Adobe, Inc.

Gaurush Hiranandani

Kumar Ayush

Natwar Modani

Shiv Kumar Saini

Chinnaobireddy Varsha

Sai Varun Reddy Maram

Atanu Ranjan Sinha

Shivani Gupta

Sumit Shekhar

Iftikhar Ahamath Burhanuddin

Tanya Goyal

Harvineet Singh

Prakhar Gupta

Pranav Ravindra Maneriker

Sunav Choudhary

Moumita Sinha

Payal Bajaj

Atanu R Sinha

Vaishnavi Subramanian

Utpal

Balaji Vasan Srinivasan

Branislav Kveton

Trevor Paulsen

Kushal Chawla

Zheng Wen

Harsh Jhamtani

Cedric Huesler

Sanket Vaibhav Mehta

Siddhant Jain

Charanjit Singh Ghai

Vaishnav Pawan Madandas

Aditya Jain

Anshul Agrawal

Methods and systems are provided herein for summarizing a set of anomalies corresponding to a group of metrics of interest to a monitoring system user. Initially, a set of anomalies corresponding to a group of metrics is identified as having values that are outside of a predetermined range. A correlation value is determined for at least a portion of pairs of anomalies in the set of anomalies. For each anomaly in the set of anomalies, an informativeness value is computed that indicates how informative each anomaly in the set of anomalies is to the monitoring system user. The correlation values and the informativeness values are then used to identify at least one key anomaly and a plurality of non-key anomalies from the set of anomalies. A summary is generated of the identified at least one key anomaly to provide information to the monitoring system user about the set of anomalies for a particular time period.

Providing personalized alerts and anomaly summarization

Certain embodiments involve extracting seasonal, level, and spike components from a time series of metrics data, which describe interactions with an online service over a time period. For example, an analytical system decomposes the time series into latent components that include a seasonal component series, a level component series, a spike component series, and an error component series. The decomposition involves configuring an optimization algorithm with a constraint indicating that the time series is a sum of these latent components. The decomposition also involves executing the optimization algorithm to minimize an objective function subject to the constraint and identifying, from the executed optimization algorithm, the seasonal component series, the level component series, the spike component series, and the error component series that minimize the objective function. The analytical system outputs at least some latent components for anomaly-detection or data-forecasting.

Extracting seasonal, level, and spike components from a time series of metrics data

Techniques and systems for determining paywall metrics are described. In an implementation, a candidate paywall metric is created that corresponds to an increased propensity of users to engage in a paid transaction when exposed to a paywall. In this way, providers of digital content may increase the proportion of users that perform a transaction when exposed to the paywall.

Determination of paywall metrics

Techniques of forecasting web metrics involve generating, prior to the end of a period of time, a probability of a metric taking on an anomalous value, e.g., a value indicative of an anomaly with respect to web traffic, at the end of the period based on previous values of the metric. Such a probability is based on a distribution of predicted values of the metric at some previous period of time. For example, a web server may use actual values of the number of bounces collected at hourly intervals in the middle of a day to predict a number of bounces at the end of the current day. Further, the web server may also compute a confidence interval to determine whether a predicted end-of-day number of bounces may be considered anomalous. The width of the confidence interval indicates the probability that a predicted end-of-day number of bounces has an anomalous value.

End of period metric projection with intra-period alerts

A machine-learning framework uses partial-click feedback to generate an optimal diverse set of items. An example method includes estimating a preference vector for a user based on diverse cascade statistics for the user, the diverse cascade statistics including previously observed responses and previously observed topic gains. The method also includes generating an ordered set of items from the item repository, the items in the ordered set having highest topic gain weighted by similarity with the preference vector, providing the ordered set for presentation to the user, and receiving feedback from the user on the ordered set. The method also includes, responsive to the feedback indicating a selected item, updating the diverse cascade statistics for observed items, wherein the updating results in penalizing the topic gain for items of the observed items that are not the selected item and promoting the topic gain for the selected item.

Online diverse set generation from partial-click feedback

The present disclosure relates to systems, methods, and non-transitory computer readable media for generating augmented reality representations of recommended products based on style similarity with real-world surroundings. For example, the disclosed systems can identify a real-world object within a camera feed and can utilize a 2D-3D alignment algorithm to identify a three-dimensional model that matches the real-world object. In addition, the disclosed systems can utilize a style similarity algorithm to generate style similarity scores for products in relation to the identified three-dimensional model. The disclosed systems can also utilize a color compatibility algorithm to generate color compatibility scores for products, and the systems can determine overall scores for products based on a combination of style similarity scores and color compatibility scores. The disclosed systems can further generate AR representations of recommended products based on the overall scores.

Generating and providing augmented reality representations of recommended products based on style similarity in relation to real-world surroundings

Certain embodiments involve enhancing personalization of a virtual-commerce environment by identifying an augmented-reality visual of the virtual-commerce environment. For example, a system obtains a data set that indicates a plurality of augmented-reality visuals generated in a virtual-commerce environment and provided for view by a user. The system obtains data indicating a triggering user input that corresponds to a predetermined user input provideable by the user as the user views an augmented-reality visual of the plurality of augmented-reality visuals. The system obtains data indicating a user input provided by the user. The system compares the user input to the triggering user input to determine a correspondence (e.g., a similarity) between the user input and the triggering user input. The system identifies a particular augmented-reality visual of the plurality of augmented-reality visuals that is viewed by the user based on the correspondence and stores the identified augmented-reality visual.

Identifying augmented reality visuals influencing user behavior in virtual-commerce environments

This disclosure generally covers systems and methods that identify objects within an augmented reality ('AR') scene (received from a user) to gather information concerning the user's physical environment or physical features and to recommend products. In particular, the disclosed systems and methods detect characteristics of multiple objects shown within an AR scene received from a user and, based on the detected characteristics, select products to recommend to the user. When analyzing characteristics, in some embodiments, the disclosed systems and methods determine visual characteristics associated with the real object or virtual object, such as color or location of an object. The disclosed systems and methods, in some embodiments, then select an endorsed product to recommend for use with the real object&#x2014;based on the determined visual characteristics&#x2014;and create a product recommendation that recommends the endorsed product.

Creating targeted content based on detected characteristics of an augmented reality scene

The present disclosure relates to systems, methods, and non-transitory computer readable media for generating augmented reality representations of recommended products based on style compatibility with real-world surroundings. For example, the disclosed systems can identify a real-world object within a camera feed and can utilize a 2D-3D alignment algorithm to identify a three-dimensional model that matches the real-world object. In addition, the disclosed systems can utilize a style compatibility algorithm to generate recommended products based on style compatibility in relation to the identified three-dimensional model. The disclosed systems can further utilize a color compatibility algorithm to determine product textures which are color compatible with the real-world surroundings and generate augmented reality representations of recommended products to provide as an overlay of the real-world environment of the camera feed.

Generating and providing augmented reality representations of recommended products based on style compatibility in relation to real-world surroundings

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 are disclosed herein for determining user behavior in an augmented reality environment. An augmented reality application executing on a computing system receives a video depicting a face of a person. The video includes a video frame. The augmented reality application augments the video frame with an image of an item selected via input from a user device associated with a user. The augmented reality application determines, from the video frame, a score representing an action unit. The action unit represents a muscle on the face of the person depicted by the video frame and the score represents an intensity of the action unit. The augmented reality application calculates, from a predictive model and based on the score, an indicator of intent of the person depicted by the video.

Augmented reality predictions using machine learning

Techniques are disclosed for identifying anomalies in small data sets, by identifying anomalies using a Generalized Extreme Student Deviate test (GESD test). In an embodiment, a data set, such as business data or a website metric, is checked for skewness and, if found to be skewed, is transformed to a normal distribution (e.g., by applying a Box-Cox transformation). The data set is checked for presence of trends and, if a trend is found, has the trend removed (e.g., by running a linear regression). In one embodiment, a maximum number of anomalies is estimated for the data set, by applying an adjusted box plot to the data set. The data set and the estimated number of anomalies is run through a GESD test, and the test identifies anomalous data points in the data set, based on the provided estimated number of anomalies. In an embodiment, a confidence interval is generated for the identified anomalies.

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Karnataka, India

Gaurush Hiranandani