Abstract: A method and apparatus for fraud detection during transactions using identity graphs are described. A method includes receiving, at a commerce platform system, a transaction from a user having initial transaction attributes and transaction data. The method also includes determining, by the commerce platform system, an identity associated with the user associated with additional transaction attributes not received with the transaction. Furthermore, the method includes accessing a feature set associated with the initial transaction attributes and the additional transaction attributes that includes machine learning (ML) model features for detecting transaction fraud.

Abstract: A system for automating software usage includes an agent configured to automate. The agent is trained on one or more training data sets. The one or more training datasets include one or more of a first training dataset including documents containing text interleaved with images, a second training dataset including text embedded in images, a third training dataset including recorded videos of software usage, a fourth training dataset including portable document format (PDF) documents, a fifth training dataset including recorded videos of software tool usage trajectories, a sixth training dataset including images of open-domain web pages, a seventh training dataset including images of specific-domain web pages, and/or an eighth training dataset including images of agentic trajectories of the agent performing interface automation task workflows.

Abstract: A system for providing artificial intelligence agents that automate software usage includes training servers configured to train agents during training, production servers configured to execute the trained agents during inference, a plurality of training datasets, and data flow logic. The data flow logic is configured to, provide, during the training, the agents and the plurality of training datasets to the training servers to cause the training servers to train the agents on the plurality of training datasets and thereby produce the trained agents, configure the production servers with the trained agents for use during the inference, provide, during the inference, prompts issued by clients to the production servers to cause the production servers to translate the prompts into agent calls to the trained agents that in turn cause the trained agents to generate outputs that are responsive to the prompts, and make the outputs available to the clients.

Abstract: A system for automating software usage includes an agent configured to automate. The agent is trained on one or more training data sets. The one or more training datasets include one or more of a first training dataset including documents containing text interleaved with images, a second training dataset including text embedded in images, a third training dataset including recorded videos of software usage, a fourth training dataset including portable document format (PDF) documents, a fifth training dataset including recorded videos of software tool usage trajectories, a sixth training dataset including images of open-domain web pages, a seventh training dataset including images of specific-domain web pages, and/or an eighth training dataset including images of agentic trajectories of the agent performing interface automation task workflows.

Abstract: A machine learning framework and method for using the same are described.

Abstract: A method and apparatus for fraud detection during transactions using identity graphs are described. A method includes receiving, at a commerce platform system, a transaction from a user having initial transaction attributes and transaction data. The method also includes determining, by the commerce platform system, an identity associated with the user associated with additional transaction attributes not received with the transaction. Furthermore, the method includes accessing a feature set associated with the initial transaction attributes and the additional transaction attributes that includes machine learning (ML) model features for detecting transaction fraud.

Abstract: A method and apparatus for fraud detection during transactions using identity graphs are described. The method may include receiving, at a commerce platform system, a transaction from a user having initial transaction attributes and transaction data. The method may also include determining, by the commerce platform system, an identity associated with the user, wherein the identity is associated with additional transaction attributes not received with the transaction. Furthermore, the method may include accessing, by the commerce platform system, a feature set associated with the initial transaction attributes and the additional transaction attributes, wherein the feature set comprises machine learning (ML) model features for detecting transaction fraud.

Abstract: An approach is provided for providing predictive classification of actionable network alerts. The approach includes receiving the plurality of alerts. Each alert of the plurality of alerts indicates an alarm condition occurring at a monitored network system, and is a data record comprising one or more data fields describing the alarm condition. The approach also includes classifying said each alert using a predictive machine learning model. The predictive machine learning model is trained to classify said each alert as actionable or non-actionable using the one or more data fields of said each alert as one or more respective classification features, and to calculate a respective probability that said each alert is actionable or non-actionable. The approach further includes presenting the plurality of alerts in a network monitoring user interface based on the respective probability of said each alert.

Abstract: An approach is provided for providing predictive classification of actionable network alerts. The approach includes receiving the plurality of alerts. Each alert of the plurality of alerts indicates an alarm condition occurring at a monitored network system, and is a data record comprising one or more data fields describing the alarm condition. The approach also includes classifying said each alert using a predictive machine learning model. The predictive machine learning model is trained to classify said each alert as actionable or non-actionable using the one or more data fields of said each alert as one or more respective classification features, and to calculate a respective probability that said each alert is actionable or non-actionable. The approach further includes presenting the plurality of alerts in a network monitoring user interface based on the respective probability of said each alert.

Abstract: New techniques and systems may be implemented to improve error correction in speech recognition. These new techniques and systems may be implemented to correct errors in speech recognition systems may be used in a standard desktop environment, in a mobile environment, or in any other type of environment that can receive and/or present recognized speech.

Abstract: New techniques and systems may be implemented to improve error correction in speech recognition. These new techniques and systems may be implemented to correct errors in speech recognition systems may be used in a standard desktop environment, in a mobile environment, or in any other type of environment that can receive and/or present recognized speech.

Abstract: Correcting incorrect text associated with recognition errors in computer-implemented speech recognition includes receiving a selection of a word from a recognized utterance. The selection indicates a bound of a portion of the recognized utterance to be corrected. A first recognition correction is produced based on a comparison between a first alternative transcript and the recognized utterance. A second recognition correction is produced based on a comparison between a second alternative transcript and the recognized utterance. The duration of the first recognition correction differs from the duration of the second recognition correction. A portion of the recognition result that is replaced with one of the first recognition correction and the second recognition correction. includes at one bound a word indicated by the selection and extends for the duration of the one of the first recognition correction and the second recognition correction with which the portion is replaced.

Abstract: New techniques and systems may be implemented to improve error correction in speech recognition. These new techniques and systems may be implemented to correct errors in speech recognition systems may be used in a standard desktop environment, in a mobile environment, or in any other type of environment that can receive and/or present recognized speech.