US 2020/0265355 A1Published application

Risk prediction based on automated analysis of documents

AssigneeIcertis, Inc.

InventorsSunu Engineer, Amitabh Jain, Monish Mangalkumar Darda

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Abstract

Embodiments are directed to managing documents over a network. A machine learning (ML) engine analyzes a plurality of documents associated with actions that were performed previously. The ML engine determines critical events associated with the performance of the actions based on the plurality documents. The ML engine generates ML models based on the critical events to compute risk values that may be associated with the critical events. In response to a request to compute risk values associated with pending actions, the ML engine determines documents that are associated with the pending actions based on the request. The ML engine determines the critical events associated with pending actions based on the documents. The ML engine employs the ML models to generate the risk values based on the documents and the critical events. The ML engine provides the risk values in response to the request.

Claims28

Dependent claims

A method for managing documents over a network using one or more processors that execute instructions to perform steps, comprising:

instantiating a machine learning (ML) engine to perform actions, including:
analyzing a plurality of documents that are associated with one or more actions that were performed previously;
determining one or more critical events that are associated with the performance of the one or more actions based on the plurality documents, wherein a portion of the one or more critical events are discovered through application of one or more of rules, heuristics, or ML modeling to the plurality of documents or the one or more actions associated with the plurality of documents;
generating one or more ML models based on the one or more critical events and the plurality of documents, wherein the ML models are trained to predict one or more risk values that are associated with the one or more critical events until an accuracy rate of the predicted one or more risk values meets one or more defined thresholds;
and
in response to a request to provide one or more risk values that are associated with one or more pending actions, performing further steps, including:
determining one or more documents that are associated with the one or more pending actions based on the request;
determining the one or more critical events that are implicitly and explicitly associated with the one or more pending actions based on the request and the one or more documents, wherein the one or more implicit and explicit critical events are included in a data object that references the one or more documents to improve the performance of computing resources employed to execute the instructions;
employing the one or more ML models to generate the one or more risk values based on the one or more documents, the predicted one or more risk values and the one or more implicit and explicit critical events;
and
providing the one or more risk values in response to the request.

8 more elements

A system for managing documents comprising:

a network computer, comprising:
a transceiver that communicates over the network;
a memory that stores at least instructions;
and
one or more processors that execute instructions that perform steps, including:
instantiating a machine learning (ML) engine to perform actions, including:
analyzing a plurality of documents that are associated with one or more actions that were performed previously;
determining one or more critical events that are associated with the performance of the one or more actions based on the plurality documents, wherein a portion of the one or more critical events are discovered through application of one or more of rules, heuristics, or ML modeling to the plurality of documents or the one or more actions associated with the plurality of documents;
generating one or more ML models based on the one or more critical events and the plurality of documents, wherein the ML models are trained to predict one or more risk values that are associated with the one or more critical events until an accuracy rate of the predicted one or more risk values meets one or more defined thresholds;
and
in response to a request to provide one or more risk values that are associated with one or more pending actions, performing further steps, including:
determining one or more documents that are associated with the one or more pending actions based on the request;
determining the one or more critical events that are implicitly and explicitly associated with the one or more pending actions based on the request and the one or more documents, wherein the one or more implicit and explicit critical events are included in a data object that references the one or more documents to improve the performance of computing resources employed to execute the instructions;
employing the one or more ML models to generate the one or more risk values based on the one or more documents, the predicted one or more risk values, and the one or more implicit and explicit critical events;
and
providing the one or more risk values in response to the request;
and
a client computer, comprising:
a transceiver that communicates over the network;
a memory that stores at least instructions;
and
one or more processors that execute instructions that perform actions, including:
providing the request to predict the one or more risk values.

20 more elements

A processor readable non-transitory storage media that includes instructions for managing document over a network, wherein execution of the instructions by one or more processors on one or more network computers performs steps, comprising:

instantiating a machine learning (ML) engine to perform actions, including:
analyzing a plurality of documents that are associated with one or more actions that were performed previously;
determining one or more critical events that are associated with the performance of the one or more actions based on the plurality documents, wherein a portion of the one or more critical events are discovered through application of one or more of rules, heuristics, or ML modeling to the plurality of documents or the one or more actions associated with the plurality of documents;
generating one or more ML models based on the one or more critical events and the plurality of documents, wherein the ML models are trained to predict one or more risk values that are associated with the one or more critical events until an accuracy rate of the predicted one or more risk values meets one or more defined thresholds;
and
in response to a request to provide one or more risk values that are associated with one or more pending actions, performing further steps, including:
determining one or more documents that are associated with the one or more pending actions based on the request;
determining the one or more critical events that are implicitly and explicitly associated with the one or more pending actions based on the request and the one or more documents, wherein the one or more implicit and explicit critical events are included in a data object that references the one or more documents to improve the performance of computing resources employed to execute the instructions;
employing the one or more ML models to generate the one or more risk values based on the one or more documents, the predicted one or more risk values and the one or more implicit and explicit critical events;
and
providing the one or more risk values in response to the request.

8 more elements

A network computer for managing documents, comprising:

a transceiver that communicates over the network;
a memory that stores at least instructions;
and
one or more processors that execute instructions that perform steps, including:
instantiating a machine learning (ML) engine to perform actions, including:
analyzing a plurality of documents that are associated with one or more actions that were performed previously;
determining one or more critical events that are associated with the performance of the one or more actions based on the plurality documents, wherein a portion of the one or more critical events are discovered through application of one or more of rules, heuristics, or ML modeling to the plurality of documents or the one or more actions associated with the plurality of documents;
generating one or more ML models based on the one or more critical events and the plurality of documents, wherein the ML models are trained to predict one or more risk values that are associated with the one or more critical events until an accuracy rate of the predicted one or more risk values meets one or more defined thresholds;
and
in response to a request to provide one or more risk values that are associated with one or more pending actions, performing further steps, including:
determining one or more documents that are associated with the one or more pending actions based on the request;
determining the one or more critical events that are implicitly and explicitly associated with the one or more pending actions based on the request and the one or more documents, wherein the one or more implicit and explicit critical events are included in a data object that references the one or more documents to improve the performance of computing resources employed to execute the instructions;
employing the one or more ML models to generate the one or more risk values based on the one or more documents, the predicted one or more risk values and the one or more implicit and explicit critical events;
and
providing the one or more risk values in response to the request.

12 more elements

Dependencies are inferred from the claim text.