Meta Platforms, Inc.

Facebook, Inc.

Square, Inc.

Block, Inc.

Martin Mroz

Steve Morin

Anuj Madan

Xuewei Ouyang

Michael Wells White

Ahmad Byagowi

David Terra

Michael Maroye Lambeta

Jason Holmes

Charles Nicholson

Shannon M Ma

Sharon Zeng

Ryan Michael O'Rourke

Aaron T Williams

Haixia Shi

Jaed Uavechanichkul

Qikun Guo

Christopher Ellsworth

Aarati Vijay Soman

Examples, methods and systems described herein may include determining, by a payment processing system, whether a second payment mechanism may be used to perform a payment transaction when a first payment mechanism is preferred and/or when the first payment mechanism has failed. Determining to use the second payment mechanism can be based on (i) an analysis of a communication status indicator received from a point-of-sale (POS) terminal that indicates which payment mechanisms are capable of communicating with the POS terminal, (ii) data received from the POS terminal that is associated with a read of a payment object, and/or (iii) an allowance criterion.

Technical fallback infrastructure

A user identifies products offered by the user to an online system. The online system identifies a product offered by the user in an image by applying a identification model to the image. If the online system identifies a product in the image with at least a maximum confidence value, the online system automatically tags the post with metadata about the product or suggests a tag to the user. If an object in the image could be one of multiple products, the online system identifies the multiple products to the user, which may be ordered based on confidences of matching the object, allowing the user to select which product is in the image. If the unlisted identifies a product in the image with less than a minimum confidence value, the online system identifies the user's offered products and suggests that the user select a product.

Identifying objects within an image from a user of an online system matching products identified to the online system by the user

An online system accesses a model trained based on a topic associated with a set of content items and the content of the set of content items. The online system applies the model to predict a probability that each of multiple content items is associated with the topic based on its content and identifies (a) content item(s) associated with at least a threshold probability. The online system retrieves information describing user engagement with the identified content item(s) and determines a value of a performance metric for the topic based on this information. If the value is at least a threshold value and the online system receives content from an entity describing an item associated with the topic, the online system communicates a recommendation to the entity to create a content item describing the item and to add a tag associated with the item upon determining an opportunity to do so.

Recommending that an entity in an online system create content describing an item associated with a topic having at least a threshold value of a performance metric and to add a tag describing the item to the content

An online system receives a content item including an image from an online system user. The online system accesses and applies a trained item detection model to predict a probability that a region of interest within the image corresponds to an item associated with an entity based on a set of pixel values associated with the region of interest. If the probability is at least a threshold probability, the online system accesses and applies a trained quality prediction model to predict a measure of quality of the image based on a set of attributes of the image. If the measure of quality is at least a threshold measure of quality, the online system includes the content item in a page associated with the entity maintained in the online system and/or adds a set of data associated with the item and/or the entity to the content item.

Including content created by an online system user in a page associated with an entity and/or adding data to the content based on a measure of quality of an image included in the content

An online system receives a content item including an image from a content-providing user and/or receives an interaction with the content item from a viewing user. The online system accesses a machine-learning model that is trained based on a set of images of items associated with an entity and attributes of each image. The online system applies the model to predict a probability that the content item includes an image of an item associated with the entity based on attributes of the image included in the content item. Based on the predicted probability, the online system updates a profile of the user (i.e., the content-providing user and/or the viewing user) to include an affinity for the item. Upon determining an opportunity to present content to the user, the online system selects content for presentation to the user based on the profile and sends the content for presentation to the user.

Updating a profile of an online system user to include an affinity for an item based on an image of the item included in content received from the user and/or content with which the user interacted

A request to perform a compute task is received. A plurality of compute processor resources eligible to perform the compute task is identified, wherein the plurality of compute processor resources includes two or more of the following: a field-programmable gate array, an application-specific integrated circuit, a graphics processing unit, or a central processing unit. Based on an optimization metric, one of the compute processor resources is dynamically selected to perform the compute task.

Hardware accelerated compute kernels for heterogeneous compute environments

Techniques are described that enable a user to share videos having dynamic overlays on a social networking system. In some examples, a server computing device may receive, at a first time from a first client device, a first video. For instance, the server computing device may begin receiving the first video after the first client device has concluded capturing the first video, but has not yet completed customizing the first video. The server computing device may receive, at a second time after the first time and from the first client device, a dynamic overlay including customizations to be applied to the first video. The dynamic overlay may be transmitted separately from the first video. In examples, the server computing device generates a second video by applying the dynamic overlay to the first video. The server computing device then provides the second video to a second client device.

Sharing videos having dynamic overlays

Techniques are described that enable a user to edit and customize captions generated by a social networking system, such as transcriptions of an audio clip. In some cases, a social networking system receives, from a first user account, a video and an audio clip associated with the video, and determines that the audio clip contains speech. The social networking system may leverage a speech-to-text component to generate a first text caption based at least in part on the speech in the audio clip. The social networking system provides the first text caption to the first user account, and receives a user input to modify a word included in the first text caption. The social networking system generates a second text caption based at least in part on the user input, and provides the video, including the second text caption, to a second user account.

Caption customization and editing

An online system receives information describing (an) item(s) associated with an entity and a content item including an image. The online system accesses and applies a trained machine-learning model to predict a probability that the content item includes an image of an item associated with the entity. If the probability is at least a threshold probability, a link to a page associated with the item that includes a set of the information describing the item(s) is added to the content item by the online system. Responsive to receiving an interaction with the link from a user presented with the content item, the online system determines a measure of similarity between the item and each additional item based on the information describing the item(s), arranges the set of the information describing the item(s) within the page based on the measure(s) of similarity, and sends the page for display to the user.

Arranging information describing items within a page maintained in an online system based on an interaction with a link to the page

Utilization metrics associated with a plurality of eligible compute tasks executable on a plurality of field-programmable gate arrays are determined. The utilization metrics are evaluated to dynamically identify a reallocation of an identified field-programmable gate array resource of the field-programmable gate arrays to handle a selected eligible compute task of the eligible compute tasks. The identified field-programmable gate array resource is automatically reprogrammed to handle the selected eligible compute task.

Dynamic allocation of FPGA resources

A request to perform a compute task is received. A service to perform at least a portion of the compute task is identified, wherein the identified service is associated with a field-programmable gate array that has been programmed to perform at least the portion of the compute task. The identified service to perform at least the portion of the compute task of the request is invoked. At least the portion of the compute task is executed using the field-programmable gate array.

Hardware accelerated compute kernels

In some examples, methods and systems may institute technical fallback by determining, by a payment processing system, and based on analysis of the communication status indicator and the data obtained when a magnetic stripe of the payment object is introduced in magnetic stripe object reader, whether the payment object was swiped while an EMV object reader was communicatively coupled to the POS terminal. If the magnetic stripe of the payment object was swiped while the EMV object reader was connected to the POS terminal, the payment processing system extracts a transaction count indicating a number of times the customer has attempted to insert a chip of the payment object into the EMV object reader prior to swiping magstripe. By comparing the transaction count with a threshold count, the payment processing system authorizes the payment transaction as a technical fallback transaction if the transaction count is greater than the threshold count.

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San Francisco, CA, United States of America

Martin Mroz