Invoca, Inc.

Michael Kingsley McCourt, Jr

Victor Jara Borda

Leland W Kirchhoff

Colin Denison Kelley

Sean Storlie

Nicholas James Burwell

Kian Ghodoussi

A computer-implemented method executed using a computing device comprises digitally generating and storing a machine learning statistical topic model in computer memory, the topic model being programmed to model call transcript data representing words spoken on a call as a function of one or more topics of a set of topics, the set of topics being modeled to comprise a set of pre-seeded topics and a set of non-pre-seeded topics, and the one or more topics being modeled as a function of a probability distribution of topics; programmatically pre-seeding the topic model with a set of keyword groups, each keyword group associating a respective set of keywords with a topic of the set of pre-seeded topics; programmatically training the topic model using unlabeled training data; conjoining a classifier to the topic model to create a classifier model, the classifier defining a joint probability distribution over topic vectors and one or more observed labels; programmatically training the classifier model using labeled training data; receiving target call transcript data comprising an electronic digital representation of a verbal transcription of a target call; programmatically determining, using the classifier model, at least one of one or more topics of the target call or one or more classifications of the target call; digitally storing the target call transcript data with additional data indicating the determined one or more topics of the target call and/or the determined one or more classifications of the target call; accessing, in computer storage, a first digitally stored electronic document comprising a first text; receiving computer input specifying a search query comprising one or more search terms; processing the search query using the classifier model to output a query topic vector representing a thematic content of the search query; processing the first text using the classifier model to output and store in the computer memory a first plurality of topic vectors each representing a topic in the text; using the query topic vector and the first plurality of topic vectors, calculating a plurality of similarity values, each of the similarity values representing a similarity of the query topic vector to a particular topic vector among the first plurality of topic vectors; outputting a visual display that specifies one or more topic vectors among the first plurality of topic vectors having one or more corresponding similarity values that are greater than a specified threshold similarity value.

Topic-based semantic search of electronic documents based on machine learning models from Bayesian Belief networks

There are disclosed devices, system and methods for determining a performance score for a trained binary signal classifier (classifier) by determining test output predictions for test input data entries that are input into the classifier. The test outputs are then compared to known outputs of each of the input data entries to determine whether each test output is a true negative (TN), true positive (TP), false negative (FN) or false positive (FP). A number of correct test outputs (R) is defined as a number of the true negative outputs (TN) plus a number of the true positive outputs (TP). A number of incorrect test outputs (W) is defined as a number of the false negative outputs (FN) plus a number of the false positive outputs (FP). The performance score is then calculated using a mathematical expression that decreases as W increases, decreases as TN decreases and decreases as TP decreases.

Performance score determiner for binary signal classifiers

There are disclosed devices, system and methods for a machine learning binary classifier automatically tolerating training data that is incorrect by determining a correct and an incorrect likelihood ratio that each training data entry has a correctly and an incorrectly labeled output. The correct and an incorrect likelihood ratio are combined with a correct and an incorrect priori odds ratio that the set of training data entries have correctly and incorrect labeled output labels. These two combinations are a correct probability and an incorrect probability that each entry of the set of entries has a correctly and an incorrect labeled output. A logistic regression model if fit to a combination of the correct probability and the incorrect probability for each training data entry to complete the training.

Binary signal classifiers that tolerate incorrect training data

There are disclosed devices, system and methods for desired signal spotting in noisy, flawed environments by identifying a signal to be spotted, identifying a target confidence level, and then passing a pool of cabined arrays through a comparator to detect the identified signal, wherein the cabined arrays are derived from respective distinct environments. The arrays may include plural converted samples, each converted sample include a product of a conversion of a respective original sample, the conversion including filtering noise and transforming the original sample from a first form to a second form. Detecting may include measuring a confidence of the presence of the identified signal in each of plural converted samples using correlation of the identified signal to bodies of known matching samples. If the confidence for a given converted sample satisfies the target confidence level, the given sample is flagged.

Growing community of inventors

Santa Barbara, CA, United States of America

Michael Kingsley McCourt, Jr