Salesforce, Inc.

Salesforce Com, Inc.

Owen Winne Schoppe

Brian J Lonsdorf

Sönke Rohde

David J Woodward

Jessica Lundin

Alan Martin Ross

Michael Reynolds Sollami

Amir Hossein Raffiee

Aashish Jain

Simon Toens

Joseph Michael Andolina

Peng-Wen Chen

Jesse Alan Hausler

Yamuna Esaiarasan

Reddy Prasad Yerradoddi

Chetan Ramaiah

Joseph Andolina

Brett Schuenemann

Ji Wang

Jason Teller

Jonathan Aniano

Joshua Giblette

Samuel Paul

Ian Schoen

John Fredric Vogt, Jr

Jianming Shao

Xing Han

Matthew Quinn Nielsen

Alicia Charmaine Williams

David James Woodward

Disclosed is a system for managing content generated by bots for presentation to a user in association with a chat application. The system receives content items generated by bots monitoring a chat application for display to a user at a user interface (UI). The system provides input based on the received one or more content items and associated contextual information to a trained machine learning (ML) model, and receives, from the trained ML model, for each of the content items, at least one score value based on at least one predicted user response associated with potentially displaying the content item to the user at the UI. The system selects a subset of content items from the received content items based on the received score values and causes a display of this selected subset of content items in addition to a display of content generated by the chat application.

Machine learning based supervised user experience for an application monitored by multiple secondary applications

Techniques are disclosed relating to emphasizing user interface elements for different users based on user attributes. In some embodiments, a system maintains a set of information (e.g., for a particular product) that includes multiple types of information. The system may access the same set of information for different user requests. The computing system may determine first and second subsets of the set of information based on attributes of the different users. The computing system may cause display of a first user interface on a device of the first user showing only the first subset of item fields for the two or more of the first set of items. The computing system may cause display of a second user interface on a device of the second user showing only the second subset of item fields for the two or more of the second set of items.

Adjusting emphasis of user interface elements based on user attributes

Techniques are disclosed for automatically converting a layout image to a text-based representation. In the disclosed techniques, a server computer system receives a layout image that includes a plurality of portions representing a plurality of user interface (UI) elements included in a UI design. The server computer system transforms, via executed of a trained residual neural network (ResNet), the layout image to a text-based representation of the layout image that specifies coordinates of bounding regions of the plurality of UI elements included in the UI design, where the text-based representation is usable to generate program code executable to render the UI design. The disclosed techniques may advantageously automate one or more portions of a UI design process and, as a result save time and computing resources via the execution of an image to text-based conversion ResNet machine learning model.

Automatic image conversion

Systems, devices, and techniques are disclosed for visual text summary generation. An input text may be received. Keywords may be extracted from the input text. Representative keywords may be generated from the keywords. A graph representation of the representative keywords may be generated. Images associated with the representative keywords may be received. A visual-representation style may be selected based on the graph representation of the representative keywords. The images associated with the representative keywords may be arranged according to the selected visual-representation style and the graph representation of the representative keywords.

Visual text summary generation

Techniques are disclosed relating to determining a similarity of components of a current webpage to different UI components for use in automatically generating an updated webpage. A computer system may receive information specifying a current webpage, including a particular current UI component and information specifying a plurality of different UI components for an updated webpage. The computer system may identify one or more characteristics of the particular current UI component. The computer system may determine, based on the identified one or more characteristics, a similarity of ones of the plurality of different UI components to the particular current UI component. The computer system may select, based on the determining, a particular different UI component from the plurality of different UI components for use, in the updated webpage, for the particular current UI component. Such techniques may advantageously improve user experience by automatically providing up-to-date user interfaces.

User interface design update automation

Techniques are disclosed for automatically generating new content using a trained 1-to-N generative adversarial network (GAN) model. In disclosed techniques, a computer system receives, from a computing device, a request for newly-generated content, where the request includes current content. The computer system automatically generates, using the trained 1-to-N GAN model, N different versions of new content, where a given version of new content is automatically generated based on the current content and one of N different style codes, where the value of N is at least two. After generating the N different versions of new content, the computer system transmits them to the computing device. The disclosed techniques may advantageously automate a content generation process, thereby saving time and computing resources via execution of the 1-to-N GAN machine learning model.

One-to-many automatic content generation

Disclosed herein are system, method, and computer program product embodiments for determining a user-preferred feature type. An embodiment operates by maintaining user-presented features associated with user-presented records, wherein the user-presented features comprise one or more user-presented feature types. After receiving a user-desired feature of the user-presented features, a user-preferred feature type of the user-presented feature types is determined based on the user-presented features and the user-desired feature. Thereafter, a new record and associated feature are to be presented with the new feature being of the user-preferred type.

Learning affinities through design variations

Disclosed herein are system, method and computer readable storage medium for detecting space suitable for overlaying media content onto an image. The system receives a candidate image which may be an image or a video frame. The candidate image is then input into a neural network. The neural network may output coordinates and one or more dimensions representing one or more bounding boxes for inserting media content into the candidate image. The one or more bounding boxes may be transmitted with a request for a media content item to be displayed in a bounding box. In response to the request the media content item may be received, and the candidate image and the media content item overlaid on top of the candidate image within the bounding box may be displayed.

Neural network based detection of image space suitable for overlaying media content

An online system generates a set of content item variations for a reference content item that include different styles of text for the content item. The different styles of text are generated by applying machine-learned style transfer models, for example, neural network based models to reference text of the reference content item. The text variations retain the textual content of the reference text but are synthesized with different styles. The online system can provide the content item variations to users on an online experimental platform to collect user interaction information that may indicate how users respond to different styles of text. The online system or the content providers can effectively target users with content items that include the style of text the users respond to based on the collected information.

Machine-learning based generation of text style variations for digital content items

Disclosed herein are system, method and computer readable storage medium for detecting space suitable for overlaying media content onto an image. The system receives an image which may be an image or a video frame. The image is processed using a number of image processing techniques in order to automatically propose spaces for inserting media content onto the image. The proposed spaces may then be further analyzed using a heuristics-based approach to select bounding boxes for inserting media content. Subsequently, one or more media content items may be selected for insertion onto the bounding boxes on the image. The system may then cause a display of the image with the selected media content item overlaid onto the image within the selected bounding boxes.

Heuristics-based detection of image space suitable for overlaying media content

A textual properties model is used to infer values for certain font properties of interest given certain text-related data, such as rendered text images. The model may be used for numerous purposes, such as aiding with document layout, identifying font families that are similar to a given font families, and generating new font families with specific desired properties. In some embodiments, the model is trained from a combination of synthetic data that is labeled with values for the font properties of interest, and partially-labeled data from existing 'real-world' documents.

Machine learning-based inference of granular font properties

A server of a generative adversarial network (GAN) for color selection; generates a training set of color palettes. A color palette generator of the server generates a first set of color palettes based on the training set of color palettes. The first set of color palettes may be compared with a reference set of color palettes to predict a curated set of color palettes. Colors from the curated set of color palettes may be removed that are within a predetermined distance from one another in a color space. The GAN may be validated by performing cluster analysis to determine outlier latent dimensions to be changed for the color selection by the GAN. Proposed color palettes may be generated based on the GAN to be displayed on a display device.

Systems and methods of generating color palettes with a generative adversarial network

Techniques are disclosed relating to generating a user interface (UI) migration plan, including intermediate UIs, for migrating from a current UI to a new UI. A computer system may receive information specifying a current UI and a new UI, and identify one or more differences between the current and the new UIs. Based on the differences, the computer system may generate information specifying one or more candidate intermediate UIs. The computer system may score the candidate intermediate UIs relative to a specified set of design criteria. The computer system may determine a UI migration plan that specifies a set of the one or more candidate intermediate UIs that are displayable in order to migrate from the current UI to the new UI, where the set of one or more intermediate UIs is selected based on the scoring. Use of the UI migration plan may advantageously reduce user interaction issues.

User interface migration using intermediate user interfaces

Disclosed herein are system, method, and computer program product embodiments for determining the minimum set of user interface (UI) elements for a task. One embodiment operates by transmitting UI elements associated with an application to a first computing device operated by a first user and a second computing device operated by a second user. Further, the embodiment includes obtaining data structures corresponding to a first task and a second task and then grouping, using a machine learning model, the data structures into a first cluster corresponding to the first task and a second cluster corresponding to the second task. Further still, the embodiment also includes determining a minimum set of UI elements required for the first task based on the first cluster.

Determining user interface elements required for task completion using machine learning

Disclosed herein are system, method, and computer program product embodiments for determining a user-preferred attribute type. An embodiment operates by maintaining user-presented attributes associated with user-presented records, wherein the user-presented attributes comprise one or more user-presented attribute types. After receiving a user-desired attribute of the user-presented attributes, a user-preferred attribute type of the user-presented attribute types is determined based on the user-presented attributes and the user-desired attribute. Thereafter, a new record and associated attribute are to be presented with the new attribute being of the user-preferred type.

Growing community of inventors

Orinda, CA, United States of America

Owen Winne Schoppe