SYSTEMS AND METHODS FOR ATTRIBUTE CHARACTERIZATION OF USABILITY TESTING PARTICIPANTS
Systems and methods for attribute determination in a usability study are provided. The system includes the ability to collect screener questions and response pairs and determine the type of question. The question and response pairs may be processed for topic and entity extractions using machine learning (ML) models. From the collected topics and entities, a dictionary of attributes may be generated and eventually expanded/added to as new information regarding the participant becomes available. This attribute dictionary may take the form of a vector dictionary, in some particular embodiments. In some cases, the type of question being posed may dictate how the response is processed. These include a Boolean style question, a quantitative question, a single response question and a multi-response type question.
1 . A method for identifying attributes in a plurality of usability study participants comprising:
receiving question and response pairs for each participant;
identifying the question type;
identifying a topic of the question using at least one topic machine learning (ML) model;
identifying an entity of the question using at least one name entity recognition (NER) ML model;
processing the responses based upon the question type;
decoding at least one attribute from the processed responses; and
storing the at least one attribute.
2 . The method of claim 1 , wherein the processing the responses includes a Boolean response processing, a quantitative response processing, a single response processing, and a multi-response processing.
3 . The method of claim 2 , wherein the Boolean response processing includes collecting a binary state for the question topic.
4 . The method of claim 2 , wherein the quantitative response processing includes performing an entity extraction on the responses.
5 . The method of claim 2 , wherein the single response processing includes performing a topic extraction and an entity extraction on the responses.
6 . The method of claim 2 , wherein the multi-response processing includes performing at least two topic extractions and an entity extraction for each topic on the responses.
7 . The method of claim 4 , wherein the entity extraction on the response is performed using a response NER ML model selected from a plurality of NER models based upon accuracy of the response NER ML model based on the topic of the question.
8 . The method of claim 1 , further comprising fielding the participants in a usability study.
9 . The method of claim 8 , wherein the fielding the participants includes:
detecting fraudulent participants based upon the vector for each participant;
screening the participants based upon the vector for each participant;
predicting a conversion rate for the participants based upon the vector for each participant;
selecting a provider based upon the conversion rate of the participants in the provider; and
onboarding participants from the provider to the usability study.
10 . The method of claim 9 , further comprising generating a question recommendation for the usability study based upon the vector for each participant.
11 . A system for identifying attributes in a plurality of usability study participants comprising:
a system server configured to receive question and response pairs for each participant, identify the question type, identify a topic of the question using at least one topic machine learning (ML) model, identify an entity of the question using at least one name entity recognition (NER) ML model, process the responses based upon the question type, decode at least one attribute from the processed responses; and
a database configured to store the at least one attribute as a vector dictionary.
12 . The system of claim 11 , wherein the processing the responses includes a Boolean response processing, a quantitative response processing, a single response processing, and a multi-response processing.
13 . The method of claim 12 , wherein the Boolean response processing includes collecting a binary state for the question topic.
14 . The system of claim 12 , wherein the quantitative response processing includes performing an entity extraction on the responses.
15 . The system of claim 12 , wherein the single response processing includes performing a topic extraction and an entity extraction on the responses.
16 . The system of claim 12 , wherein the multi-response processing includes performing at least two topic extractions and an entity extraction for each topic on the responses.
17 . The system of claim 14 , wherein the entity extraction on the response is performed using a response NER ML model selected from a plurality of NER models based upon accuracy of the response NER ML model based on the topic of the question.
18 . The system of claim 11 , further comprising fielding the participants in a usability study.
19 . The system of claim 18 , wherein the fielding the participants includes:
detecting fraudulent participants based upon the vector for each participant;
screening the participants based upon the vector for each participant;
predicting a conversion rate for the participants based upon the vector for each participant;
selecting a provider based upon the conversion rate of the participants in the provider; and
onboarding participants from the provider to the usability study.
20 . The system of claim 19 , further comprising generating a question recommendation for the usability study based upon the vector for each participant.
21 . A method of predicting fulfillment criteria for a usability study comprising:
performing topic and entity extractions on a question/response pair using a machine learning (ML) model;
decoding the extracted topic and entity to generate an attribute for a plurality of study participants;
estimating the conversion rate of a subset of the study participants based upon the rarity of an attribute and the number of the plurality of study participants that are known to have said attribute;
estimate a time to field based upon the estimated conversion rate and a number of extended study offers;
querying a historical study database to compare the usability study to previous usability studies to estimate duration of the study; and
estimate a time to completion for the study based upon the estimated time to field and the estimated duration.