US 2021/0081972 A1Published application

System and method for proactive client relationship analysis

AssigneeRimini Street, Inc.

InventorsPraveen Sahni, Brian Slepko, Craig Mackereth

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Abstract

A service provider system receives case data of a client from a client service system. Vector data is collected from the case data through integration and aggregation. Signals of anomalies or sentiments are detected through machine learning from the integrated and aggregated vector data. The signals are validated, consolidated, and associated with case, contact, and client object types. A user interface presents the validated and consolidated signals to users who proactively take action based on the signals. The user interface includes dashboards, notifications, and indicators.

Claims44

Dependent claims

A system for proactively detecting client satisfaction comprising:

at least one processor;
and
at least one memory coupled to the at least one processor, the at least one memory having stored therein instructions which, when executed by any set of the one or more processors, perform a process including:
collecting historical case vector data from one or more client service systems;
training, using the historical case vector data, one or more machine learning anomaly detection models to detect anomalies in case data indicating potential client dissatisfaction;
obtaining current case vector data representing current client cases associated with one or more clients of a service provider;
providing the current case vector data to the trained one or more machine learning anomaly detection models;
identifying, using the one or more machine learning anomaly detection models, one or more anomalies in the current case vector data for one or more specific current client cases;
generating a signal report, the signal report including a listing of each of the one or more specific current client cases having one or more identified anomalies and the specific one or more anomalies associated with the listed more specific current client cases having one or more identified anomalies;
and
providing the signal report to an agent for the service provider.

8 more elements

A system for proactively detecting client satisfaction comprising:

at least one processor;
and
at least one memory coupled to the at least one processor, the at least one memory having stored therein instructions which, when executed by any set of the one or more processors, perform a process including:
obtaining, using one or more computing systems, current case data representing cases associated with clients of a service provider;
providing the current case data to one or more machine learning-based language processing models;
identifying, using the one or more machine learning-based language processing models, one or more client sentiments in the current case data for one or more specific current client cases;
generating, using one or more computing systems, a signal report, the signal report including a listing of each of the one or more specific cases having one or more identified client sentiments and the specific one or more client sentiments associated with the listed more specific current client cases having one or more identified one or more client sentiments;
and
providing, using one or more computing systems, the signal report to an agent for the service provider.

6 more elements

A system for proactively detecting client satisfaction comprising:

at least one processor;
and
at least one memory coupled to the at least one processor, the at least one memory having stored therein instructions which, when executed by the at least one processor, perform a process including:
collecting, using one or more computing systems, historical case vector data from one or more client service systems;
training, using the historical case vector data, one or more machine learning anomaly detection models to detect anomalies in case data indicating potential client dissatisfaction;
obtaining, using one or more computing systems, current case vector data representing current client cases associated with one or more clients of a service provider;
providing the current case vector data to the trained one or more machine learning anomaly detection models;
identifying, using the one or more machine learning anomaly detection models, one or more anomalies in the current case data for one or more specific current client cases;
providing the current case data to one or more machine learning-based language processing models;
identifying, using the one or more machine learning-based language processing models, one or more client sentiments in the current case data for one or more specific current client cases;
generating, using one or more computing systems, a signal report, the signal report including:
a listing of each of the one or more specific current client cases having one or more identified anomalies and the specific one or more anomalies associated with the listed more specific current client cases having one or more identified anomalies;
a listing of each of the one or more specific cases having one or more identified client sentiments and the specific one or more client sentiments associated with the listed more specific current client cases having one or more identified one or more client sentiments;
and
providing, using one or more computing systems, the signal report to an agent for the service provider.

12 more elements

Dependencies are inferred from the claim text.