Oracle International Corporation

Anilkumar Gande

Kyasaram Vishwa Prasad

Naveen Jafer Nizar

Srikanth Subrahmanya Nandula

Ayushi Behl

Subir Kawal Hira

Roya Lashgari Afshar

Margaret Sue Lloyd

Subhadarshi Mishra

A system provides automatic, end-to-end tagging of email messages. While a message is being composed at a sending email client, the server may receive email information that is used as an input to a predictive model. The model identifies tags that are available to a specific user group or email list that apply to the email message. These predicted tags are sent back to the email client, where they may be embedded in the email message with other user-defined tags. As the message is passed through the email server, the system may use any changes made to the predicted tags to retrain the model. When the message is received at a second email client, the receiver may further edit the tags, and any changes may again be used to retrain the model.

End-to-end email tag prediction

An artificial neural network (ANN) determines a conversation snippet sentiment score based on content of the conversation snippet and contextual attributes associated with the conversation snippet. Contextual attributes may include, for example, a role within an organizational hierarchy of a user participating in the conversation snippet. Information representing the content is input into a hidden layer sequence of the ANN; information representing the contextual attributes is input into another hidden layer sequence of the ANN. Additionally or alternatively, a weighing engine determines a topical sentiment score by aggregating weighted conversation snippet sentiment scores. Weights to be applied to the conversation snippet sentiment scores may be determined based on, for example, a proportion of conversation snippets associated with the same topic that are conducted on a particular conversation channel as compared with other conversation channels, and respective roles of users participating in the conversation snippets conducted on the particular conversation channel.

Using neural network and score weighing to incorporate contextual data in sentiment analysis

Methodologies and systems are described herein whereby application functionality may be optimized and extended based on a contextual analysis of the features that are accessed within the application. In one or more embodiments, the contextual analysis includes (a) detecting that an end user of an application is using a particular feature of the application and (b) determining that a set of one or more third-party applications are configured for operations accessing data sets of the same type accessed by the particular feature. Responsive at least to the detecting and determining operations, the set of one or more third-party applications are presented to the user when the user is accessing the particular feature of the first application.

Context-based analytical engine for extending application functionality

Systems and methods, are provided for self-healing solutions to problems that occur during a software build. In one embodiment, build log data structures of build data, generated during execution of a software build of a software application, are automatically monitored. The build data is automatically analyzed to detect patterns that can result in build violations. Detected patterns are automatically matched to remediation solutions stored in a knowledge database. The remediation solutions are automatically applied to the software application.

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Hyderabad Telangana, India

Anilkumar Gande