Abstract: A device may receive information associated with an entity. The information may include a first resource and a second resource. The first resource may be associated with a first file type, and the second resource may be associated with a second file type that is different than the first file type. The first resource may be associated with a first source, and the second resource may be associated with a second source that is different than the first source. The device may extract a plurality of attributes associated with the entity based on the information. The device may implement a natural language processing technique to extract the plurality of attributes. The device may associate the plurality of attributes with a plurality of elements based on extracting the plurality of attributes. The device may provide information that identifies the plurality of elements and the plurality of attributes to permit and/or cause an action to be performed.

Abstract: Techniques are described for routing service requests in a computer-implemented service environment. A received service request may be initially analyzed to determine a priority of the request. In some implementations, one or more actions may be automatically performed to provide an initial response to the requester. The text of the request may be analyzed to automatically determine a category of the request. In some implementations, a classification engine may determine the category of the request through use of a classification model that has been trained using one or more machine learning (ML) techniques and/or that employs Natural Language Processing (NLP). Based on the category, the request may be routed to agent(s) for handling. Routing may include generating a ticket that includes the request, the category, the priority, and/or other information, and the ticket may be provided to the appropriate agent(s) through a ticketing service.

Abstract: A device may receive information associated with an entity. The information may include a first resource and a second resource. The first resource may be associated with a first file type, and the second resource may be associated with a second file type that is different than the first file type. The first resource may be associated with a first source, and the second resource may be associated with a second source that is different than the first source. The device may extract a plurality of attributes associated with the entity based on the information. The device may implement a natural language processing technique to extract the plurality of attributes. The device may associate the plurality of attributes with a plurality of elements based on extracting the plurality of attributes. The device may provide information that identifies the plurality of elements and the plurality of attributes to permit and/or cause an action to be performed.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for predictive modeling for unintended outcomes are disclosed. In one aspect, a method includes the actions of accessing an order history that, for each of one or more past orders, indicates (i) one or more order details associated with the order, and (ii) a fulfillment outcome associated with the order. The actions further include selecting one or more particular past orders that are associated with the particular unintended order fulfillment outcome. The actions further include generating a predictive model. The actions further include receiving one or more order details associated with a subsequently received order. The actions further include providing the one or more order details as input to the predictive model. The actions further include, identifying a remedial action. The actions further include providing data indicating the remedial action.

Abstract: Techniques are described for routing service requests in a computer-implemented service environment. A received service request may be initially analyzed to determine a priority of the request. In some implementations, one or more actions may be automatically performed to provide an initial response to the requester. The text of the request may be analyzed to automatically determine a category of the request. In some implementations, a classification engine may determine the category of the request through use of a classification model that has been trained using one or more machine learning (ML) techniques and/or that employs Natural Language Processing (NLP). Based on the category, the request may be routed to agent(s) for handling. Routing may include generating a ticket that includes the request, the category, the priority, and/or other information, and the ticket may be provided to the appropriate agent(s) through a ticketing service.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for predictive modeling for unintended outcomes are disclosed. In one aspect, a method includes the actions of accessing an order history that, for each of one or more past orders, indicates (i) one or more order details associated with the order, and (ii) a fulfillment outcome associated with the order. The actions further include selecting one or more particular past orders that are associated with the particular unintended order fulfillment outcome. The actions further include generating a predictive model. The actions further include receiving one or more order details associated with a subsequently received order. The actions further include providing the one or more order details as input to the predictive model. The actions further include, identifying a remedial action. The actions further include providing data indicating the remedial action.