Oracle International Corporation

Blizzard Entertainment, Inc.

Eric L Sutton

Tuck Chang

Carl Chimes

Dave Lawrence

Alan Dabiri

James Anhalt

Dustin R Garvey

Uri Shaft

Sampanna Shahaji Salunke

Andrew Ioannou

Eugene W Trent, Ii

Sumathi Gopalakrishnan

Jae Young Yoon

Sreeji Krishnan Das

Brent Arthur Enck

Timothy Mark Frazier

Adrienne Wong

Dhileeban Kumaresan

Amr Fawzy Fahmy

Lik Wong

Alexander R Buckley

Michael Patrick Rodgers

Girish Kumar Balachandran

Bura Chuhadar

Jeffrey James Rothe

Allison Burnett

Karen Theisen

Ashutosh Gupta

Rajesh Easwaramoorthy

Victor Chan

Udom Dwivedi

Techniques for performing analytics using automatically generated labels for time series data and numerical lists are disclosed. In some embodiments, a system loads a set of one or more time series datasets. A respective time series dataset may include a set of data points based on varying values of a metric of one or more computing resources over a window of time. The system assigns labels to a subset of the data points in the time series datasets. The label assigned to a given data point may be descriptive of a pattern reflected by the data point relative to other data points in the time series. The system further identifies a pattern of automatically assigned labels that is indicative of an event affecting the one or more computing resources. Responsive to identifying the pattern of labels, the system may trigger a responsive action.

Automatically-generated labels for time series data and numerical lists to use in analytic and machine learning systems

Techniques for triggering a transfer of a chat conversation with a user from a chatbot to a human agent based on detection of transfer criteria are disclosed. The chatbot uses natural language processing and a generative model to collect and organize information from the chat conversation to present to the human agent in a report when the chat conversation is transferred to the human agent. The chat conversation is transferred to the human agent by presenting the report and a graphical chat interface to the human agent. The graphical chat interface displays messages from chat conversation between the human agent and the user and displays messages from chat conversations between the human agent and multiple other users. Transferring the chat conversation from the chatbot to the human agent includes presenting interface elements to the human agent for receiving user input from the human agent for transmission to the user.

Trigger-based transfer of conversations from a chatbot to a human agent

Techniques for predictive system remediation are disclosed. Based on attributes associated with applications of one or more system-selected remedial actions to one or more problematic system behaviors in a system (e.g., a database system), the system determines a predicted effectiveness of one or more future applications of a remedial action to a particular problematic system behavior, as of one or more future times. The system determines that the predicted effectiveness of the one or more future applications of the remedial action is positive but does not satisfy a performance criterion. Responsive to determining that the predicted effectiveness is positive but does not satisfy the performance criterion, the system generates a notification corresponding to the predicted effectiveness not satisfying the performance criterion. The system applies the remedial action to the particular problematic system behavior, despite already determining that the predicted effectiveness does not satisfy the one or more performance criteria.

Predictive system remediation

Techniques for generating plug-in application recipe (PIAR) extensions are disclosed. A PIAR management application discovers a particular data type within one or more data values for a particular field of a plug-in application, where the particular data type is (a) different from a data type of the particular field as reported by the plug-in application and (b) narrower than the data type of the particular field while complying with the data type of the particular field. The PIAR management application identifies one or more mappings between (a) the particular data type and (b) one or more data types for fields accepted by actions of plug-in applications. The PIAR management application presents a user interface including one or more candidate PIAR extensions based on the mapping(s). Based on a user selection of a candidate PAIR extension, the PIAR management application executes a PIAR that includes the selected PIAR extension.

Generating plug-in application recipe extensions

Methods, computer-readable media, and computing devices are described herein for restoring gameplay by replaying past inputs. During gameplay, capture logic captures participant inputs that were received to change states of game objects. The capture logic stores the captured information in an electronic record of period(s) of the gameplay. After the period(s) of gameplay, replay logic receives a request to initiate subsequent gameplay beginning from a point in time from a period of past gameplay. The replay logic causes the subsequent gameplay to begin from the point at least in part by loading saved state information and replaying the captured participant inputs.

Restoring gameplay by replaying past inputs

Techniques are described for triggering conditional automated social posts. According to an embodiment, a set of one or more conditions is received through a user interface by a system executing on one or more computing devices. The system monitors one or more social media channels for target content that has been posted on at least one social media channel of the one or more social media channels. In response to detecting, by the system executing on one or more computing devices, that the target content has been posted on at least one social media channel of the one or more social media channels, the system determines whether the set of one or more conditions are satisfied. In response to determining that the set of one or more conditions are satisfied, the system triggers an action responsive to the target content.

Conditional automatic social posts

Techniques for semantic analysis-based generation of plug-in application recipes (PIAR's) are disclosed. Responsive to receiving a data item that specifies (a) a desired genus of actions and/or (b) a desired genus of triggers, a PIAR management application performs semantic analysis on the data item to identify one or more candidate PIAR's. The candidate PIAR(s) is/are identified based at least in part on mapping of actions and/or triggers to the desired genus of actions and/or the desired genus of triggers. The mapping is based at least in part on metadata, associated with profiles for plug-in applications, corresponding to actions and/or triggers. The PIAR management application stores, for each plug-in application, a corresponding profile to define the plug-in application for use in one or more future PIAR's. Based on user input approving a particular PIAR in the one or more candidate PIAR's, the PIAR management application executes the particular PIAR.

Semantic analysis-based plug-in application recipe generation

Techniques for user-assisted plug-in application recipe (PIAR) execution are disclosed. During execution of a PIAR, a PIAR management application applies one or more data values for a plug-in application field to a machine learning model, to obtain: (a) a candidate mapping between one or more sub-values discovered within the data value(s) and another field accepted by an action of another plug-in application, the data value(s) being of a data type different from a reported data type of the other field, and (b) a confidence metric associated with the candidate mapping, based at least in part on whether the sub-value(s) fit(s) one or more stored formats mapped to the other data type. Based on a determination that the confidence metric does not satisfy a threshold confidence criterion, the PIAR management application obtains user input affirming or rejecting the candidate mapping, and applies the user input to execution of the PIAR.

User-assisted plug-in application recipe execution

Systems and methods are described for efficiently detecting an optimal number of behaviors to model software system performance data and the aspects of the software systems that best separate the behaviors. The behaviors may be ranked according to how well fitting functions partition the performance data.

Automatic behavior detection and characterization in software systems

Growing community of inventors

Chicago, IL, United States of America

Eric L Sutton