Abstract: The disclosed system and method provide a way to create, update, and execute dynamic goal plans. Updating a dynamic goal plan may be based on the initial sequence of actions of the goal plan as well as the corresponding states of the actions. By using a sequence to sequence model, a goal plan can still be processed when the length of the input (initial sequence of actions) differs from the length of the output (updated sequence of actions). A sequence to sequence model can determine the interdependencies between actions that can contribute to the optimal order in which actions can efficiently be performed. A single layer neural network or clustering can be used to approximate the state of a goal plan that may be capable infinite states. This approximation improves accuracy in capturing the state of a goal plan, thereby improving accuracy in predicting the future state of a system, which can help with planning (e.g., gathering resources in advance).

Abstract: Implementations are directed to receiving a target object model representative of a target object, receiving a source object model representative of a source object, defining a set of target segments and a set of source segments using a segmentation machine learning (ML) model, for each target segment and source segment pair in a set of target segment and source segment pairs, generating a compatibility score representing a degree of similarity between a target segment and a source segment, the compatibility score calculated based on global feature representations of each of the target segment and the source segment, each global feature representation determined from a ML model, selecting a source segment for style transfer based on compatibility scores, and merging the source segment into the target object model to replace a respective target segment within the target object model and providing a stylized target object model.

Abstract: Implementations of the present disclosure include receiving a content image depicting a first set of objects and a style image depicting a second set of objects and a collective style, processing, by the design generation assistant, the content image using one or more ML models to determine a first sub-set of objects, to which a style is to be transferred from the style image, the first sub-set of objects including fewer objects than the first set of objects, processing, by the design generation assistant, the style image using the ML models to determine a second sub-set of objects, from which the style is to be transferred, and generating, by the design generation assistant, an output image with stylized objects, each stylized object including an object in the first sub-set of objects having the style of the one or more objects in the second sub-set of objects.

Abstract: An interactive troubleshooting assistant and method for troubleshooting a system in real time to repair (fix) one or more problems in a system is disclosed. The interactive troubleshooting assistant and method may include receiving multimodal inputs from sensors, wearable devices, a person, etc. that may be input into a feature extractor including attention layers and pre-processing units of a cloud computing system hosted by one or more servers, such as a private cloud system. A pre-processing unit converts the raw multimodal input into a structed form so that an attention layer can give weights to features provided by the pre-processing unit according to their importance. The weighted extracted features may be provided to an actions predictor. The actions predictor generates the most suitable action based on the weighted extracted features generated by the feature extractor based on the multimodal inputs.

Abstract: An interactive troubleshooting assistant and method for troubleshooting a system in real time to repair (fix) one or more problems in a system is disclosed. The interactive troubleshooting assistant and method may include receiving multimodal inputs from sensors, wearable devices, a person, etc. that may be input into a feature extractor including attention layers and pre-processing units of a cloud computing system hosted by one or more servers, such as a private cloud system. A pre-processing unit converts the raw multimodal input into a structed form so that an attention layer can give weights to features provided by the pre-processing unit according to their importance. The weighted extracted features may be provided to an actions predictor. The actions predictor generates the most suitable action based on the weighted extracted features generated by the feature extractor based on the multimodal inputs.

Abstract: An interactive troubleshooting assistant and method for troubleshooting a system in real time to repair (fix) one or more problems in a system is disclosed. The interactive troubleshooting assistant and method may include receiving multimodal inputs from sensors, wearable devices, a person, etc. that may be input into a feature extractor including attention layers and pre-processing units of a cloud computing system hosted by one or more servers, such as a private cloud system. A pre-processing unit converts the raw multimodal input into a structed form so that an attention layer can give weights to features provided by the pre-processing unit according to their importance. The weighted extracted features may be provided to an actions predictor. The actions predictor generates the most suitable action based on the weighted extracted features generated by the feature extractor based on the multimodal inputs.

Abstract: The present disclosure relates to a system, and method for computer-based recursive learning of artificial intelligence (AI) apprentice agents. The system includes a system circuitry in communication with a database and a memory. The system circuitry is configured to receive a new data-structure comprising one or more inputs and a goal, and convert, using a perception agent, the one or more inputs of the new data-structure into one or more input feature parameters of the new data-structure. The system circuitry is configured to obtain, using a reasoning agent, an action for the new data-structure, and determine, using an evaluation agent, whether the action for the new data-structure generates the goal of the new data-structure. When it is determined that the action generates the goal of the new data-structure, the system circuitry is further configured to store the new data-structure in the database.

Abstract: Examples of article designing are described herein. In an example, image data, margin data, and sales data corresponding to a plurality of articles may be obtained. The obtained data may be analyzed to identify a first article image of a first article and a second article image of a second article. The first article image is integrated with the second article image, based on an article attribute to generate a transformed article image. The article attribute may be an attribute having a maximum likelihood of making the article popular. The transformed article image may be filtered based on predefined filtering rules to obtain a curated article design image. The curated article design image is assessed to generate a design score indicative of a popularity and/or a sellability of an article, and a design of the article may be selected for a post design selection process, based on the design score.

Abstract: A task transformation architecture meets the technical challenges of geographically distributed task assignment and completion, e.g., by crowd-sourced workers who are external to any given enterprise that needs the task completed. The architecture delivers task information to the workers while maintaining confidentiality. In one aspect, the architecture determines when task descriptions include confidential information, transforms the task to remove the confidential information, and delivers the transformed task to the worker. When the worker responds with a completed project, e.g., source code for executing the transformed task, the architecture transforms the completed project into a form that restores the confidential information.

Abstract: According to an example, with respect to knowledge graph based learning content generation, a plurality of concepts may be extracted from a plurality of documents. A word embedding similarity, and pointwise mutual information may be determined between each concept of the plurality of concepts. A concept similarity may be determined between each concept of the plurality of concepts, and a plurality of concept pairs that include similar concepts may be identified. A relationship may be determined between concepts for each concept pair of the plurality of concept pairs, and a determination may be made as to whether a concept of a concept pair is a pre-requisite of another concept of the concept pair to generate a knowledge graph. Based on the knowledge graph, a plurality of attributes and a learning goal for a learner, a concept of the plurality of concepts that matches the learning goal may be determined.

Abstract: Implementations of the present disclosure include receiving a content image depicting a first set of objects and a style image depicting a second set of objects and a collective style, processing, by the design generation assistant, the content image using one or more ML models to determine a first sub-set of objects, to which a style is to be transferred from the style image, the first sub-set of objects including fewer objects than the first set of objects, processing, by the design generation assistant, the style image using the ML models to determine a second sub-set of objects, from which the style is to be transferred, and generating, by the design generation assistant, an output image with stylized objects, each stylized object including an object in the first sub-set of objects having the style of the one or more objects in the second sub-set of objects.

Abstract: The global proliferation of high speed communication networks has created unprecedented opportunities for geographically distributed resource identification, evaluation, selection, and allocation. However, while the opportunities exist and continue to grow, the realization of those opportunities has fallen behind. A dynamic resource assessment system helps to solve the enormous technical challenges of finding the resources, evaluating the resources, and determining how to allocate the resources to achieve the highest likelihood of successfully completing the task.

Abstract: A method, computer program product, and computing system for obtaining, by a computing device, encounter information of a patient encounter, wherein the encounter information may include audio encounter information obtained from at least a first encounter participant. The audio encounter information obtained from at least the first encounter participant may be processed. A user interface may be generated displaying a plurality of layers associated with the audio encounter information obtained from at least the first encounter participant.

Abstract: A method, computer program product, and computing system for obtaining, by a computing device, encounter information of a patient encounter, wherein the encounter information may include audio encounter information obtained from at least a first encounter participant. The audio encounter information obtained from at least the first encounter participant may be processed. An alert may be generated to obtain additional encounter information of the patient encounter.

Abstract: A method, computer program product, and computing system for obtaining, by a computing device, encounter information of a patient encounter, wherein the encounter information may include audio encounter information obtained from at least a first encounter participant. The audio encounter information obtained from at least the first encounter participant may be processed. A user interface may be generated displaying a plurality of layers associated with the audio encounter information obtained from at least the first encounter participant, wherein at least one of the plurality of layers is one of exposed to the user interface and not exposed to the user interface based upon, at least in part, a confidence level.

Abstract: A method, computer program product, and computing system for: receiving an initial portion of an encounter record; processing the initial portion of the encounter record to generate initial content for a medical report; receiving one or more additional portions of the encounter record; and processing the one or more additional portions of the encounter record to modify the medical report.

Abstract: A method, computer program product, and computing system for obtaining, by a computing device, encounter information of a patient encounter, wherein the encounter information may include audio encounter information obtained from at least a first encounter participant. The audio encounter information obtained from at least the first encounter participant may be processed. A user interface may be generated displaying a plurality of layers associated with the audio encounter information obtained from at least the first encounter participant. A user input may be received from a peripheral device to navigate through each of the plurality of layers associated with the audio encounter information displayed on the user interface.

Abstract: A crowdsource assistant provides guidance to crowdsource resources navigating a crowdsource platform to bid for and be accepted to accomplish tasks. The crowdsource assistant utilizes machine learning to train similarity computation models that improve the crowdsource assistant's computing capability to generate more relevant recommendations to a resource in a faster and more efficient manner.

Abstract: A call center system for reducing communication latency includes an input/output (I/O) interface for receiving one or more queries from a customer terminal; a processor in communication with the I/O interface; and non-transitory computer readable media in communication with the processor that stores instruction code. The instruction code is executed by the processor and causes the processor to route the one or more queries to a plurality of artificial intelligent (AI) logic modules and receive, from one or more of the AI logic modules, information that facilitates providing, by a call center agent, responses to the one or more queries. The processor also routes actual responses to the one or more queries made by the call center agent to the AI logic modules; and receives from at least one AI logic module one or more scores associated with one or more metrics that rate different aspects if the actual responses.

Abstract: A task transformation architecture meets the technical challenges of geographically distributed task assignment and completion, e.g., by crowd-sourced workers who are external to any given enterprise that needs the task completed. The architecture delivers task information to the workers while maintaining confidentiality. In one aspect, the architecture determines when task descriptions include confidential information, transforms the task to remove the confidential information, and delivers the transformed task to the worker. When the worker responds with a completed project, e.g., source code for executing the transformed task, the architecture transforms the completed project into a form that restores the confidential information.