US 12,739,299 B2Granted

Systems and methods for routing peer-to-peer communications via telecommunications networks based on bifurcated user-specific sentiment analysis

AssigneeCapital One Services, LLC

InventorDeepak Ramamohan

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Abstract

Routing peer-to-peer communications via telecommunications networks based on bifurcated user-specific sentiment analysis may be facilitated. In some embodiments, a system may generate a sentiment value related to each utterance of a set of utterances associated with a user by providing each utterance of the set of utterances to a sentiment machine learning model. The system may bin each utterance into a set of bins based on the sentiment values. The system may determine a sentiment probability of each bin of the set of bins by randomly sampling a subset of utterances corresponding to a respective bin of the set of bins. The system may determine an overall sentiment probability for a transcript based on the determined sentiment probability of each bin. In response to receiving a communication request, the system may route the communication request to an agent based on the overall sentiment probability satisfying a threshold sentiment probability.

Claims20

Dependent claims

A system for routing peer-to-peer communications via telecommunications networks based on bifurcated user-specific sentiment analysis, the system comprising:

one or more processors and non-transitory computer-readable media comprising instructions that, when executed by the one or more processors, perform operations comprising:
receiving a transcript of a dialogue between a user and an agent, the transcript comprising utterances between the user and the agent;
extracting, from the transcript, a set of utterances associated with the user;
providing the set of utterances associated with the user to each of a plurality of sentiment machine learning models configured to output a sentiment value for each utterance of the set of utterances associated with the user;
binning each utterance of the set of utterances associated with the user into one or more bins based on the sentiment values, wherein each bin of the one or more bins corresponds to a non-overlapping numerical range of sentiment values;
determining, for each bin of the one or more bins, a negative-sentiment probability associated with a respective bin based on randomly sampling a subset of the set of utterances in the respective bin;
determining an overall negative-sentiment probability for the transcript based on the negative-sentiment probability associated with each bin of the one or more bins;
linking the overall negative-sentiment probability for the transcript to a user identifier associated with the user;
in response to receiving a communication request from the user comprising the user identifier, determining whether the overall negative-sentiment probability linked to the user identifier satisfies a threshold negative-sentiment probability value;
and
routing the communication request to a second agent in response to the overall negative-sentiment probability satisfying the threshold negative-sentiment probability value.

8 more elements

A method for routing peer-to-peer communications via telecommunications networks based on bifurcated user-specific sentiment analysis, the method comprising:

extracting, from a transcript of a dialogue between a user and a first agent, a set of utterances associated with the user;
generating a sentiment value related to each utterance of the set of utterances associated with the user by providing each utterance of the set of utterances to a sentiment machine learning model;
binning each utterance of the set of utterances associated with the user into a set of bins based on the sentiment values, wherein each bin of the set of bins corresponds to a non-overlapping numerical range of sentiment values;
determining, for each bin of the set of bins, a sentiment probability corresponding to a respective bin based on randomly sampling a subset of utterances in the respective bin;
determining an overall sentiment probability for the transcript based on the sentiment probability corresponding to each bin of the set of bins;
linking the overall sentiment probability for the transcript to a user identifier associated with the user;
and
in response to receiving a communication request from the user comprising the user identifier, routing the communication request to a second agent based on the overall sentiment probability linked to the user identifier satisfying a threshold sentiment probability.

5 more elements

One or more non-transitory, computer-readable media comprising instructions that, when executed by one or more processors, perform operations comprising:

extracting, from a transcript of a dialogue between a user and a first agent, a set of utterances associated with the user;
generating a sentiment value related to each utterance of the set of utterances associated with the user by providing each utterance of the set of utterances to a sentiment machine learning model;
binning each utterance of the set of utterances associated with the user into a set of bins based on the sentiment values, wherein each bin of the set of bins corresponds to a non-overlapping range of sentiment values;
determining, for each bin of the set of bins, a sentiment probability corresponding to a respective bin based on randomly sampling a subset of utterances in the respective bin;
determining an overall sentiment probability for the transcript based on the sentiment probability corresponding to each bin of the set of bins;
linking the overall sentiment probability for the transcript to a user identifier associated with the user;
and
in response to receiving a communication request from the user comprising the user identifier, routing the communication request to a second agent based on the overall sentiment probability linked to the user identifier satisfying a threshold sentiment probability.

5 more elements

Dependencies are inferred from the claim text.