Adobe, Inc.

Atanu R Sinha

Pranav Ravindra Maneriker

Gaurush Hiranandani

Natwar Modani

Shivani Gupta

Utpal

Vaishnavi Subramanian

Balaji Vasan Srinivasan

Niyati Himanshu Chhaya

Georgios Theocharous

Cedric Huesler

Kokil Jaidka

Anandhavelu N

Sunny Dhamnani

Navita Goyal

Reshmi Naduparambil Sasidharan

Rishiraj Saha Roy

Padmanabhan Anandan

Prashant Chawla

Shubham Saxena

Abhijeet Singh

Vamsi Krishna Bokam

Tanay Asija

Soham Dan

Kaarthik Raja Meenakshi Viswanathan

Yogesh Singh

Raja Kumar Dubey

The present disclosure relates to methods, systems, and non-transitory computer-readable media for determining a cognitive, action-selection bias of a user that influences how the user will select a sequence of digital actions for execution of a task. For example, the disclosed systems can identify, from a digital behavior log of a user, a set of digital action sequences that correspond to a set of sessions for a task previously executed by the user. The disclosed systems can utilize a machine learning model to analyze the set of sessions to generate session weights. The session weights can correspond to an action-selection bias that indicates an extent to which a future session for the task executed by the user is predicted to be influenced by the set of sessions. The disclosed systems can provide a visual indication of the action-selection bias of the user for display on a graphical user interface.

Detecting cognitive biases in interactions with analytics data

In some embodiments, an intervention evaluation system estimates counterfactual metric for a focal online platform based on an assessment model built using performance data of the focal online platform and control online platforms. The intervention evaluation system accesses performance data of the focal online platform that has been subject to a temporary intervention and performance data of control online platforms that are not subject to the temporary intervention. The intervention evaluation system determines estimation weights for these control online platforms based on the performance data in a pre-intervention period. Based on the estimation weights, the intervention evaluation system computes a counterfactual metric indicating the performance of the focal online platform in a post-intervention period in the absence of the temporary intervention. The counterfactual metric is transmitted to the focal online platform, where the counterfactual metric is usable for modifying an interactive computing environment provided by the focal online platform.

Facilitating changes to online computing environment by assessing impacts of temporary interventions

This disclosure relates to methods, non-transitory computer readable media, and systems that, based on a sparse textual segment, can use machine learning models to generate document variants that are both conforming to digital content guidelines and uniquely tailored for distribution to client devices of specific audiences via specific delivery channels. To create such variants, in some embodiments, the methods, non-transitory computer readable media, and systems generate suggested modifications to a draft document that correspond to features of content-guideline-conforming documents. Additionally, or alternatively, in certain implementations, the disclosed methods, non-transitory computer readable media, and systems generate suggested modifications to a draft document that correspond to features of audience-channel-specific documents.

Automatically generating digital enterprise content variants

This disclosure relates to utilizing a statistical model trained on character dimensions to determine a likelihood of a person purchasing a product. The method may include obtaining user-input data of a first person (e.g., textual-input data, survey-response data, offer information, or clickstream data associated with a first person). A character profile for the first person is derived using the user-input data and a psycholinguistic lexicon. A statistical model is generated based on the derived character profile of the first person. Second user-input data associated with a second person is obtained. The second user-input is applied to the statistical model to determine an output of the model (e.g., a statistical probability value that quantifies, for example, a predicted intention of the second person to purchase a particular product).

Techniques for generating a psychographic profile

Multimedia document summarization techniques are described. That is, given a document that includes text and a set of images, various implementations generate a summary by extracting relevant text segments in the document and relevant segments of images with constraints on the amount of text and number/size of images in the summary.

Multimedia document summarization

Systems and methods disclosed select and display icons corresponding to topics of digital documents. In particular, in one or more embodiments, systems and methods identify similar clusters of documents from a plurality of documents and generate icons for each identified cluster of documents. For instance, systems and methods identify topical phrases from identified clusters and utilize the topical phrases to identify icons corresponding to the topical phrases. More specifically, one or more embodiments include systems and methods that generate generalized terms from topical phrases utilizing a concept taxonomy and then identify icons by comparing the generated generalized terms to tags corresponding to a plurality of icons.

Assigning of topical icons to documents to improve file navigation

A quality metric of a multimedia summary of a multimedia content item is determined based, in part, on semantic similarities of the summary and content item, rather than just on word frequencies. This is accomplished in some embodiments by identifying a semantic meaning of the summary and multimedia content item using vector analysis. The vectors of the summary and the vectors of the multimedia content item are compared to determine semantic similarity. In other examples, the quality metric of the multimedia summary is determined based on, in part, a coherence between an image portion of a summary and a text portion of the summary for determining a quality metric of a multimedia summary.

Determining quality of a summary of multimedia content

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Boulder, CO, United States of America

Atanu R Sinha