Abstract: Methods, systems, devices, and computer-readable media for generating authentication policy recommendations and insights using generative AI are described. An authentication policy recommendation system associated with an identity management system may receive a request from a client organization for an authentication policy configuration recommendation for an application associated with the client organization. One or more recommended authentication policy rules may be generated for the application using a machine learning model (such as a large-learning model). The model may output the authentication policy rules in a machine-readable format. Based on an analysis of applying information about the client organization to the model-generated recommended authentication policy rule to generate a context-specific recommended authentication policy rule, an impact of implementing the context-specific recommended authentication policy rule may be determined and output.

Abstract: A method of accessing data from a data storage device includes transmitting a request including a first part to the data storage device, based on availability of memory in host device. The first part includes data access information and one or more parameters for accessing data from data storage device. The method further includes receiving a first notification from the data storage device, indicating a transfer of the data to a storage buffer associated with the data storage device. The method further includes accessing the data from the data storage device by transmitting second part of request to the data storage device. The second part includes buffer information associated with a host buffer of host device. The data is transferred by the data storage device from the storage buffer to the host buffer of host device using buffer information.

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 device may receive a three-dimensional (3D) computer-aided design (CAD) model, and may generate an assembly graph with nodes that represent components and edges that represent contact between the components. The device may generate component graphs for the components, and may generate an assembly descriptor based on the assembly graph and the component graphs. The device may process the assembly descriptor, with a graph convolution network model, to generate node embeddings, and may apply pooling to the node embeddings to generate graph embeddings. The device may calculate a cross attention between the components to generate component interrelations, and may utilize the graph embeddings and the component interrelations to predict links between the components. The device may predict poses and joint axes for the components, and may generate assembly instructions based on the graph embeddings, the component interrelations, the links, the poses, and the joint axes.

Abstract: A method of accessing data from a data storage device includes transmitting a request including a first part to the data storage device, based on availability of memory in host device. The first part includes data access information and one or more parameters for accessing data from data storage device. The method further includes receiving a first notification from the data storage device, indicating a transfer of the data to a storage buffer associated with the data storage device. The method further includes accessing the data from the data storage device by transmitting second part of request to the data storage device. The second part includes buffer information associated with a host buffer of host device. The data is transferred by the data storage device from the storage buffer to the host buffer of host device using buffer information.

Abstract: Implementations are directed to processing a content object model through a ML model to provide a set of base content feature representations, processing a style object model through the ML model to provide a set of base style feature representations, executing iterations including: generating, by the ML model, a set of stylized feature representations for an initial stylized object model, the initial stylized object model having one or more adjusted parameters relative to a previous iteration, determining a total loss based on the set of stylized feature representations, the set of base content feature representations, and the sets of base style feature representations, and determining that the total loss is non-optimized, and in response, initiating a next iteration, executing an iteration including determining that the total loss is optimized, and in response providing the initial stylized object model as output of the iterative process.

Abstract: In some examples, artificial intelligence-based intelligent programming assistance may include ascertaining, for a software program that is to be completed, code for the program, and identifying, based on an analysis of error message-based contextual information associated with the ascertained code, an error in the ascertained code. A resolution to the identified error may be generated, and the code may be modified to resolve the identified error, Based on an analysis of problem description-based contextual information associated with the ascertained code, a next token associated with the ascertained code may be generated, and used to generate further code for the program. A performance of a user may be analyzed with respect to generation of the code for the program to generate feedback for the user. A query associated with the program may be ascertained from the user, and classified by utilizing an ontology to generate a response to the query.

Abstract: A method performed by a storage device for reducing latency of read operations performed on the storage device, includes: transmitting Host Memory Buffer (HMB) destination information to a host device. The HMB destination information relates to a read destination space reserved within a HMB of the host device. The method also includes receiving a read request from the host device for performing at least one data read operation on a memory of the storage device. The read request specifies data to be read from the memory of the storage device. The method further includes: performing the at least one data read operation on the memory for reading the data specified in the read request and transmitting the data read from the memory to the read destination space reserved within the HMB of the host device.

Abstract: Systems and methods that support functionality for performing disassembly sequence planning are disclosed. The systems and methods may obtain 3D model data for a product to be disassembled and may convert the 3D model to one or more graph-based representations. An encoder is provided to generate a set of embeddings based on the one or more graph-based representations. A decoder is provided to generate disassembly data based on the set of embeddings. In an aspect, the disassembly data may indicate an order in which each component is to be removed during disassembly of the product and a direction in which each component should be removed.

Abstract: A method for adapting text message communications in electronic financial transactions to accommodate users with color vision deficiency. During a registration process, a primary account number, a selection of an enrollment in a color vision deficiency accommodation service, a selection of a preferred critical text color, and an electronic device identifier are received from a user. During a subsequent electronic card present transaction process, the primary account number and the electronic device identifier are received from the user via a payment gateway webpage. During an adaptation process, a text of a message containing a critical text element communicated to the user regarding the transaction is adapted by presenting the critical text element in the preferred critical text color previously selected by the user. The message may be adapted within a template, or the message may be dynamically adapted by searching the message and identifying the critical text element.

Abstract: A method for adapting text message communications in electronic financial transactions to accommodate users with color vision deficiency. During a registration process, a primary account number, a selection of an enrollment in a color vision deficiency accommodation service, a selection of a preferred critical text color, and an electronic device identifier are received from a user. During a subsequent electronic card present transaction process, the primary account number and the electronic device identifier are received from the user via a payment gateway webpage. During an adaptation process, a text of a message containing a critical text element communicated to the user regarding the transaction is adapted by presenting the critical text element in the preferred critical text color previously selected by the user. The message may be adapted within a template, or the message may be dynamically adapted by searching the message and identifying the critical text element.

Abstract: Implementations are directed to processing a content object model through a ML model to provide a set of base content feature representations, processing a style object model through the ML model to provide sets of base style feature representations, executing iterations including: generating, by the ML model, sets of stylized feature representations for an initial stylized object model, the initial stylized object model having one or more adjusted parameters relative to a previous iteration, determining a total loss based on the sets of stylized feature representations, the set of base content feature representations, and the sets of base style feature representations, and determining that the total loss is non-optimized, and in response, initiating a next iteration, executing an iteration of the iterative process, the iteration including determining that the total loss is optimized, and in response providing the initial stylized object model as output of the iterative process.

Abstract: In some implementations, a robot host may receive a video associated with assembly using a plurality of sub-objects. The robot host may determine spatio-temporal features based on the video and may identify a plurality of actions represented in the video based on the spatio-temporal features. The robot host may map the plurality of actions to the plurality of sub-objects to generate an assembly plan and may combine output from a point cloud model and output from a color embedding model to generate a plurality of sets of coordinates corresponding to the plurality of sub-objects. The robot host may perform object segmentation to estimate a plurality of grip points and a plurality of widths corresponding to the plurality of sub-objects. Accordingly, the robot host may generate instructions, for robotic machines, based on the assembly plan, the plurality of sets of coordinates, the plurality of grip points, and the plurality of widths.

Abstract: In some examples, generative network-based floor plan generation may include receiving, for a floor plan that is to be classified, a layout graph for which user constraints are encoded as a plurality of room types. The user constraints may include spatial connections therebetween. Based on the layout graph, embedding vectors for each room type of the plurality of room types may be generated. Bounding boxes and segmentation masks may be determined for each room embedding from the layout graph, and based on an analysis of the embedding vectors. A space layout may be generated by combining the bounding boxes and the segmentation masks. The floor plan may be generated based on an analysis of the space layout, and synthesized based on the space layout, noise, and a contextual graph embedding to generate a synthesized floor plan. The synthesized floor plan may be classified as authentic or not-authentic.

Abstract: A method for securely presenting transaction card data of a transaction card includes receiving, by a server, a pairing request to pair the transaction card with a user device. Based on the pairing request, the transaction card is paired with the user device by way of a pairing identifier. The server receives, from the user device, a data presentation request to present the transaction card data on the user device. In response to the data presentation request, the server validates the pairing between the user device and the transaction card based on the pairing identifier. The server communicates, to the user device, the transaction card data based on the validation of the pairing between the user device and the transaction card. The communicated transaction card data is presented on a user interface rendered on the user device.

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: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support mechanisms for generating a feasible assembly plan for a product based on data analytics. In aspects, information on components of a product is obtained from one or more product models (e.g., a three-dimensional (3D) computer aided design (CAD) model) that define the individual components of the product. The individual component information may be used to represent the assembly of the product as an assembly graph, in which each node of the assembly graph represents one of the components of the product to be assembled. The assembly graph is passed through a set of data analytics modules to generate the feasible assembly plan, or assembly sequence, as a series of sequential contact predictions, wherein each contact prediction identifies a component to be connected to one or more other components of the product.

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: 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 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).