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1. 发明申请

US20210120121A1 SIGNAL DISCOVERY USING ARTIFICIAL INTELLIGENCE MODELS 有权
公开(公告)号：US20210120121A1
公开(公告)日：2021-04-22
申请号：US16897568
申请日：2020-06-10
申请人： Invoca, Inc.
发明人： Michael McCourt , Sean Storlie , Victor Borda , Michael Lawrence , Anoop Praturu
IPC分类号： H04M3/22 , G06N7/00 , G06F16/45 , G10L15/197
摘要： 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.

2. 发明授权

US11115520B2 Signal discovery using artificial intelligence models 有权
公开(公告)号：US11115520B2
公开(公告)日：2021-09-07
申请号：US16897568
申请日：2020-06-10
申请人： Invoca, Inc.
发明人： Michael McCourt , Sean Storlie , Victor Borda , Michael Lawrence , Anoop Praturu
IPC分类号： H04M3/22 , G10L15/18 , G10L15/197 , G06F16/45 , G06N7/00
摘要： 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.

3. 发明授权

US11429901B1 Pitman-Yor process topic modeling pre-seeded by keyword groupings 有权
公开(公告)号：US11429901B1
公开(公告)日：2022-08-30
申请号：US17552991
申请日：2021-12-16
申请人： Invoca, Inc.
发明人： Michael McCourt , Victor Borda
IPC分类号： G10L15/04 , G06N20/00 , G06F40/40
摘要： 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.

4. 发明申请

US20210118432A1 GENERATING TRAINING DATASETS FOR A SUPERVISED LEARNING TOPEL MODEL FROM OUTPUTS OF A DISCOVERY TOPIC MODEL 有权
公开(公告)号：US20210118432A1
公开(公告)日：2021-04-22
申请号：US16996653
申请日：2020-08-18
申请人： Invoca, Inc.
发明人： Michael McCourt , Anoop Praturu
IPC分类号： G10L15/18 , G06N20/00 , G06N5/04 , G10L15/22 , G10L15/06 , G10L15/197
摘要： Systems and methods for generating training data for a supervised topic modeling system from outputs of a topic discovery model are described herein. In an embodiment, a system receives a plurality of digitally stored call transcripts and, using a topic model, generates an output which identifies a plurality of topics represented in the plurality of digitally stored call transcripts. Using the output of the topic model, the system generates an input dataset for a supervised learning model by identify a first subset of the plurality of digitally stored call transcripts that include the particular topic, storing a positive value for the first subset, identifying a second subset that do not include the particular topic, and storing a negative value for the second subset. The input training dataset is then used to train a supervised learning model.

5. 发明申请

US20230055948A1 AUTOMATIC CLASSIFICATION OF PHONE CALLS USING REPRESENTATION LEARNING BASED ON THE HIERARCHICAL PITMAN-YOR PROCESS 有权
公开(公告)号：US20230055948A1
公开(公告)日：2023-02-23
申请号：US17397709
申请日：2021-08-09
申请人： Invoca, Inc.
发明人： Michael McCourt
IPC分类号： G10L15/14 , G10L15/06 , G06N20/00
摘要： Embodiments of the disclosed technology include a representation learning model for classification of natural language text. In embodiments, a classification model comprises a feature model and a classifier. The feature model may be hierarchical in nature: data may pass through a series of representations, decreasing in specificity and increasing in generality. Intermediate levels of representation may then be used as automatically learned features to train a statistical classifier. Specifically, the feature model may be based on a hierarchical Pitman-Yor process. In embodiments, once the feature model has been expressed as a Bayesian Belief Network and some aspect of the feature model has been selected for prediction, the feature model may be attached to the classifier. In embodiments, after training, potentially using a mix of labeled and unlabeled data, the classification model can be used to classify documents such as call transcripts based on topics of conversation represented in the transcripts.

6. 发明授权

US11521601B2 Detecting extraneous topic information using artificial intelligence models 有权
公开(公告)号：US11521601B2
公开(公告)日：2022-12-06
申请号：US16996761
申请日：2020-08-18
申请人： Invoca, Inc.
发明人： Michael McCourt , Michael Lawrence
IPC分类号： G06F16/35 , G10L15/18 , G06N20/00 , G10L15/183 , G10L15/22 , G06N5/04 , G06N5/02
摘要： Systems and methods for improving machine learning systems used to model topics on a plurality of calls are described herein. In an embodiment, a server computer receives plurality of digitally stored call transcripts that have been prepared from digitally recorded voice calls. The server computer uses a topic model of an artificial intelligence machine learning system, the topic model modeling words of a call as a function of one or more word distributions for each topic of a plurality of topics, to generate an output of the topic model which identifies the plurality of topics represented in the plurality of call transcripts. The server computer computes, for a particular topic of the plurality of topics a first value representing a vocabulary of the particular topic and a second value representing a consistency of the particular topic in two more call transcripts of the plurality of call transcripts which include the particular topic. Based, at least in part, on one or more of the first value or the second value, the server computer determines that the particular topic meets a particular criterion and, in response, updates the output of the topic model to remove the particular topic or distinguish the particular topic from other topics of the plurality of topics which do not meet the particular criterion.

7. 发明申请

US20220383863A1 GENERATING TRAINING DATASETS FOR A SUPERVISED LEARNING TOPIC MODEL FROM OUTPUTS OF A DISCOVERY TOPIC MODEL 有权
公开(公告)号：US20220383863A1
公开(公告)日：2022-12-01
申请号：US17817228
申请日：2022-08-03
申请人： Invoca, Inc.
发明人： Michael McCourt , Anoop Praturu
IPC分类号： G10L15/18 , G06N20/00 , G10L15/197 , G10L15/22 , G10L15/06 , G06N5/04
摘要： Systems and methods for generating training data for a supervised topic modeling system from outputs of a topic discovery model are described herein. In an embodiment, a system receives a plurality of digitally stored call transcripts and, using a topic model, generates an output which identifies a plurality of topics represented in the plurality of digitally stored call transcripts. Using the output of the topic model, the system generates an input dataset for a supervised learning model by identify a first subset of the plurality of digitally stored call transcripts that include the particular topic, storing a positive value for the first subset, identifying a second subset that do not include the particular topic, and storing a negative value for the second subset. The input training dataset is then used to train a supervised learning model.

8. 发明授权

US11410644B2 Generating training datasets for a supervised learning topic model from outputs of a discovery topic model 有权
公开(公告)号：US11410644B2
公开(公告)日：2022-08-09
申请号：US16996653
申请日：2020-08-18
申请人： Invoca, Inc.
发明人： Michael McCourt , Anoop Praturu
IPC分类号： G10L15/22 , G10L15/18 , G06N20/00 , G10L15/197 , G10L15/06 , G06N5/04
摘要： Systems and methods for generating training data for a supervised topic modeling system from outputs of a topic discovery model are described herein. In an embodiment, a system receives a plurality of digitally stored call transcripts and, using a topic model, generates an output which identifies a plurality of topics represented in the plurality of digitally stored call transcripts. Using the output of the topic model, the system generates an input dataset for a supervised learning model by identify a first subset of the plurality of digitally stored call transcripts that include the particular topic, storing a positive value for the first subset, identifying a second subset that do not include the particular topic, and storing a negative value for the second subset. The input training dataset is then used to train a supervised learning model.

9. 发明申请

US20210118433A1 DETECTING EXTRANEOUS TOPIC INFORMATION USING ARTIFICIAL INTELLIGENCE MODELS 有权
公开(公告)号：US20210118433A1
公开(公告)日：2021-04-22
申请号：US16996761
申请日：2020-08-18
申请人： Invoca, Inc.
发明人： Michael McCourt , Michael Lawrence
IPC分类号： G10L15/18 , G06N20/00 , G06N5/04 , G10L15/22 , G10L15/183
摘要： Systems and methods for improving machine learning systems used to model topics on a plurality of calls are described herein. In an embodiment, a server computer receives plurality of digitally stored call transcripts that have been prepared from digitally recorded voice calls. The server computer uses a topic model of an artificial intelligence machine learning system, the topic model modeling words of a call as a function of one or more word distributions for each topic of a plurality of topics, to generate an output of the topic model which identifies the plurality of topics represented in the plurality of call transcripts. The server computer computes, for a particular topic of the plurality of topics a first value representing a vocabulary of the particular topic and a second value representing a consistency of the particular topic in two more call transcripts of the plurality of call transcripts which include the particular topic. Based, at least in part, on one or more of the first value or the second value, the server computer determines that the particular topic meets a particular criterion and, in response, updates the output of the topic model to remove the particular topic or distinguish the particular topic from other topics of the plurality of topics which do not meet the particular criterion.

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