Abstract: Method and apparatus for controlling delivery of an item. An electronic order specifying the item is received. An item profile for the first item is retrieved. A first instance of positional data for the user and a pattern of delivery preferences of the user are determined. Embodiments determine a first location for delivering the package to the user, based on the item profile, the first instance of positional data for the user and the pattern of delivery preferences of the first user. Shipment of the first item towards the first location is initiated, and based on subsequent network communications with an electronic device associated with the user, a second instance of positional data is determined. Delivery of the first item is facilitated to one of the first location corresponding to the first instance of positional data and a second location corresponding to the instance of positional data.

Abstract: Examples described herein provide a digital assistant crafting a response based on target sentiment identification from user input. The digital assistant receives unstructured data input and identifies a segment of the input that includes a facet item. A sentiment associated with the facet item in the segment is identified and classified to identify a targeted sentiment directed towards the facet item. A response is generated based on the targeted sentiment and the facet item.

Abstract: Disclosed are various embodiments for routing communications to service agents based on a workflow. A computing device identifies a user intent object corresponding to a user interaction with the computing device. The computing device then generates a search criterion based on the identified user intent object and uses the search criterion for identifying the user intent in subsequent user interactions. After identifying the user intent, the computing device can identify a workflow corresponding to the user intent object. Later, the computing device can process one or more tasks associated with the workflow.

Abstract: Examples are generally directed towards context-sensitive generation of conversational responses. Context-message-response n-tuples are extracted from at least one source of conversational data to generate a set of training context-message-response n-tuples. A response generation engine is trained on the set of training context-message-response n-tuples. The trained response generation engine automatically generates a context-sensitive response based on a user generated input message and conversational context data. A digital assistant utilizes the trained response generation engine to generate context-sensitive, natural language responses that are pertinent to user queries.

Abstract: Disclosed are various embodiments for navigating a workflow using a text messaging interface. A client device executes an application. The application renders the messaging interface on a display. The application also renders within the messaging interface a response to a previous selection of a first user interface element. The application further renders a second user interface element within the messaging interface that, when manipulated, causes the application to remove the response and the second user interface element from the messaging interface and also to rerender the first user interface element within the messaging interface.

Abstract: Disclosed are various embodiments for routing communications to service agents based on a workflow. A computing device identifies a user intent object corresponding to a user interaction with the computing device. The computing device then identifies a workflow corresponding to the user intent object. Later, the computing device identifies a service agent capable of completing the workflow. Then, the computing device routes a user to the service agent.

Abstract: Examples described herein dynamically personalize a digital assistant for a specific user, creating a personal connection between the digital assistant and the user. The digital assistant accesses user activity and generates queries based on the user activity. The digital assistant facilitates natural language conversations as machine learning sessions between the digital assistant and the user using the one or more queries to learn the user's preferences and receives user input from the user during the learning session in response to the queries. The digital assistant dynamically updates a personalized profile for the user based on the user input during the natural language conversations.

Abstract: This invention relates to polypropylene fibers and fabrics containing polypropylene fibers, the fibers comprising propylene polymers comprising at least 50 mol % propylene, said polymers having: a) a melt flow rate (MFR, ASTM 1238, 230° C., 2.16 kg) of about 10 dg/min to about 25 dg/min; b) a dimensionless Stress Ratio/Loss Tangent Index R2 [defined by Eq. (8)] at 190° C. from about 1.5 to about 30; c) an onset temperature of crystallization under flow, Tc,rheol, (as determined by SAOS rheology, 190° C., 1° C./min, where said polymer has 0 wt % nucleating agent present), of at least about 123° C.; d) an average meso run length determined by 13C NMR of at least about 55 or higher; and e) optionally, a loss tangent, tan ?, [defined by Eq. (2)] at an angular frequency of 0.1 rad/s at 190° C. from about 14 to about 70.

Abstract: Examples are generally directed towards context-sensitive generation of conversational responses. Context-message-response n-tuples are extracted from at least one source of conversational data to generate a set of training context-message-response n-tuples. A response generation engine is trained on the set of training context-message-response n-tuples. The trained response generation engine automatically generates a context-sensitive response based on a user generated input message and conversational context data. A digital assistant utilizes the trained response generation engine to generate context-sensitive, natural language responses that are pertinent to user queries.

Abstract: Examples are generally directed towards automatic assessment of machine generated conversational responses. Context-message-response n-tuples are extracted from at least one source of conversational data to generate a set of multi-reference responses. A response in the set of multi-reference responses includes it context-message data pair and rating. The rating indicates a quality of the response relative to the context-message data pair. A response assessment engine generates a metric score for a machine-generated response based on an assessment metric and the set of multi-reference responses. The metric score indicates a quality of the machine-generated conversational response relative to a user-generated message and a context of the user-generated message. A response generation system of a computing device, such as a digital assistant, is optimized and adjusted based on the metric score to improve the accuracy, quality, and relevance of responses output to the user.

Abstract: This invention relates to polypropylene fibers and fabrics containing polypropylene fibers, the fibers comprising propylene polymers comprising at least 50 mol % propylene, said polymers having: a) a melt flow rate (MFR, ASTM 1238, 230° C., 2.16 kg) of about 10 dg/min to about 25 dg/min; b) a dimensionless Stress Ratio/Loss Tangent Index R2 [defined by Eq. (8)] at 190° C. from about 1.5 to about 30; c) an onset temperature of crystallization under flow, Tc,rheol, (as determined by SAOS rheology, 190° C., 1° C./min, where said polymer has 0 wt % nucleating agent present), of at least about 123° C.; d) an average meso run length determined by 13C NMR of at least about 55 or higher; and e) optionally, a loss tangent, tan ?, [defined by Eq. (2)] at an angular frequency of 0.1 rad/s at 190° C. from about 14 to about 70.

Abstract: This invention relates to polypropylene fibers and fabrics containing polypropylene fibers, the fibers comprising propylene polymers comprising at least 50 mol % propylene, said polymers having: a) a melt flow rate (MFR, ASTM 1238, 230° C., 2.16 kg) of about 10 dg/min to about 25 dg/min; b) a dimensionless Stress Ratio/Loss Tangent Index R2 [defined by Eq. (8)] at 190° C. from about 1.5 to about 30; c) an onset temperature of crystallization under flow, Tc,rheol, (as determined by SAOS rheology, 190° C., 1° C./min, where said polymer has 0 wt % nucleating agent present), of at least about 123° C.; d) an average meso run length determined by 13C NMR of at least about 55 or higher; and e) optionally, a loss tangent, tan ?, [defined by Eq. (2)] at an angular frequency of 0.1 rad/s at 190° C. from about 14 to about 70.

Abstract: The present invention relates generally to a bowl assembly and in particular, a mixing bowl assembly.

Abstract: Systems and methods of documenting an event are disclosed. Events may include, but are not limited to, an accident, a crime, transportation of cargo, a medical procedure, a legal proceeding, an economic transaction, and/or a construction project. An event may be documented by data including, but not limited to, optical and/or audio records of the event. The event documentary may be accessible only via a database storage service bureau which has been certified to maintain accurate and authentic data by a party acceptable to a judicial system. The documentary may be admissible in a court of law. The documentary may be useful for determining the cause of an event and/or preventing a future event.

Abstract: A new, ultrasensitive, capture EIA based assay--ml-HAV EIA suitable for the detection of vaccine or disease-inducted Hepatitis A virus specific antibodies of IgG class in saliva.