Embodiments are directed to a method for determining an interview script in a claims submission. The method may comprising receiving data relating to a claim being submitted, which may include claims submission data input by a user, information relating to the user, and one or more features. Data associated with the one or more features may be determined from an artificial intelligence model. A first score based on the data associated with the one or more features and data associated with the information relating to the user may be determined and used to determine an interview script. In one embodiment, questions in the interview script may continue to be provided to the interviewer computer if a continually updated score remains above a predetermined threshold. In another embodiment, the user may be routed to a live interview with a human representative if a continually updated score drops below a predetermined threshold.

Embodiments are directed to a method for determining an interview script in a claims submission. The method may comprising receiving data relating to a claim being submitted, which may include claims submission data input by a user, information relating to the user, and one or more features. Data associated with the one or more features may be determined from an artificial intelligence model. A first score based on the data associated with the one or more features and data associated with the information relating to the user may be determined and used to determine an interview script. In one embodiment, questions in the interview script may continue to be provided to the interviewer computer if a continually updated score remains above a predetermined threshold. In another embodiment, the user may be routed to a live interview with a human representative if a continually updated score drops below a predetermined threshold.

A data server may support segment identification based on a selected user profile. For example, a user may select a user profile as the basis for identifying a segment of additional user profiles. The server may identify attributes associated with the selected user identifier and generate an expression based on the identified subset. The expression may include a normalization function corresponding to at least one attribute. The normalization function may identify correlated attribute values for an attribute associated with the selected user profile. The data server may query a data storage system to identify the additional user profiles based on the expression. The data server may also support user defined Boolean expressions such that the expression is used to identify user identifiers associated with a first attribute and a second attribute.

A method for coordinating actions between an audio channel and a synchronized non-audio channel includes receiving an indication of a start of a session associated with a user and having an audio channel that is synchronized with a non-audio channel. Thereafter, repeated determinations are made as to whether a prompt on the non-audio channel has been received from the user. In response to each determination that the prompt on the non-audio channel has not been received from the user, a signal is sent to cause an inaudible output on the audio channel to the user. In response to a determination that the prompt on the non-audio channel has been received from the user, an audible output is selected based on an activity by the user on the non-audio channel, and a signal is sent to cause the audible output to be output on the audio channel.

A method implements a customer recognition system. A request with an identifier of an unidentified user is received. Sparse data is generated from string information corresponding to the identifier. Preexisting identifiers are filtered to generate a list of candidate identifiers using the sparse data. The plurality of preexisting identifiers correspond to a plurality of preexisting users. A core identifier is selected by determining a match between the identifier and a preexisting identifier from the preexisting identifiers using distance information generated using the list of candidate identifiers. The core identifier is matched to the identifier using the match to identify the unidentified user as a preexisting user from the plurality of preexisting users.

Disclosed are methods, apparatus, systems, and computer readable storage media for interacting with components across different domains in a single user interface in an online social network. The user interface includes a first component and a second component, where the first component exposes content from a first database system at a first network domain and the second component exposes content from a second database system at a second network domain. A first interaction with the first component is received at a computing device, followed by a reference being provided in the second component, where the reference includes information related to the first interaction. A second interaction with the second component regarding the reference can be received at the computing device. Interactions between the components hosted on different database systems can occur through an application programming interface (API).

Systems and methods of design, delivery, and analysis of customer feedback surveys include receiving interaction content. Interaction content is analyzed to identify at least one issue arising in the interaction content. A survey of a plurality of questions is automatedly created based upon the identified at least one issue. A delivery channel for the survey is determined. The survey is delivered through the determined delivery channel. A survey response with survey results data is received.

A system and method are presented for customer journey event representation learning and outcome prediction using neural sequence models. A plurality of events are input into a module where each event has a schema comprising characteristics of the events and their modalities (web clicks, calls, emails, chats, etc.). The events of different modalities can be captured using different schemas and therefore embodiments described herein are schema-agnostic. Each event is represented as a vector of some number of numbers by the module with a plurality of vectors being generated in total for each customer visit. The vectors are then used in sequence learning to predict real-time next best actions or outcome probabilities in a customer journey using machine learning algorithms such as recurrent neural networks.

A request to contact a service provider may be received from a client machine. The request may be associated with an identity claim and including a service identifier. The identity claim may be validated via a distributed identity service that includes a plurality of identity nodes in communication via a network. Validating the identity claim may include determining a designated network identifier associated with a distributed identity account shared among the plurality of identity nodes. A service query that includes the service identifier and the designated network identifier may be sent to a plurality of customer relations management services. A communication session may be established between a service provider remote computing system and the client machine. The service provider may store customer relations management information at a designated one of the plurality of customer relations management services.

A request to contact a service provider may be received from a client machine. The request may be associated with an identity claim and including a service identifier. The identity claim may be validated via a distributed identity service that includes a plurality of identity nodes in communication via a network. Validating the identity claim may include determining a designated network identifier associated with a distributed identity account shared among the plurality of identity nodes. A service query that includes the service identifier and the designated network identifier may be sent to a plurality of customer relations management services. A communication session may be established between a service provider remote computing system and the client machine. The service provider may store customer relations management information at a designated one of the plurality of customer relations management services.