Patent Issued for Learning based metric determination for service sessions (USPTO 11272057): United Services Automobile Association

2022 MAR 30 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- From Alexandria, Virginia, NewsRx journalists report that a patent by the inventors Jayapalan, Vijay (San Antonio, TX, US), Smith, Eric J. (Helotes, TX, US), TenEyck, Jr., John McChesney (San Antonio, TX, US), Yarbrough, Gregory (San Antonio, TX, US), filed on November 12, 2020, was published online on March 8, 2022.

The patent’s assignee for patent number 11272057 is United Services Automobile Association (San Antonio, Texas, United States).

News editors obtained the following quote from the background information supplied by the inventors: “An organization may use any number of computing systems, communications networks, data storage devices, or other types of systems to provide service to individuals. An organization may also employ service representatives that use the various systems to assist individuals in service sessions that are conducted over the telephone, in a video conference, through text chat sessions, in person, and/or over other communication channels. Organizations may strive to provide an efficient and productive service interaction between service representatives and the individuals being serviced, while maintaining an appropriate quality level for the service provided by service representatives.”

As a supplement to the background information on this patent, NewsRx correspondents also obtained the inventors’ summary information for this patent: “Implementations of the present disclosure are generally directed to determining one or more metrics that describe a service session. More specifically, implementations are directed to using machine learning techniques, such as deep learning that includes classification, clustering, and/or other techniques, to determine metric(s) for a service session and a customer experience.

“In general, innovative aspects of the subject matter described in this specification can be embodied in methods that includes actions of receiving, by at least one processor, a session record of communications between a service representative (SR) and an individual during a service session. The methods include the actions of providing, by at least one processor, the session record as input to at least one computer-processable model that determines, based on the session record, at least one metric for the service session, the at least one model having been trained, using machine learning and based at least partly on survey data for previous service sessions, to provide the at least one metric associated with the individual’s experience. The methods include the actions of associating, by at least one processor, the metric of the individual’s experience with the individual. The methods also include the actions of communicating, by at least one processor, the at least one metric for presentation through a user interface of a computing device.

“Implementations can optionally include one or more of the following features, alone or in combination. The service session may be an audio call between the CSR and the individual. The session record may include an audio record of at least a portion of the audio call. The methods may include developing, by at least one processor, the at least one model based on training data that describes, for each of a plurality of previous service sessions: a previous session record of communications during a respective previous service session, and at least one survey score provided to rate the respective previous service session. The respective previous service session may be an audio call; and the previous session record may include an audio record of at least a portion of the audio call. The methods may include the actions of updating the customer’s requirements based on metric and selecting another service representative to interact with the individual on a subsequent service session based in part on the updated customer requirements. The methods may include the actions of receiving a request for a service session from a first individual, identifying a second individual similar to the first individual, and connecting the first individual with another service representative, where the other service representative is selected, at least in part, based on the metrics associated with the second individual.

“Other implementations of any of the above aspects include corresponding systems, apparatus, and computer programs that are configured to perform the actions of the methods, encoded on computer storage devices. The present disclosure also provides a computer-readable storage medium coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations in accordance with implementations of the methods provided herein. The present disclosure further provides a system for implementing the methods provided herein. The system includes one or more processors, and a computer-readable storage medium coupled to the one or more processors having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations in accordance with implementations of the methods provided herein.

“Implementations of the present disclosure provide one or more of the following advantages. Through use of machine learning developed models to determine metrics that are predicted survey results, implementations provide a technique to determine survey results in a service environment even in instances when the individual being serviced does not complete a survey. Moreover, the model(s) enable an objective determination of survey results across a wider range of individuals than would ordinarily complete survey results, thus providing more accurate, objective, and comprehensive view of the service provided in a service environment. By providing more accurate survey results through use of predictive modeling, implementations may avoid repeatedly requesting that individual(s) complete surveys following a service session, thus avoiding the expenditure of processing power, network bandwidth, storage space, active memory, and/or other computing resources that may be expended in traditional service environments. In instances where the predictive model(s) are sufficiently developed to enable session metric(s) to be determined without surveying individuals, implementations also avoid the expenditure of computing resources that would otherwise be consumed to survey individuals following a service session.”

The claims supplied by the inventors are:

“1. A computer-implemented method performed by at least one processor, the method comprising: training a neural network to process a first record of communications between a first service representative (SR) and a first individual during a first service session to generate one or more predicted survey scores that rate the first service session on one or more criteria, wherein the training is conducted using training data comprising, for each of one or more previous service sessions, a respective previous record of the previous service session and a respective set of one or more actual survey scores provided by a serviced individual to rate the previous service session on the one or more criteria; receiving, by at least one processor, a second record of communications between a second service representative (SR) and a second individual during a second service session; processing, using the trained neural network, the second record to determine one or more new predicted survey scores that rate the second service session; associating, by at least one processor, the one or more new predicted survey scores with the second individual; and communicating, by at least one processor, the one or more new predicted survey scores for presentation through a user interface of a computing device.

“2. The computer-implemented method of claim 1, wherein: the second service session is an audio call between the SR and the individual; and the second record includes an audio record of at least a portion of the audio call.

“3. The computer-implemented method of claim 1, wherein: the respective previous service session is an audio call; and the respective previous record includes an audio record of at least a portion of the audio call.

“4. The computer-implemented method of claim 1, further comprising: updating requirements of the second individual’s based on a metric; and selecting another service representative to interact with the second individual on a subsequent service session based in part on the updated requirements.

“5. The computer-implemented method of claim 1, further comprising: receiving a request for a service session from a third individual; identifying a fourth individual similar to the third individual; and connecting the third individual with another service representative, where the other service representative is selected, at least in part, based on metrics associated with the fourth individual.

“6. A system comprising: at least one processor; and a 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: training a neural network to process a first record of communications between a first service representative (SR) and a first individual during a first service session to generate one or more predicted survey scores that rate the first service session on one or more criteria, wherein the training is conducted using training data comprising, for each of one or more previous service sessions, a respective previous record of the previous service session and a respective set of one or more actual survey scores provided by a serviced individual to rate the previous service session on the one or more criteria; receiving a second record of communications between a second service representative (SR) and a second individual during a second service session; processing, using the trained neural network, the second record to determine one or more new predicted survey scores that rate the second service session; associating the one or more new predicted survey scores with the second individual; and communicating the one or more new predicted survey scores for presentation through a user interface of a computing device.

“7. The system of claim 6, wherein: the second service session is an audio call between the SR and the individual; and the second record includes an audio record of at least a portion of the audio call.

“8. The system of claim 6, wherein: the respective previous service session is an audio call; and the respective previous record includes an audio record of at least a portion of the audio call.

“9. The system of claim 6, further comprising: updating requirements of the second individual’s based on a metric; and selecting another service representative to interact with the second individual on a subsequent service session based in part on the updated requirements.

“10. The system of claim 6, further comprising: receiving a request for a service session from a third individual; identifying a fourth individual similar to the third individual; and connecting the third individual with another service representative, where the other service representative is selected, at least in part, based on metrics associated with the fourth individual.

“11. 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: training a neural network to process a first record of communications between a first service representative (SR) and a first individual during a first service session to generate one or more predicted survey scores that rate the first service session on one or more criteria, wherein the training is conducted using training data comprising, for each of one or more previous service sessions, a respective previous record of the previous service session and a respective set of one or more actual survey scores provided by a serviced individual to rate the previous service session on the one or more criteria; receiving a second record of communications between a second service representative (SR) and a second individual during a second service session; processing, using the trained neural network, the second record to determine one or more new predicted survey scores that rate the second service session; associating the one or more new predicted survey scores with the second individual; and communicating the one or more new predicted survey scores for presentation through a user interface of a computing device.

“12. The medium of claim 11, wherein: the second service session is an audio call between the SR and the individual; and the second record includes an audio record of at least a portion of the audio call.

“13. The medium of claim 11, wherein: the respective previous service session is an audio call; and the respective previous record includes an audio record of at least a portion of the audio call.

“14. The medium of claim 11, further comprising: updating requirements of the second individual’s based on a metric; and selecting another service representative to interact with the second individual on a subsequent service session based in part on the updated requirements.

“15. The medium of claim 11, further comprising: receiving a request for a service session from a third individual; identifying a fourth individual similar to the third individual; and connecting the third individual with another service representative, where the other service representative is selected, at least in part, based on metrics associated with the fourth individual.”

For additional information on this patent, see: Jayapalan, Vijay. Learning based metric determination for service sessions. U.S. Patent Number 11272057, filed November 12, 2020, and published online on March 8, 2022. Patent URL: http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PALL&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.htm&r=1&f=G&l=50&s1=11272057.PN.&OS=PN/11272057RS=PN/11272057

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