Invoca, Inc.

Victor Jara Borda

Colin Denison Kelley

Jason S Spievak

Michael Kingsley McCourt, Jr

Sean Storlie

Leland W Kirchhoff

Michael McCourt

Nicholas James Burwell

David S Trandal

Anoop Praturu

Michael Lawrence

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

In one embodiment, the disclosed technology involves: 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 that includes pre-seeded topics and non-pre-seeded topics; programmatically pre-seeding the topic model with a set of keyword groups; programmatically training the topic model using unlabeled training data; conjoining a classifier to the topic model to create a classifier model; programmatically training the classifier model using labeled training data; receiving target call transcript data; programmatically determining at least one of one or more topics of the target call or one or more classifications of the target call; and digitally storing the target call transcript data with additional data indicating the determined topics and/or classifications of the target call.

Pitman-yor process topic modeling pre-seeded by keyword groupings

Pitman-Yor process topic modeling pre-seeded by keyword groupings

Systems and methods for improving call topic models are described herein. In an embodiment a server computer receives call transcript data comprising an electronic digital representation of a verbal transcription of a call between a first person of a first person type and a second person of a second person type. The server computer splits the call transcript data into first person type data comprising words spoken by the first person in the call and second person type data comprising words spoken by the second person type in the call. The server computer uses a stored topic model to determine a topic of the call, the topic model simultaneously modeling the first person type data as a function of a first probability distribution of words used by the first person type for one or more topics and the second person type data as a function of a second probability distribution of words used by the second person type for the one or more topics, both the first probability distribution of words and the second probability distribution of words being modeled as a function of a third probability distribution of words for the one or more topics. The server computer then stores a data record identifying the topic of the call and/or stores data identifying the topic of the call with the call transcripts.

Signal discovery using artificial intelligence models

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.

Desired signal spotting in noisy, flawed environments

Methods and systems for reducing the impact of fraudulent or spam calls are disclosed. One or more calls to a first phone address are received, the first phone address not currently associated with a communication for a customer prospect. The calling party phone addresses associated with the calls received prior to the association of the first phone address to the first communication are stored. Then the first phone address is associated to the first communication. A first call to the first phone address is received. Prior to answering the first call and at least partly in response to a determination that a calling party phone address of the first call matches one of the previously stored calling party phone address, the first call is identified as a spam or otherwise undesirable call candidate and may be rejected.

Processing unwanted calls

Systems and methods of processing inbound calls

An online and offline communication processing and tracking system using data processing and data/voice networks is described. A phone address from a phone address number pool is automatically assigned to a first customer prospect communication. A call quality rating is determined and recorded. A call from a caller directed to the phone address is received at a call bridging system coupled to at least one network, wherein the call is associated with call signaling information. Based at least in part on the call quality associated with the caller call, the caller call is routed to entity placing a bid on the caller call. An outbound call is generated from the call bridging system and the inbound and the outbound calls are bridged.

Growing community of inventors

Santa Brabara, CA, United States of America

Victor Jara Borda