Abstract: A next operation in a workflow corresponding to a subject matter is automatically generated. A subject matter data record is received, including one or more historical operations previously executed in the workflow and a workflow template including one or more dynamic workflow nodes. At least one next operation is programmatically generated from the one or more dynamic workflow nodes based on the one or more historical operations previously executed in the workflow, wherein the at least one next operation is parameterized based on content in the subject matter data record. The at least one programmatically generated next operation is output into the workflow for execution.

Abstract: Text coherence is classified by receiving a multiword text string into a machine learning model, determining, by the machine learning model, semantic probability data representing a probability that a word of the received multiword text string is semantically correlated to one or more other words in the multiword text string, determining, by the machine learning model, an inferential aggregate perplexity score of the multiword text string, based on the determined semantic probability data, outputting, from the machine learning model, the inferential aggregate perplexity score, and classifying a coherence of the multiword text string based on whether the outputted inferential aggregate perplexity score satisfies a coherence condition, wherein the coherence condition is based on a predefined coherence score.

Abstract: A method of generating keyword-based dialogue summaries is provided. The method includes inputting a transcript of an audio conversation and a keyword into a machine learning model trained based on encodings representing the keyword and the transcript, generating computer-generated text different from and semantically descriptive of the transcript and semantically associated with the keyword, and outputting the computer-generated text in association with a selectable item selectable for inclusion of the computer-generated text in displayed text representing the transcript, the selectable item associated with the keyword.

Abstract: Methods for speaker role determination and scrubbing identifying information are performed by systems and devices. In speaker role determination, data from an audio or text file is divided into respective portions related to speaking parties. Characteristics classifying the portions of the data for speaking party roles are identified in the portions to generate data sets from the portions corresponding to the speaking party roles and to assign speaking party roles for the data sets. For scrubbing identifying information in data, audio data for speaking parties is processed using speech recognition to generate a text-based representation. Text associated with identifying information is determined based on a set of key words/phrases, and a portion of the text-based representation that includes a part of the text is identified. A segment of audio data that corresponds to the identified portion is replaced with different audio data, and the portion is replaced with different text.

Abstract: The disclosure herein describes determining topics of communication transcripts using trained summarization models. A first communication transcript associated with a first communication is obtained and divided into a first set of communication segments. A first set of topic descriptions is generated based on the first set of communication segments by analyzing each communication segment of the first set of communication segments with a generative language model. A summarization model is trained using the first set of communication segments and associated first set of topic descriptions as training data. The trained summarization model is then applied to a second communication transcript and, based on applying the trained summarization model to the second communication transcript, a second set of topic descriptions of the second communication transcript is generated.

Abstract: A recommendation system for recommending a target feature value for a target feature for a target deployment is provided. The recommendation system, for each of a plurality of deployments, collects feature values for the features of that deployment. The recommendation system then generates a model for recommending a target feature value for the target feature based on the collected feature values of the features for the deployments. The recommendation system applies the model to the features of the target deployment to identify a target feature value for the target feature. The recommendation system then provides the identified target feature value as a recommendation for the target feature for the target deployment.

Abstract: A recommendation system for recommending a target feature value for a target feature for a target deployment is provided. The recommendation system, for each of a plurality of deployments, collects feature values for the features of that deployment. The recommendation system then generates a model for recommending a target feature value for the target feature based on the collected feature values of the features for the deployments. The recommendation system applies the model to the features of the target deployment to identify a target feature value for the target feature. The recommendation system then provides the identified target feature value as a recommendation for the target feature for the target deployment.

Abstract: The disclosure herein describes determining topics of communication transcripts using trained summarization models. A first communication transcript associated with a first communication is obtained and divided into a first set of communication segments. A first set of topic descriptions is generated based on the first set of communication segments by analyzing each communication segment of the first set of communication segments with a generative language model. A summarization model is trained using the first set of communication segments and associated first set of topic descriptions as training data. The trained summarization model is then applied to a second communication transcript and, based on applying the trained summarization model to the second communication transcript, a second set of topic descriptions of the second communication transcript is generated.

Abstract: The disclosure herein describes reducing training bias in outputs generated by a generative language model. A communication segment associated with a communication is obtained by at least one processor of a generative language model. An output value associated with the communication segment is generated by the generative language model. The output value is mapped to a set of training bias values associated with the generative language model and based on the mapping of the output value to a training bias value of the set of training bias values, an alternative output value is generated. The alternative output value is used in a generated segment output for the communication segment. The accuracy of segment outputs generated by the generative language model is improved through reducing or eliminating its training biases.

Abstract: In various embodiments, methods and systems for implementing a media management system, for video data processing and adaptation data generation, are provided. At a high level, a video data processing engine relies on different types of video data properties and additional auxiliary data resources to perform video optical character recognition operations for recognizing characters in video data. In operation, video data is accessed to identify recognized characters. A video OCR operation to perform on the video data for character recognition is determined from video character processing and video auxiliary data processing. Video auxiliary data processing includes processing an auxiliary reference object; the auxiliary reference object is an indirect reference object that is a derived input element used as a factor in determining the recognized characters. The video data is processed based on the video OCR operation and based on processing the video data, at least one recognized character is communicated.

Abstract: Methods for speaker role determination and scrubbing identifying information are performed by systems and devices. In speaker role determination, data from an audio or text file is divided into respective portions related to speaking parties. Characteristics classifying the portions of the data for speaking party roles are identified in the portions to generate data sets from the portions corresponding to the speaking party roles and to assign speaking party roles for the data sets. For scrubbing identifying information in data, audio data for speaking parties is processed using speech recognition to generate a text-based representation. Text associated with identifying information is determined based on a set of key words/phrases, and a portion of the text-based representation that includes a part of the text is identified. A segment of audio data that corresponds to the identified portion is replaced with different audio data, and the portion is replaced with different text.

Abstract: Methods for speaker role determination and scrubbing identifying information are performed by systems and devices. In speaker role determination, data from an audio or text file is divided into respective portions related to speaking parties. Characteristics classifying the portions of the data for speaking party roles are identified in the portions to generate data sets from the portions corresponding to the speaking party roles and to assign speaking party roles for the data sets. For scrubbing identifying information in data, audio data for speaking parties is processed using speech recognition to generate a text-based representation. Text associated with identifying information is determined based on a set of key words/phrases, and a portion of the text-based representation that includes a part of the text is identified. A segment of audio data that corresponds to the identified portion is replaced with different audio data, and the portion is replaced with different text.

Abstract: Methods, systems, and media are shown for reducing the vulnerability of user accounts to attack that involve creating a rule for a user account that includes a permitted parameter corresponding to a user account activity property, monitoring the account activity of the user account. If it is determined that account activity property is inconsistent with the permitted parameter, then the user account is disabled. An example of a permitted parameter is a permitted time period, such as a start time, an end time, a recurrence definition, a days of the week definition, a start date, an end date, and a number of occurrences definition. Other examples are a physical parameter, such as a permitted geographic location, device, or network, or a permitted usage parameter, such as a permitted application, data access, or domain.

Abstract: The disclosure herein describes providing action item information for a current activity based on similarity with past activities. Activity attributes indicative of an activity outcome are identified, and a random forest classifier based on the identified activity attributes is generated. The random forest classifier classifies an activity based on the activity attributes. Similarity factors associated with the current activity and past activities are calculated based on the random forest classifier. Based on the similarity factors, data value ranges of performance indicators of past activities associated with the activity outcome are determined. Based on comparing the determined data value ranges to performance indicator data values of the current activity, action item information associated with the performance indicator data values of the current activity is provided.

Abstract: Data from social networking applications and other applications that can be used to communicate are combined for a user to generate a graph of the various relationships that the user has with other users in the social networking applications and other applications. In addition, the behaviors of each user with respect to communicating through the various social networking applications and other applications are monitored to generate task data that describes user preferences for communicating using each social networking application or other application for different tasks. At a later time, when a user is looking to connect with another user for an indicated task such as networking, the graph can be used to recommend paths to other users in the various social networking applications and other applications, and the generated task data can be used to rank the recommended paths based on the indicated task.

Abstract: Disclosed herein is a system and method for generating a user profile for a user based on the way the user interacts with consumable content in an application, such as an electronic book in an e-reader. The actions of the user with regards to the content are monitored and the associated with characteristics of the content. This association is used to build the user's profile of their likes and dislikes which are then provided to a recommender system to provide more relevant and personalize recommendations.

Abstract: Methods for speaker role determination and scrubbing identifying information are performed by systems and devices. In speaker role determination, data from an audio or text file is divided into respective portions related to speaking parties. Characteristics classifying the portions of the data for speaking party roles are identified in the portions to generate data sets from the portions corresponding to the speaking party roles and to assign speaking party roles for the data sets. For scrubbing identifying information in data, audio data for speaking parties is processed using speech recognition to generate a text-based representation. Text associated with identifying information is determined based on a set of key words/phrases, and a portion of the text-based representation that includes a part of the text is identified. A segment of audio data that corresponds to the identified portion is replaced with different audio data, and the portion is replaced with different text.

Abstract: Aspects of the technology described herein improve an object recognition system by specifying a type of picture that would improve the accuracy of the object recognition system if used to retrain the object recognition system. The technology described herein can take the form of an improvement model that improves an object recognition model by suggesting the types of training images that would improve the object recognition model's performance For example, the improvement model could suggest that a picture of a person smiling be used to retrain the object recognition system. Once trained, the improvement model can be used to estimate a performance score for an image recognition model given the set characteristics of a set of training of images.

Abstract: Technology is disclosed for providing dynamic identification and extraction or tagging of contextually-coherent text blocks from an electronic document. In an embodiment, an electronic document may be parsed into a plurality of content tokens that each corresponds to a portion of the electronic document, such as a sentence or a paragraph. Employing a sliding window approach, a number of token groups are independently analyzed, where each group of tokens has a different number of tokens included therein. Each token group is analyzed to determine confidence scores for various determinable contexts based on content included in the token set. The confidence scores can then be processed for each token group to determine an entropy score for the token group. In this way, one of the analyzed token groups can be selected as a representative text block that corresponds to one of the plurality of determinable contexts.

Abstract: In various embodiments, methods and systems for implementing a media management system, for video data processing and adaptation data generation, are provided. At a high level, a video data processing engine relies on different types of video data properties and additional auxiliary data resources to perform video optical character recognition operations for recognizing characters in video data. In operation, video data is accessed to identify recognized characters. A video OCR operation to perform on the video data for character recognition is determined from video character processing and video auxiliary data processing. Video auxiliary data processing includes processing an auxiliary reference object; the auxiliary reference object is an indirect reference object that is a derived input element used as a factor in determining the recognized characters. The video data is processed based on the video OCR operation and based on processing the video data, at least one recognized character is communicated.