US 11,146,682 B1 · Learning based metric determination for service sessions

Abstract

Techniques are described for generating metric(s) that predict survey score(s) for a service session. Model(s) may be trained, through supervised or unsupervised machine learning, using training data from previous service sessions between service representative(s) and individual(s). Training data may include, for previous service session(s), a session record (e.g., audio record) of the session and a set of survey scores provided by the serviced individual to rate the session on one or more criteria (e.g., survey questions). The model(s) may be trained to output, based on an input session record, metric(s) that each correspond to a survey score that would have been provided by the individual had they completed the survey. The model may be a concatenated model that is a combination of a language model output from a language classifier recurrent neural network, and an acoustic model output from an acoustic feature layer convolutional neural network.

Claims20

Dependent claims

A computer-implemented method, comprising:

assigning a number of calls received by a customer service environment to a demand block of a first blockchain, each call in the number of calls comprising an electronic communication from a respective customer requesting service from the customer service environment;
assigning management of the number of calls in the demand block to a particular demand node from among a plurality of demand nodes based on a first result of a first proof of work algorithm executed by the particular demand node;
assigning a number of customer service representatives (CSRs) to a supply block of a second blockchain, each CSR in the number of CSRs being logged in to the customer service environment;
assigning management of the number of CSRs in the supply block to a particular supply node from among a plurality of supply nodes based on a second result of a second proof of work algorithm executed by the particular supply node;
and
matching the supply block with the demand block to initiate service sessions between individual calls from the supply block and individual CSRs from the demand block.

3 more elements

A system comprising:

at least one processor;
and at least one data store coupled to the at least one processor having instructions stored thereon which, when executed by the at least one processor, causes the at least one processor to perform operations comprising:
assigning a number of calls received by a customer service environment to a demand block of a first blockchain, each call in the number of calls comprising an electronic communication from a respective customer requesting service from the customer service environment;
assigning management of the number of calls in the demand block to a particular demand node from among a plurality of demand nodes based on a first result of a first proof of work algorithm executed by the particular demand node;
assigning a number of customer service representatives (CSRs) to a supply block of a second blockchain, each CSR in the number of CSRs being logged in to the customer service environment;
assigning management of the number of CSRs in the supply block to a particular supply node from among a plurality of supply nodes based on a second result of a second proof of work algorithm executed by the particular supply node;
and
matching the supply block with the demand block to initiate service sessions between individual calls from the supply block and individual CSRs from the demand block.

5 more elements

A non-transitory computer readable storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising:

at least one processor;
and at least one data store coupled to the at least one processor having instructions stored thereon which, when executed by the at least one processor, causes the at least one processor to perform operations comprising:
assigning a number of calls received by a customer service environment to a demand block of a first blockchain, each call in the number of calls comprising an electronic communication from a respective customer requesting service from the customer service environment;
assigning management of the number of calls in the demand block to a particular demand node from among a plurality of demand nodes based on a first result of a first proof of work algorithm executed by the particular demand node;
assigning a number of customer service representatives (CSRs) to a supply block of a second blockchain, each CSR in the number of CSRs being logged in to the customer service environment;
assigning management of the number of CSRs in the supply block to a particular supply node from among a plurality of supply nodes based on a second result of a second proof of work algorithm executed by the particular supply node;
and
matching the supply block with the demand block to initiate service sessions between individual calls from the supply block and individual CSRs from the demand block.

5 more elements

Dependencies are inferred from the claim text.

Learning based metric determination for service sessions

Life of this patent

Learning based metric determination for service sessions

Life of this patent

Abstract

Claims20