Wevo, Inc.

Charlie Hoang

Dustin R Garvey

Frank Chiang

Nitzan Shaer

Janet Muto

Alexa Stewart

Hannah Sieber

Jon Andrews

Shannon Walsh

Jessica Yau

Andrea Paola Aguilera García

Alexander Barza

Laurie Delaney

Nick Montaquila

Mary McMurray

Joseph Gibson

Rick Alarcon Sisniegas

Marshall McCready

Keith Horvath

Shachar Koresh

Laryssa Costa De Souza

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

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 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 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 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 herein for selecting, curating, normalizing, enriching, and synthesizing the results of user experience (UX) tests. In some embodiments, a system identifies a set of expectation elements associated with one or more UX tests. An expectation element may specify, using unstructured data that does not conform to a schema, an expectation for a user experience and a respective outcome for the user experience. A themer model may generate predictions that map the respective expectation elements to a theme from a theme schema, which may include a plurality of themes. A selector model may generate selection scores for the expectation elements. The predicted themes and selection scores may be used to render user interfaces and/or trigger other actions directed to optimizing a product's design.

Scalable systems and methods for discovering and documenting user expectations

Techniques are described herein for providing adaptable testing and benchmarking of user experiences with respect to one or more products. In some embodiments, the techniques include systems and methods for predicting performance of facets of a user experience under new testing and benchmarking methodologies. The systems and methods may generate a prediction for the results of a UX test even though the methodology and mechanics to quantify the user experience may vary significantly from previous methodologies. The techniques allow for methodologies to evolve over time without losing historical context or the ability to meaningfully compare historical test results with tests run using updated testing and benchmark models. Further, the techniques allow for benchmarks to be computed in real-time or near real-time as methodologies change without requiring tests to be run according to the new methodologies.

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Brighton, MA, United States of America

Charlie Hoang