In clinical documentation, mere documentation of a condition in a patient's records may not be enough. To be considered sufficiently documented, the patient's record needs to show that no documentation drop-offs (DDOs) have occurred over the course of the patient's stay. However, DDOs can be extremely difficult to detect. To solve this problem, the invention trains time-sensitive deep learning (DL) models on a per condition basis using actual and/or synthetic patient data. Utilizing an ontology, grouped concepts can be generated on the fly from real-time hospital data and used to generate time-series data that can then be analyzed by trained time-sensitive DL models to determine whether a DDO for a condition has occurred during the stay. Non-time-sensitive models can be used to detect all the conditions documented during the stay. Outcomes from the models can be compared to determine whether to notify a user that a DDO has occurred.

Systems and methods of the present disclosure enable context-aware haptic error notifications. The systems and methods include a processor to receive input segments into a software application from a character input component and determine a destination. A context identification model predicts a context classification of the input segments based at least in part on the software application and the destination. Potential errors are determined in the input segments based on the context classification. An error characterization machine learning model determines an error type classification and an error severity score associated with each potential error and a haptic feedback pattern is determined for each potential error based on the error type classification and the error severity score of each potential error of the one or more potential errors. And a haptic event latency is determined based on the error type classification and the error severity score of each potential error.

In a computer-implemented method for information retrieval and a processing system of a computer-implemented information retrieval system, an input text is received by a Natural Language Processing, NLP, suite, wherein the NLP suite comprises a plurality of models. At least one of the plurality of models is a model trained using selected features. The selected features are determined using a feature selection process. The input text is processed by each one of the plurality of models. An intermediate representation of the input text is generated by each one of the plurality of models. An enhanced representation of the input text is generated by combining a plurality of the generated intermediate representations. Information is retrieved based on the enhanced representation of the input text.

In a computer-implemented method for information retrieval and a processing system of a computer-implemented information retrieval system, an input text is received by a Natural Language Processing, NLP, suite, wherein the NLP suite comprises a plurality of models. At least one of the plurality of models is a model trained using selected features. The selected features are determined using a feature selection process. The input text is processed by each one of the plurality of models. An intermediate representation of the input text is generated by each one of the plurality of models. An enhanced representation of the input text is generated by combining a plurality of the generated intermediate representations. Information is retrieved based on the enhanced representation of the input text.

Methods and systems are disclosed herein for using anomaly detection in timeseries data of user sentiment to detect incidents in computing systems and identify events within an enterprise. An anomaly detection system may receive social media messages that include a timestamp indicating when each message was published. The system may generate sentiment identifiers for the social media messages. The sentiment identifiers and timestamps associated with the social media messages may be used to generate a timeseries dataset for each type of sentiment identifier. The timeseries datasets may be input into an anomaly detection model to determine whether an anomaly has occurred. The system may retrieve textual data from the social media messages associated with the detected anomaly and may use the text to determine a computing system or event associated with the detected anomaly.

Methods and systems are disclosed herein for using anomaly detection in timeseries data of user sentiment to detect incidents in computing systems and identify events within an enterprise. An anomaly detection system may receive social media messages that include a timestamp indicating when each message was published. The system may generate sentiment identifiers for the social media messages. The sentiment identifiers and timestamps associated with the social media messages may be used to generate a timeseries dataset for each type of sentiment identifier. The timeseries datasets may be input into an anomaly detection model to determine whether an anomaly has occurred. The system may retrieve textual data from the social media messages associated with the detected anomaly and may use the text to determine a computing system or event associated with the detected anomaly.

An audio stream is detected during a communication session with a user. Natural language processing on the audio stream is performed to update a set of attributes by supplementing the set of attributes based on attributes derived from the audio stream. A set of filter values is updated based on the updated set of attributes. The updated set of filter values is used to query a set of databases to obtain datasets. A probabilistic program is executed during the communication session by determining a set of probability parameters characterizing a probability of an anomaly occurring based on the datasets and the set of attributes. A determination is made if whether the probability satisfies a threshold. In response to a determination that the probability satisfies the threshold, a record is updated to identify the communication session to indicate that the threshold is satisfied.

A method and apparatus for generating a user intention understanding satisfaction evaluation model, a method and apparatus for evaluating a user intention understanding satisfaction, an electronic device and a storage medium are provided, relating to intelligent voice recognition and knowledge graphs. The method for generating a user intention understanding satisfaction evaluation model is: acquiring a plurality of sets of intention understanding data, at least one set of which comprises a plurality of sequences corresponding to multi-round behaviors of an intelligent device in multi-round man-machine interactions; and learning the plurality of sets of intention understanding data through a first machine learning model, to obtain the user intention understanding satisfaction evaluation model after the learning, wherein the user intention understanding satisfaction evaluation model is configured to evaluate user intention understanding satisfactions of the intelligent device in the multi-round man-machine interactions according to the plurality of sequences corresponding to the multi-round man-machine interactions.

A method for aligning a translation of original caption data with an audio portion of a video is provided. The method includes identifying, by a processing device, original caption data for a video that includes a plurality of caption character strings. The processing device identifies speech recognition data that includes a plurality of generated character strings and associated timing information for each generated character string. The processing device maps the plurality of caption character strings to the plurality of generated character strings using assigned values indicative of semantic similarities between character strings. The processing device assigns timing information to the individual caption character strings based on timing information of mapped individual generated character strings. The processing device aligns a translation of the original caption data with the audio portion of the video using assigned timing information of the individual caption character strings.

A method for aligning a translation of original caption data with an audio portion of a video is provided. The method includes identifying, by a processing device, original caption data for a video that includes a plurality of caption character strings. The processing device identifies speech recognition data that includes a plurality of generated character strings and associated timing information for each generated character string. The processing device maps the plurality of caption character strings to the plurality of generated character strings using assigned values indicative of semantic similarities between character strings. The processing device assigns timing information to the individual caption character strings based on timing information of mapped individual generated character strings. The processing device aligns a translation of the original caption data with the audio portion of the video using assigned timing information of the individual caption character strings.