Patent ID: 11861540
Assignee: ALLSTATE INSURANCE COMPANY
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 19:
20. One or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor, a communication interface, and memory, cause the computing platform to:
determine, based on one or more of a plurality of audio transcription files, each associated with one of a plurality of individuals, a model for dynamic performance evaluation and training, wherein the model is a machine learning model that is dynamically updated as additional audio transcription files are received;
receive an additional audio transcription file associated with a first individual of the plurality of individuals;
analyze the additional audio transcription file using natural language processing and the machine learning model, wherein the analysis results in one or more proficiency scores for one or more events in the additional audio transcription file, wherein:
the one or more events include at least one of: asking for a sale, making a multiline offering, handling a sales objection, or closing a sale,
the one or more proficiency scores for the one or more events indicate a proportion of occurrences of the one or more events in audio transcription files for the first individual,
the one or more proficiency scores for the one or more events is adjusted when a successful outcome is determined from the analysis of the additional audio transcription file, and
one or more weights are assigned to the one or more proficiency scores for the one or more events, wherein a first weight of the one or more weights is assigned to a first event of the one or more events in a first geographic region and a second weight is assigned to the first event in a second geographic region, the first weight is greater than the second weight;

identify a learning style of the first individual based on the analysis using the natural language processing and the machine learning model;
determine, based on the one or more proficiency scores, performance feedback tailored to the first individual, wherein the performance feedback is based on the learning style of the first individual;
generate, using the performance feedback tailored to the first individual, a feedback interface;
send, to a user device associated with the first individual, the feedback interface;
determining an impact on performance caused by the performance feedback tailored to the first individual; and
dynamically modifying, based on the impact on performance, the machine learning model for dynamic performance evaluation and training.