US 11,995,670 B2
User experience management system
Emily T. Schlick, Ballwin, MO (US); Tyler Corbett, Creve Coeur, MO (US); Clinton R. Laytham, Bel-Nor, MO (US); Alexander Chea, Boca Raton, FL (US); Alison Bodker, Cudahy, WI (US); Kalynn Clinton, St. Louis, MO (US); and Kathryn Golden, Austin, TX (US)
Assigned to Express Scripts Strategic Development, Inc., St. Louis, MO (US)
Filed by Express Scripts Strategic Development, Inc., St. Louis, MO (US)
Filed on Jun. 2, 2021, as Appl. No. 17/337,071.
Claims priority of provisional application 63/033,335, filed on Jun. 2, 2020.
Prior Publication US 2021/0374778 A1, Dec. 2, 2021
Int. Cl. G06Q 30/0203 (2023.01); G06Q 30/0201 (2023.01)
CPC G06Q 30/0203 (2013.01) [G06Q 30/0201 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
receiving training data comprising a bag of words representation of text, a mapping of words to an identification of each word, and an estimated quantity of topics, the estimated quantity of topics being based on a predetermined topic probability and predetermined word probability;
training a machine learning model based on the training data to infer a topic distribution of a corpus comprising survey information and to estimate problems, the training of the machine learning model comprising:
comparing one or more trends observed between responses to surveys and contents of tickets and calls;
identifying problems based on the one or more trends; and
building the machine learning model based on the identified problems;
generating, by the machine learning model using a processor, a set of problems at a predetermined frequency based on free-form comments data in a databased of a computer, wherein data in the database comprises results of surveys, data on tickets and calls, and data on system monitoring;
generating, for display, an output of the machine learning model in response to generating the set of problems, the output comprising a plurality of geometric shapes each representing a different topic in a plurality of topics included in the output of the machine learning model, a first of the plurality of geometric shapes having a first size corresponding to a first quantity of words associated with a first topic, a second of the plurality of geometric shapes having a second size corresponding to a second quantity of words associated with a second topic;
selecting a subset of problems from the set of problems based on a priority matrix and criteria;
generating a message comprising a problem definition and a prioritization based on the subset of problems;
transmitting the message to a product management system;
analyzing a resolution generated by the product management system; and
monitoring the resolution, wherein monitoring the resolution includes performing system monitoring and storing data associated with the resolution in the database.