Oracle International Corporation

Wevo, Inc.

Baker Hughes Corporation

Intellectual Assets LLC

Intellectual Assests LLC

General Electric Company

The University of Tennessee Research Foundation

Oracle Lnternational Corporation

Dustin R Garvey

Sampanna Shahaji Salunke

Uri Shaft

Sumathi Gopalakrishnan

Lik Wong

Amit Ganesh

Timothy Mark Frazier

Frank Chiang

Janet Muto

Nitzan Shaer

Alexa Stewart

Brent Arthur Enck

Charlie Hoang

Hannah Sieber

Shriram Krishnan

Prasad Ravuri

Andrea Paola Aguilera García

Shannon Walsh

Alexander Barza

Maria Elisabeth Kaval

Kenny C Gross

Eric L Sutton

Jon Andrews

Jessica Yau

Neil Goodman

Laurie Delaney

Long Yang

Aleksey M Urmanov

Kalyanaraman Vaidyanathan

Edwina Ming-Yue Lu

John Branson Bley

Dario Bahena Tapia

Mary McMurray

Melany Carvalho

Joseph Gibson

Nick Montaquila

Kelly Lyons

Ruthie McCready

Sara Peters

Kim Coccoluto

Everett Granger

Julia McCarthy

Scott Charles Evans

Hanno Reckmann

Joerg Lehr

Eric S Chan

Randall L Bickford

Edwina Lu

Olof Hummes

Raghu Hanumanth Reddy Patti

J Wesley Hines

Michael Avrahamov

Shachar Koresh

Laryssa Costa De Souza

Dario BahenaTapia

Mohammad Sadegh Ebrahimi

Xuemei Gao

Yongqiang Zhang

Debabrata Sarkar

Bing Hu

Rick Alarcon Sisniegas

Joerg Baumann

Marshall McCready

Martin John

Keith Horvath

Maria Kaval

Systems and methods for trending patterns within a set of time-series data are described. In one or more embodiments, a set of one or more groups of data points that are associated with a particular seasonal pattern are generated within volatile and/or non-volatile storage. A set of pairwise slopes is determined for data point pairs within the set of one or more groups of data points. Based, at least in part on the plurality of pairwise slopes, a representative trend rate for the particular seasonal pattern is determined. A set of forecasted values is then generated within volatile or non-volatile storage based, at least in part, on the representative trend rate for the particular seasonal pattern.

Systems and methods for trending patterns within time-series data

Techniques are described herein for using artificial intelligence (AI) and machine learning (ML) to automate, accelerate, and enhance various aspects of comparative user experience testing. Embodiments interface with generative language models to compare user experiences and summarize the results of the comparison. In some embodiments, automated systems and programmatic processes access a series of analysis contexts, where a context includes a collection of message content fragments. The systems and processes may use the message content fragments for a given context to construct a dialogue with a generative language model to compare separate user experiences based on the results of a set of user experience tests. The output of the generative language model at one stage of the analysis may be combined with content fragments for another context to craft a dialogue at another stage of the analysis and/or to perform additional analyses of the user experiences.

Systems and methods for automating comparative user experience test analyses

One or more embodiments relate to a method for refining usability testing through the systematic collection and analysis of user click paths within an online interface. By tracking and filtering these click paths based on one or both of publicly observable attributes and non-publicly observable attributes, obtained in part through user responses to screening questions, the method generates distinct, aggregated click paths. These paths are then subjected to in-depth analysis that yields insights and metrics regarding user behavior and usability. Some embodiments further extend to the presentation of an interactive representation of these paths within a testing interface that allows researchers to explore and extract actionable findings.

Usability click tracking with navigable click paths

Techniques are described herein for selecting, curating, normalizing, enriching, and synthesizing the results of user experience (UX) tests. In some embodiments, a system receives input defining or modifying a theme schema for classifying results of user experience tests. Responsive to receiving the input, the system trains a themer model based at least in part on example classifications in a training dataset, where the classifications map results to themes within the theme schema. When a new set of results for a user experience test is received, the trained machine learning model may generate a set of predicted themes to classify the test results. The output of the model may be used to render user interfaces and/or trigger other actions directed to optimizing a product's design.

Artificial intelligence based theme builder for processing user expectations

Techniques for artificial intelligence driven configuration management are described herein. In some embodiments, a machine-learning process determines a feature set for a plurality of deployments of a software resource. Based on varying values in the feature set, the process clusters each of the plurality of deployments into a cluster of a plurality of clusters. Each cluster of the plurality of clusters comprises one or more nodes and each node of the one or more nodes corresponds to at least a subset of values of the feature set that are detected in at least one deployment of the plurality of deployments of the software resource. The process determines a representative node for each cluster of the plurality of clusters. An operation may be performed based on the representative node for at least one cluster.

Artificial intelligence driven configuration management

Operations of a prompt management system are disclosed. The operations may include: receiving a prompt for performing a set of tasks, assigning an agent group that includes a plurality of agents to perform a set of roles associated with a dataset in support of the set of tasks, causing the plurality of agents to perform the set of roles using a first machine-learning model, receiving a set of role results from the plurality of agents responsive to performing the set of roles, performing the set of tasks using at least a second machine-learning model, and providing a task result for display on a user interface device. The set of tasks may include executing an operation on the set of role results using the second machine-learning model, and generating a task result that includes a product of the operation executed on the set of role results.

Prompt management systems for moderating generative artificial intelligence operations

Techniques are described herein for producing machine-generated findings given a set of user experience test results. In some embodiments, the system generates the findings using an artificial intelligence and machine learning engine. The findings may highlight areas that are predicted to provide the most insight into optimizing a product's design. A finding may be generated based on all or a subset of the test result elements, including qualitative and/or quantitative data contained therein. A finding may summarize a subset of the UX test results that are interrelated. A finding may link a summary to one or more references extracted from the set of test results to show support for the machine-generated insights in the underlying raw test data. Machine-generated findings reports may provide near instantaneous guidance for optimizing product designs while removing extraneous information from a vast quantity of raw test result data.

Scalable systems and methods for discovering and summarizing test result facts

Techniques are described herein for using artificial intelligence to select, curate, normalize, enrich, and synthesize the results of user experience (UX) tests. In some embodiments, a system identifies a set of unstructured textual elements associated with one or more UX tests. The system may configure agents using generative language model services, including a reviewing agent that reviews and edit outputs of a machine learning classification model applied to the unstructured textual elements and/or a curating agent that selects unstructured textual elements to represent themes within the user experience test classified using the machine learning classification model. The outputs may be used to enhance the scalability, function, and efficiency of applications directed at improving product designs.

Agent based methods for discovering and documenting user expectations

Techniques for machine-learning of long-term seasonal patterns are disclosed. In some embodiments, a network service receives a set of time-series data that tracks metric values of at least one computing resource over time. Responsive to receiving the time-series data, the network service detects a subset of metric values that are outliers and associated with a plurality of timestamps. The network service maps the plurality of timestamps to one or more encodings of at least one encoding space that defines a plurality of encodings for different seasonal patterns. Based on the mapped encodings, the network service generates a representation of a seasonal pattern. Based on the representation of the seasonal pattern, the network service may perform one or more operations in association with the at least one computing resource.

Systems and methods for detecting long term seasons

Techniques and embodiments are described herein for detecting and mitigating fraudulent activity within user experience (UX) test applications. In some embodiments, a system applies a set of rules and/or machine learning (ML) models to each respondent of an online survey or UX test. Different ML models may be trained to learn domain-specific patterns indicative of fraudulent activity. The system may then select the ML models based on attributes of the UX test and/or respondent. The selected rules and/or ML models may generate a probabilistic score representing a likelihood that the respondent is currently engaging in or will engage in fraudulent activity with respect to a UX test. If the score exceeds a threshold, then the system may take action to mitigate the fraudulent activity, such as triggering the removal of the user from an accepted respondent pool, halting further engagement between the respondent and the UX test, and generating alerts.

Scalable system and methods for curating user experience test respondents

Techniques are disclosed for summarizing, diagnosing, and correcting the cause of anomalous behavior in computing systems. In some embodiments, a system identifies a plurality of time series that track different metrics over time for a set of one or more computing resources. The system detects a first set of anomalies in a first time series that tracks a first metric and assigns a different respective range of time to each anomaly. The system determines whether the respective range of time assigned to an anomaly overlaps with timestamps or ranges of time associated with anomalies from one or more other time series. The system generates at least one cluster that groups metrics based on how many anomalies have respective ranges of time and/or timestamps that overlap. The system may preform, based on the cluster, one or more automated actions for diagnosing or correcting a cause of anomalous behavior.

Systems and methods for multivariate anomaly detection in software monitoring

Techniques are described for automatically detecting and accommodating state changes in a computer-generated forecast. In one or more embodiments, a representation of a time-series signal is generated within volatile and/or non-volatile storage of a computing device. The representation may be generated in such a way as to approximate the behavior of the time-series signal across one or more seasonal periods. Once generated, a set of one or more state changes within the representation of the time-series signal is identified. Based at least in part on at least one state change in the set of one or more state changes, a subset of values from the sequence of values is selected to train a model. An analytical output is then generated, within volatile and/or non-volatile storage of the computing device, using the trained model.

Systems and methods for detecting and accommodating state changes in modelling

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 are described herein for selecting, curating, normalizing, enriching, and synthesizing the results of user experience tests. In some embodiments, a system identifies a qualitative element within a result set for a user experience test. The system then selects a machine learning model to apply based on one or more attributes associated with the user experience test and generates a predicted visibility, quality, and/or relevance for the qualitative element. Based on the prediction, the system generates a user interface that curates a set of results of the user experience test.

Scalable systems and methods for curating user experience test results

Techniques are described for classifying seasonal patterns in a time series. In an embodiment, a set of time series data is decomposed to generate a noise signal and a dense signal, where the noise signal includes a plurality of sparse features from the set of time series data and the dense signal includes a plurality of dense features from the set of time series data. A set of one or more sparse features from the noise signal is selected for retention. After selecting the sparse features, a modified set of time series data is generated by combining the set of one or more sparse features with a set of one or more dense features from the plurality of dense features. At least one seasonal pattern is identified from the modified set of time series data. A summary for the seasonal pattern may then be generated and stored.

Unsupervised method for classifying seasonal patterns

Growing community of inventors

Redwood City, CA, United States of America

Dustin R Garvey