Icertis, Inc.

Bmc Software, Inc.

Monish Mangalkumar Darda

Amitabh Jain

Sunu Engineer

Ravi Kothari

Suhas A Kelkar

Nagi Prabhu

Yogesh Haribhau Kulkarni

Dhruv Chaudhari

Harshil Shah

Karanbir Gujral

Manikantan Narender Nath

Embodiments are directed to a machine learning engine that determines training documents and validation documents from a plurality of documents. The machine learning engine may determine attributes associated with the documents. In response to receiving a request to predict attribute values of a selected document the machine learning engine may train a plurality of ML models to predict the attribute values based on the training documents and the attributes and associate the trained ML models with an accuracy score. The machine learning engine may determine candidate ML models from the trained ML models based on the training accuracy scores. The machine learning engine may evaluate and rank the candidate ML models based on the request and the validation documents. The machine learning engine may generate confirmed ML models based on the ranked candidate ML models such that the confirmed ML models may answer the request.

Automated training and selection of models for document analysis

Embodiments are directed to representing documents using document keys. Documents that include one or more clauses may be provided. Each clause type for the one or more clauses in documents may be determined based on one or more classification models. One or more clause identifiers may be associated with the one or more clauses based on one or more clause types of each clause. A document key may be generated for each document based on an ordered collection of the one or more clauses included in each document such that each clause identifier may be positioned in the document key based on an order of its location in a corresponding clause of a document. The documents may be analyzed based on evaluations of one or more document keys corresponding to the documents. One or more reports may be generated based on one or more results of the analysis.

Representing documents using document keys

Embodiments are directed to representing documents using document keys. Documents that include one or more clauses may be provided. Each clause type for the one or more clauses in documents may be determined based on one or more classification models. One or more clause identifiers may be associated with the one or more clauses based on one or more clause types of each clause. A document key may be generated for each document based on an ordered collection of the one or more clauses included in each document such that each clause identifier may be positioned in the document key based on an order of its location in a corresponding clause of a document. The documents may be analyzed based on comparisons of one or more document keys corresponding to the documents. One or more reports may be generated based on one or more results of the analysis.

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.

Risk prediction based on automated analysis of documents

Embodiments are directed to managing documents where clauses in a document may be identified. Evaluations of the clauses may be provided based on evaluators and machine learning (ML) models that assign each of the clauses to a category and a confidence score. Actions associated with the clauses may be monitored including updates to content of the clauses. Inconsistent evaluations associated with the clauses be identified. The ML models may be retrained based on the content of the clauses associated with the inconsistent evaluations.

Clause discovery for validation of documents

Disclosed is a method of managing computer resources in a dynamic computing environment. The method includes identifying available resources from an available pool based on an augmented model, the available pool including resources unallocated resources, allocating the identified available resources in accordance with the augmented model, identifying reserve resources from a reserve pool based on the augmented model, the reserve pool including resources not allocated and not configured, and upon determining the available pool includes a number of resources below a threshold, replenishing the available pool with the identified reserve resources.

Automating application provisioning for heterogeneous datacenter environments

Disclosed are methods and systems for intelligent resource pool management of heterogeneous datacenter resources. In one embodiment, intelligent resource pool management is utilized to assist in application provisioning performed based upon a blueprint and deployment model defining requirements of the provisioned application. In other embodiments, intelligent resource pool managers are configured to work in concert with other intelligent resource pool managers and/or a centralized provisioning engine. Resource pools may also be configured in a hierarchical manner whereby higher level resource pools may automatically draw resources from lower level resource pools as directed by one or more intelligent resource pool managers.

Growing community of inventors

Pune, India

Monish Mangalkumar Darda