores associations between entities and fields of response data. The computer system also includes a processor configured to receive a request to process an intent; generate a request to fulfill the intent; transmit the request to a fulfillment service; receive, from the fulfillment service, response data specifying values of the fields; identify the values of the fields within the response data; identify the entities via the associations using the fields; store, within the memory, the values of the fields as values of the entities; and retrain a natural language processor using the values of the entities.

A chat input identifier may receive various chat inputs based on voice or text inputs from a user. The chat input identifier may apply different filters to the chat inputs to identify one or more chat correction pairs (e.g., chat input with errors, corrected chat input) from among the plurality of chat inputs. The chat correction pairs are used to train an auto-correction model. The trained auto-correction model receives a given chat input that has one or more errors. The auto-correction model processes the given chat input to generate a corrected version of the given chat input (without the need to obtain a correction from the user). The corrected chat input is then provided to a dialog-driven application.