FMR‘s patent involves a method for predicting emotion profiles using Voice of Customer (VoC) data. By clustering user profiles based on metadata and analyzing emotions, deviations in new customer interactions can trigger specific actions. This innovative approach enhances customer experience and engagement. GlobalData’s report on FMR gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on FMR, AI for workflow management was a key innovation area identified from patents. FMR's grant share as of February 2024 was 59%. Grant share is based on the ratio of number of grants to total number of patents.
Emotion profile prediction using voice of customer data
A recently granted patent (Publication Number: US11922444B1) outlines a method for emotion profile prediction using Voice of Customer (VoC) data. The method involves obtaining user profiles with metadata, forming clusters based on related user metadata, generating emotion profiles for each cluster using VoC data and a pretrained language model, receiving new VoC data and user metadata for a new user, identifying a cluster based on user metadata correlation, determining deviations in emotion attributes, and taking predetermined actions based on these deviations. The user metadata includes attributes like age, gender, location, and topics of interest, and the VoC data comprises transcripts, audio recordings, chat sessions, and emails within a predefined time range.
Furthermore, the patent describes a system and a non-transitory computer-readable medium for implementing the method. The system includes a processor coupled to memory, which is configured to perform the steps outlined in the method. It generates clusters based on related user metadata, emotion profiles using VoC data and a pretrained language model, identifies clusters for new users, determines deviations in emotion attributes, and takes actions accordingly. The non-transitory computer-readable medium contains software that enables computing devices to execute the method steps, including obtaining user profiles, forming clusters, generating emotion profiles, receiving new VoC data, identifying clusters, determining deviations, and taking actions based on these deviations. The user attributes in the metadata include age, gender, location, and topics of interest, providing a comprehensive approach to emotion profile prediction using VoC data.
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