Abstract: A device-management system performs processing, such as audio processing, in an instance of a virtual machine corresponding to a functionally limited (local) device. To register the user device, the device-management system receives a registration request that includes device information, encryption data, and an indication of an associated user account. The device-management system then sends this registration data to a service-provider system, which returns a shared encryption key. The device-management system and the user device may use this shared encryption key to securely communicate. The device-management system may de-allocate the instance upon detecting a period of inactivity of the user device and may re-allocate the instance when new activity is detected. The device-management system may further determine when and if audio data to be sent to the user device is encoded using a codec not implemented by the user device.

Abstract: Certain embodiments involve using a machine-learning tool to generate metadata identifying segments and topics for text within a document. For instance, in some embodiments, a text processing system obtains input text and applies a segmentation-and-labeling model to the input text. The segmentation-and-labeling model is trained to generate a predicted segment for the input text using a segmentation network. The segmentation-and-labeling model is also trained to generate a topic for the predicted segment using a pooling network of the model to the predicted segment. The output of the model is usable for generating metadata identifying the predicted segment and the associated topic.

Abstract: Techniques are provided for generating a digital image of simulated handwriting using an encoder-decoder neural network trained on images of natural handwriting samples. The simulated handwriting image can be generated based on a style of a handwriting sample and a variable length coded text input. The style represents visually distinctive characteristics of the handwriting sample, such as the shape, size, slope, and spacing of the letters, characters, or other markings in the handwriting sample. The resulting simulated handwriting image can include the text input rendered in the style of the handwriting sample. The distinctive visual appearance of the letters or words in the simulated handwriting image mimics the visual appearance of the letters or words in the handwriting sample image, whether the letters or words in the simulated handwriting image are the same as in the handwriting sample image or different from those in the handwriting sample image.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for classifying an input image utilizing a classification model conditioned by a generative model and/or self-supervision. For example, the disclosed systems can utilize a generative model to generate a reconstructed image from an input image to be classified. In turn, the disclosed systems can combine the reconstructed image with the input image itself. Using the combination of the input image and the reconstructed image, the disclosed systems utilize a classification model to determine a classification for the input image. Furthermore, the disclosed systems can employ self-supervised learning to cause the classification model to learn discriminative features for better classifying images of both known classes and open-set categories.

Abstract: This disclosure describes methods, non-transitory computer readable storage media, and systems that extract a definition for a term from a source document by utilizing a single machine-learning framework to classify a word sequence from the source document as including a term definition and to label words from the word sequence. To illustrate, the disclosed system can receive a source document including a word sequence arranged in one or more sentences. The disclosed systems can utilize a machine-learning model to classify the word sequence as comprising a definition for a term and generate labels for the words from the word sequence corresponding to the term and the definition. Based on classifying the word sequence and the generated labels, the disclosed system can extract the definition for the term from the source document.

Abstract: Techniques are provided for generating a digital image of simulated handwriting using an encoder-decoder neural network trained on images of natural handwriting samples. The simulated handwriting image can be generated based on a style of a handwriting sample and a variable length coded text input. The style represents visually distinctive characteristics of the handwriting sample, such as the shape, size, slope, and spacing of the letters, characters, or other markings in the handwriting sample. The resulting simulated handwriting image can include the text input rendered in the style of the handwriting sample. The distinctive visual appearance of the letters or words in the simulated handwriting image mimics the visual appearance of the letters or words in the handwriting sample image, whether the letters or words in the simulated handwriting image are the same as in the handwriting sample image or different from those in the handwriting sample image.

Abstract: Techniques are provided for generating a digital image of simulated handwriting using an encoder-decoder neural network trained on images of natural handwriting samples. The simulated handwriting image can be generated based on a style of a handwriting sample and a variable length coded text input. The style represents visually distinctive characteristics of the handwriting sample, such as the shape, size, slope, and spacing of the letters, characters, or other markings in the handwriting sample. The resulting simulated handwriting image can include the text input rendered in the style of the handwriting sample. The distinctive visual appearance of the letters or words in the simulated handwriting image mimics the visual appearance of the letters or words in the handwriting sample image, whether the letters or words in the simulated handwriting image are the same as in the handwriting sample image or different from those in the handwriting sample image.

Abstract: Certain embodiments involve transforming an electronic document into a tagged electronic document. For instance, an electronic document processing application generates a tagged electronic document from an input electronic document. The electronic document processing application accesses one or more feature maps that identify, via a set of object-recognition rules, identified objects in the electronic document. The electronic document processing application also obtains a heat map of the electronic document that represents attributes in a pixel-wise manner. The electronic document processing application computes a tag by applying a fusion deep learning model to the one or more feature maps and the heat map. The electronic document processing application generates the tagged electronic document by applying the tag to the electronic document.

Abstract: In some embodiments, a computing system computes tags for an electronic document. The computing system identifies sets of objects for the electronic document by applying a set of object-recognition rules to the electronic document, with each object-recognition rule generating a set of identified objects. The computing system generates feature maps that represent a set of identified objects. The computing system generates a heat map that identifies attributes of the electronic document including object candidates of the electronic document by applying a page-segmentation machine-learning model to the electronic document. The computing system computes a tag by applying a fusion deep learning module to the feature map and the heat map to correlate a document object identified by the feature map with an attribute of the electronic document identified by the heat map. The computing system generates the tagged electronic document by applying the tag to the electronic document.

Abstract: A device-management system performs audio processing, such as acoustic echo cancellation or beamforming, in a computing-resource allocation corresponding to a functionally limited device. The device-management system may be a locally-connected network device that is in communication with one or more user devices; the device-management system may also or instead be a remote device that communicates with the user devices using the locally-connected network device. The device-management system may receive audio data from one or more microphones of one or more user devices. To perform acoustic echo cancellation, the device-management system may receive and process time data corresponding to a time of output of audio by the user device.

Abstract: In some embodiments, a computing system computes tags for an electronic document. The computing system identifies sets of objects for the electronic document by applying a set of object-recognition rules to the electronic document, with each object-recognition rule generating a set of identified objects. The computing system generates feature maps that represent a set of identified objects. The computing system generates a heat map that identifies attributes of the electronic document including object candidates of the electronic document by applying a page-segmentation machine-learning model to the electronic document. The computing system computes a tag by applying a fusion deep learning module to the feature map and the heat map to correlate a document object identified by the feature map with an attribute of the electronic document identified by the heat map. The computing system generates the tagged electronic document by applying the tag to the electronic document.

Abstract: In an embodiment, a computer system comprises one or more computer processors configured with a message transfer application; a message transfer/vision processing (MT/VP) interface coupled to the one or more computer processors and interposed between the message transfer application and a vision processing computer, wherein the MT/VP interface performs operations comprising: extracting risk indicator data from a message that is in transit to a recipient computer on a computer network; in response to the risk indicator data matching a message risk criterion, transmitting an image address for an image of interest coupled to the message or the image of interest to the vision processing computer; receiving, from the vision processing computer, a label that semantically describes visual content of the image of interest; using the label, querying a set of correlation data to determine a reference address that is associated with the label; in response to the image address matching the reference address, transmitting

Abstract: Determining whether a subject tax return is fraudulent includes extracting from the subject tax return information and identifying one or more subject nodes based on the extracted information. Separately, a plurality of external nodes is generated based upon previously filed tax returns. At least a portion of the plurality of external nodes is fraud-indicative nodes. The subject nodes are compared to the external nodes to identify shared relationships of related information, such as a tax return related to an external node having the same bank account information as the subject tax return related to the subject node. Based upon shared information, links are determined to indicate whether the subject node is indicative of fraud.

Abstract: Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of determining a product shipping cost for a product and coordinating a display on the electronic device of the user of the product shipping cost of a carrier shipping cost that is less than the maximum shipping cost for the product. The maximum shipping cost for the product can be determined by determining carriers comprising a transit time for shipping the product that is less than or equal to a shipping time requirement of a service level agreement with the user, determining a baseline cost for shipping the product, retrieving a fixed threshold cost for the product, and combining the fixed threshold cost and the baseline cost to determine a maximum shipping cost for the product.

Abstract: Systems and methods for discovering and interacting with video on a frame level are disclosed. A person viewing a video can capture a frame from a video using a mobile device. The captured frame and information about items within the frame are displayed on the user's mobile device. The user can learn about the items or interact with the displayed items such as purchasing the items using the mobile device.

Abstract: Systems and methods for discovering and interacting with video on a frame level are disclosed. A person viewing a video can capture a frame from a video using a mobile device. The captured frame and information about items within the frame are displayed on the user's mobile device. The user can learn about the items or interact with the displayed items such as purchasing the items using the mobile device.

Abstract: Electronic greeting cards include a greeting card with two or more interconnected panels in combination with a digital multimedia player device which includes an electronic display and an audio output, and circuitry which is operative to receive, store and play digital multimedia files and content. The various greeting card structures cover and encapsulate or otherwise house and adorn the digital multimedia player. Digital files are loaded on to the digital multimedia player by a connection to a network, or directly from a data storage device such as an SD card or USB connection or compact flash which interfaces with a port in the digital multimedia player. Pre-recorded digital multimedia greeting card content is either pre-loaded on a portable data storage device, or selected for purchase and downloaded or transferred for replay by the digital multimedia player of the electronic greeting card.

Abstract: Single-image super-resolution (SISR) is the problem of generating a high resolution image from a single low resolution image. The SISR technique known as neighbor embedding utilizes a training ensemble of pairs of low and high resolution image patches, where the patches in a given pair represent the same image region. The present invention improves upon prior neighbor embedding algorithms by offering a practical, computationally efficient method of neighbor embedding for generating a high resolution version of a low resolution image. The technique may also be applied to generate high resolution versions of low resolution text images for subsequent input into OCR engines. OCR character error rates found on the high resolution images are drastically lower than those found when OCR is applied to the original low resolution text images.

Abstract: Electronic greeting cards include a greeting card with two or more interconnected panels in combination with a digital multimedia player device which includes an electronic display and an audio output, and circuitry which is operative to receive, store and play digital multimedia files and content. The various greeting card structures cover and encapsulate or otherwise house and adorn the digital multimedia player. Digital files are loaded on to the digital multimedia player by a connection to a network, or directly from a data storage device such as an SD card or USB connection or compact flash which interfaces with a port in the digital multimedia player. Pre-recorded digital multimedia greeting card content is either pre-loaded on a portable data storage device, or selected for purchase and downloaded or transferred for replay by the digital multimedia player of the electronic greeting card.

Abstract: A method is provided for sharing a personalized homepage with a plurality of users, the method comprising: providing a personalized homepage created by a first user from among the plurality of users, the personalized homepage comprising personal content; upon storing the personalized homepage, sending a first indication to all other users from among the plurality of users, wherein that first indication relates to the creation of said personalized homepage; receiving from at least one second user from among the plurality of users, one or more comments associated with at least some of the personal content comprised in the first user's personalized homepage; and providing the first user with the one or more comments received from the at least one second user.