Suki Ai, Inc.

Adobe, Inc.

Ganesh Satish Mallya

Jatin Chhugani

Maneesh Dewan

Sudheer Tumu

Nithyanand Kota

Vamsi Reddy Chagari

Alan Diec

Mihir Naware

Jonas Dahl

Deepak Pai

Shankar Venkitachalam

Debraj Basu

Systems and methods to translate a spoken command to a selection sequence are disclosed. Exemplary implementations may: obtain audio information representing sounds captured by a client computing platform; analyze the sounds to determine spoken terms; determine whether the spoken terms include one or more of the terms that are correlated with the commands; responsive to determining that the spoken terms are terms that are correlated with a particular command stored in the electronic storage, perform a set of operations that correspond to the particular command; responsive to determine that the spoken terms are not the terms correlated with the commands stored in the electronic storage, determining a selection sequence that causes a result subsequent to the analysis of the sounds; correlate the spoken terms with the selection sequence; store the correlation of the spoken terms with the selection sequence; and perform the selection sequence to cause the result.

Systems and methods to translate a spoken command to a selection sequence

Enhanced methods for improving the performance of classifiers are described. A ground-truth labeled dataset is accessed. A classifier predicts a predicted label for datapoints of the dataset. A confusion matrix for the dataset and classifier is generated. A credibility interval is determined for a performance metric for each label. A first labels with a sufficiently large credibility interval is identified. A second label is identified, where the classifier is likely to confuse, in its predictions, the first label with the second label. The identification of the second label is based on instances of incorrect label predictions of the classifier for the first and/or the second labels. The classifier is updated based on a new third label that includes an aggregation of the first label and the second label. The updated classifier model predicts the third label for any datapoint that the classifier previously predicted the first or second labels.

Quantifying and improving the performance of computation-based classifiers

Systems and methods to implement commands based on selection sequences to a user interface are disclosed. Exemplary implementations may: store, electronic storage, a library of terms utterable by users that facilitate implementation of intended results; obtain audio information representing sounds captured by a client computing platform; detect the spoken terms uttered by the user present within the audio information; determine whether the spoken terms detected are included in the library of terms; responsive to determination that the spoken terms are not included in the library of terms, effectuate presentation of an error message via the user interface; record a selection sequence that the user performs subsequent to the presentation of the error message that causes a result; correlate the selection sequence with the spoken terms based on the selection sequence recorded subsequent to error message to generate correlation; and store the correlation to the electronic storage.

Systems and methods to implement commands based on selection sequences to a user interface

The present disclosure relates to systems, methods, and non-transitory computer readable media for clustering media items in a semantic space to generate theme-based folders that organize media items by content theme. In particular, the disclosed systems can access media items that are stored in an original folder structure. The disclosed systems can generate content-based tags for each media item in a collection of media items. Based on the generated tags, the disclosed systems can map the collection of media items to a semantic space and cluster the collection of media items. The disclosed systems determine themes for the clusters based on the generated tags. The disclosed systems can present a media item navigation graphical user interface comprising the collection of media items organized by themes. The disclosed system can present the media item navigation graphical user interface without altering the original folder structure.

Generating theme-based folders by clustering digital images in a semantic space

Automatically generating theme-based folders by clustering media items in a semantic space

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Sunnyvale, CA, United States of America

Ganesh Satish Mallya