Abstract: A system for data-driven, modular decision making and trajectory generation includes a computing system. A method for data-driven, modular decision making and trajectory generation includes: receiving a set of inputs; selecting a learning module such as a deep decision network and/or a deep trajectory network from a set of learning modules; producing an output based on the learning module; repeating any or all of the above processes; and/or any other suitable processes. Additionally or alternatively, the method can include training any or all of the learning modules; validating one or more outputs; and/or any other suitable processes and/or combination of processes.

Abstract: An autonomous system for washing and drying household laundry includes a plurality of autonomous combination washer and dryers each including a perforated drum disposed within a tub mated thereto at a drive end opposite an opening. A first air orifice is disposed through the tub. A detachable door includes a second air orifice configured to receive an air conduit such that air flows through the first and second air orifices during a drying cycle while the door seals the opening. A controller is configured to instruct a drive motor to spin the drum about a spin axis extending between the drive end and the opening. A driven carrier in operable communication with the controller slides along a raised rail disposed adjacent the plurality autonomous combination washer and dryers and retrieves, holds, and reseats a door in an opening of one of the plurality of autonomous combination washer and dryers.

Abstract: Disclosed is an improved approach for managing security alerts to automatically isolate malicious security alerts from benign alerts using an ensemble model of pattern recognition techniques. In some embodiments, the approach provides for automatically isolating security alerts of malicious attack from security alerts that correspond to undesirable, yet benign, activity in computer networks, cloud infrastructures and SAAS applications. Specifically, the approach provides for qualitative contextual assessments of these alerts using an ensemble of models. These ensemble models leverage a history of security events on a computer network, cloud infrastructure and SAAS applications to determine a level of relevance for received alerts and determine, based on that level of relevance, how or if they should be presented to an administrator.

Abstract: Disclosed is an improved approach for translating entity prioritization rules to a continuous numerical space. In some embodiments, the approach provided is a system for using qualitative prioritization criteria to train a system that generates quantitative urgency scores for entities. In some embodiments, this comprises an embedding scheme that enables the translation of entity information and their related alerts to a set of qualitative labels based on at least quantitative information. Generally, the system includes a set of analyst actions that establish desired mappings which are used to train a more general model that maps entity embeddings to responses. In some embodiments, the approach comprises one or more models that receive an entity embedding as an input and outputs a score that characterizes the urgency of the response warranted for that entity. In some embodiments, this is performed using various features (e.g., importance, actor type, velocity, and breadth).

Abstract: A contact center server provides to an agent device one or more automated response recommendations determined by an executable virtual assistant platform to correspond to customer message data received from a customer device as part of conversation data. Further, the contact center server receives a selection of one of the automated response recommendations from the agent device and identifies agent response data to the customer message data transmitted from the agent device to the customer device. Further, the contact center server, using one or more classification models, determines when there is a change between the selected response recommendation and the identified agent response data. Further, the contact center server associates one or more tags to the identified agent response data in the conversation data when the determination indicates the change.

Abstract: Systems and methods for inspecting aircraft via an unmanned aerial vehicle (UAV) system are disclosed. In some implementations a system, comprises an unmanned aerial vehicle (UAV) and a computing system configured to establish a coordinate system in a space exterior to the aircraft in a global positioning system (GPS) denied environment. The system also has a controller configured to navigate the UAV along a flight path proximate to a surface of an aircraft according to the established coordinate system and a camera configured to obtain image data. A light detection and ranging (LiDAR) sensor is configured to obtain LiDAR sensor data of an inspection location on the surface to determine a presence of damage.

Abstract: Provided is a system and method for processing Compliance Data Subject Requests (DSRs) across a network by providing an executable planning model that contains a DSR domain description language, a DSR action description language, DSR problem templates, and predetermined logical, temporal, and dependency constraints. The DSR domain description language includes domains, with each domain including a domain name, and an object hierarchy representing various objects and their inter-relationship within each domain. The DSR action description language includes at least DELETE, REMOVE, OBFUSCATE, and ENCRYPT actions. The DSR problem templates each correspond to an operation that operates upon a compliance object within one of the domains, using at least one of the DSR actions. An instance of DSR processing may comprise receiving from a user DSR that includes a user object on which DSR processing is required and automatically processing the user DSR by executing the executable planning model.

Abstract: Apparatuses, methods, program products, and systems are disclosed for machine learning based user targeting. An apparatus includes one or more memory devices in communication with one or more processing devices. An apparatus includes a subject module that receives subject data associated with a subject. Subject data includes data that describes a subject. An apparatus includes an affinity module that determines, using a machine learning model, an affinity level of each of one or more entities in relation to a subject. Subject data is provided as input to a machine learning model. An apparatus includes a campaign module that generates one or more promotional campaign strategies for a subject based at least in part on an affinity level of each of one or more entities in relation to a subject. One or more promotional campaign strategies target a subject to one or more entities.

Abstract: Systems and methods are disclosed for generating a 3D avatar and object fitting recommendations using a biomechanical analysis of observed actions with a focus on representing actions through computer-generated 3D avatars. Physical quantities of biomechanical actions can be measured from the observations, and the system can analyze these values, compare them to target or optimal values, and use the observations and known biomechanical capabilities to initiate a payment transaction for an object offered by a merchant that corresponds with the recommendation.

Abstract: Datasource processors may communicate with an artificial intelligence (AI) engine in order to generate, in parallel, object summaries from datasource objects received from datasources. Each object summary may include an object identifier, one or more local entities, and a mapping from each of the one or more local entities to one or more attributes. A global entity resolver may augment the object summaries by mapping each of the local entities to a global entity. Policy engines may evaluate, in parallel, the object summaries with respect to a security and/or privacy policy. If a security and/or privacy violation is recognized, a remediation measure may be applied in connection with the datasource object for which the security and/or privacy violation exists.

Abstract: A system for computer-aided triage includes and/or interfaces with a computing system. A method for computer-aided triage includes receiving a data packet including a set of images; and processing the set of images to determine a suspected condition and/or associated features. Additionally or alternatively, the method can include any or all of: preprocessing the set of images; triggering an action based on the suspected condition and/or associated features; determining a recipient based on the suspected condition; preparing a data packet for transfer; transmitting information to a device associated with the recipient; receiving an input from the recipient and triggering an action based on the input; aggregating data; and/or any other suitable processes.

Abstract: Systems and methods for training and utilizing constrained generative models in accordance with embodiments of the invention are illustrated. One embodiment includes a method for training a constrained generative model. The method includes steps for receiving a set of data samples from a first distribution, identifying a set of constraints from a second distribution, and training a generative model based on the set of data samples and the set of constraints.

Abstract: Systems and methods are disclosed for generating and providing guided practices and coaching feedback (e.g., illustrated in 3D images, video, or audio) that allow users performing sports activities to follow along to improve their physical movements in a digital environment. The feedback may be provided with a 3D avatar using a biomechanical analysis of observed actions with a focus on representing actions through computer-generated 3D avatars. Physical quantities of biomechanical actions can be measured from the observations, and the system can analyze these values, compare them to watchlist values, animate the 3D avatar based on the measurements, and display the 3D avatar on a computer-generated user interface.

Abstract: Systems and methods are disclosed for object fitting recommendations using a biomechanical analysis of observed actions. Physical quantities of biomechanical actions can be measured from the observations, and the system can analyze these values, compare them to target or optimal values, and use the observations and known biomechanical capabilities to generate the object fitting recommendations.

Abstract: Devices, systems, and methods for autonomously separating a mass of clean laundry into single articles for folding and/or packing include autonomous separating device. The device includes a platform including a plurality of sequential work volumes and a stationary floor extending between an inlet end and an outlet end of the platform, a plurality of arms disposed in series along the platform for rotating, tilting, extending, and retracting a terminal gripper of each arm into an associated work volume for grabbing at least one of a plurality of laundry articles and passing the at least one laundry article to an adjacent work volume for grasping and hoisting by an adjacent arm. The device includes a bin, platform, conveyor and/or repositioning robot adjacent the outlet end for receiving each single article one at a time as each separate article passes beyond the outlet end.

Abstract: System and method for determining physiological parameters of a person are disclosed. A physiological parameter may be obtained by analyzing a facial image of a person, and determining, from the facial image, a physiological parameter of the person by processing the facial image with a data processor. A neural network model such as regression deep learning convolutional neural network is used to predict the physiological parameter. An image processor screens out images which can't be recognized as facial images and adjust facial images to frontal facial images for predicting of physiological parameters.

Abstract: Systems and methods to maintain amends to an annotation as discrete chronological events are disclosed. Exemplary implementations may: obtain a selection of a first annotation template for a first annotation via a client computing platform; generate a root node based on the selection of the first annotation template; obtain a first command to update to the first annotation; append a first update node to the root node responsive to the first command; obtain a second command to update to the first annotation; append a second update node to the first node responsive to the second command; receive an indication to present the first annotation; generate, responsive to the indication, the first annotation by populating the first annotation template included in the root node based on the first node set and in sequential order indicated by the edges; and effectuate presentation of the first annotation.

Abstract: According to some aspects, techniques are provided for building and deploying a machine learning application that do not require a user to have expert knowledge of machine learning or programming. These techniques may be executed by a system that provides a graphical user interface which allows a user to visually define a workflow for a machine learning application, without requiring the user to be an expert in machine learning. The system may automatically represent the workflow as a specification that may be used to build and deploy a machine learning application. The system may automatically execute the workflow in a series of stages while managing data flow and execution context between the stages. Such an execution process may provide flexibility in execution so that a user can build a complex machine learning application without it being necessary for the user to have detailed knowledge of how execution is managed.

Abstract: Systems and methods are disclosed for receiving one or more electronic slide images associated with a tissue specimen, the tissue specimen being associated with a patient and/or medical case, partitioning a first slide image of the one or more electronic slide images into a plurality of tiles, detecting a plurality of tissue regions of the first slide image and/or plurality of tiles to generate a tissue mask, determining whether any of the plurality of tiles corresponds to non-tissue, removing any of the plurality of tiles that are determined to be non-tissue, determining a prediction, using a machine learning prediction model, for at least one label for the one or more electronic slide images, the machine learning prediction model having been generated by processing a plurality of training images, and outputting the prediction of the trained machine learning prediction model.

Abstract: A computer-implemented method of reviewing digital pathology data may include receiving a digital pathology image into a digital storage device, the digital pathology image being associated with a patient, providing for display the digital pathology image on a display, pairing the digital pathology image with a physical token of the digital pathology image in an interactive system, receiving one or more commands from the interactive system, determining one or more manipulations or modifications to the displayed digital pathology image based on the one or more commands, and providing for display a modified digital pathology image on the display according to the determined one or more manipulations or modifications.