Hcl Technologies Limited

Hcl America Inc.

Navin Sabharwal

Amit Agrawal

A method and system for performing cloud vendor arbitrage using AI is disclosed. The method includes receiving each of a plurality of metrices for each of a set of components associated with an application and infrastructure deployment, and creating one or more feature vectors corresponding to each of the plurality of metrices. The one or more feature vectors are created based on corresponding one or more first pre-trained machine learning models. The method further includes predicting a metric value corresponding to each of plurality of metrices, based on assessing of the one or more feature vectors using corresponding one or more first pre-trained machine learning models and performing cloud vendor arbitrage by computing prices for each of the set of components from price data received from each of a plurality of cloud vendors. The method further includes determining a cloud preference from at least one of plurality of cloud vendors.

Method and system for performing cloud vendor arbitrage using artificial intelligence (AI)

Method and system for determining a conversation system from a multi-conversation system using Artificial Intelligence (AI) is provided. The method includes receiving a user query associated with a domain and creating a hierarchical tree comprising a root node and a child node using a first pre-trained machine learning model. The method further includes traversing the hierarchical tree for a path between root node and one leaf child node to identify a topic hierarchy. The path is associated with a confidence score corresponding to mapping between user query and match data of nodes in the path. The method further includes determining a conversation system from the multi-conversation system for outputting data to answer the user query corresponding to one leaf child node of one path with a highest confidence score.

System and method for designing artificial intelligence (AI) based hierarchical multi-conversation system

A method and system for determining collaboration between employees is disclosed. In some embodiments, the method includes receiving a plurality of collaboration parameters associated with a set of employees. The method further includes creating a plurality of employee nodes associated with the set of employees in a hierarchical tree, based on the plurality of collaboration parameters and a first pre-trained machine learning model. The method further includes generating a plurality of vector embeddings associated with the plurality of employee nodes, based on the first pre-trained machine learning model. The method further includes determining a degree of collaboration between at least two employees from the set of employees based on one or more vector embeddings from the generated plurality of embeddings.

Method and system for determining collaboration between employees using artificial intelligence (AI)

Disclosed is a system for generating Clinical trial protocol design document with selection of a Patient and an Investigator for a clinical trial process. The system inputs meaningful information derived from the raw data, a pre-Drafted protocol, a regulatory authorities' protocol curated by regulatory authorities, and a pre-stored dataset, present in a repository. A Clinical trial protocol design document is drafted by generating a case frame upon extracting data in form of a key value into a standard document. Each key value is validated and a prediction score is computed based on overlapping of the interim Clinical trial protocol design template with the pre-Drafted protocol and the regulatory authorities' protocol to determine whether the interim Clinical trial protocol design document is approved or rejected. A Clinical trial protocol design document is generated when the interim Clinical trial protocol design document is approved.

System and method for generating clinical trial protocol design document with selection of patient and investigator

The present disclosure relates to system(s) and method(s) to perform control testing to mitigate risks in an organization. The system may extract sentences from control documents, and may classify the sentences into one of questions and non-questions, based on at least one of active learning and pro-active learning. Interpretations of the questions may thereafter be generated. Relevant documents related to each of the interpretations of the questions may be identified and extracted, from repositories. Artificial Intelligence (AI) may be used to identify the relevant documents. A cognitive master may be implemented to organize meetings between control testers and process owners for discussing over effectiveness of design and implementation test of test plans to mitigate the risks.

System and method to perform control testing to mitigate risks in an organization

The invention relates to system and method for automating software development life cycle. In some embodiments, the method includes receiving a plurality of code snippets being utilized for developing a software application from a plurality of sources in a software development life cycle, generating a plurality of embedding vectors corresponding to the plurality of code snippets, and generating a high-level feature vector corresponding to each of the plurality of code snippets based on the corresponding embedding vector using a deep learning model. The method further includes generating a final merged code comprising a final sequence of code lines by combining the sequence of code lines corresponding to the plurality of code snippets using the deep learning model. The deep learning model is trained to generate the high-level feature vector and arrange the sequence of code lines based on historical data from the software development life cycle.

System and method for automating software development life cycle

Disclosed is a system for automatically summarizing documents pertaining to a predefined domain. A document finder module enables a web crawler to crawl web resources in order to find a plurality of documents. A keyword determination module determines a set of keywords from the plurality of documents and a rank associated to each keyword of the set of keywords. A clustering module clusters the plurality of documents into one or more clusters. A score computation module identifies a subset of the set of keywords for each cluster upon computing a similarity score, corresponding to each keyword, for each cluster. A summary generation module generates a summary for each cluster based on presence of one or more keywords, of the subset, in each document classified in the cluster.

System and method for automatically summarizing documents pertaining to a predefined domain

The present disclosure relates to system(s) and method(s) for generating a score for a runbook or a script. The system receives a ticket and a ticket description. The system further identifies a set of policies based on an analysis of historical data, the ticket and the ticket description. Further, the system determines a set of functions based on an analysis of the set of policies using a neural network technique and an Inverse Reinforcement Learning technique. Furthermore, the system recommends a runbook or a script based on the set of functions, the ticket and the ticket description. The runbook or the script is further executed to resolve the ticket. Based on the execution, the system records a script success or a script failure. The system further generates a score for the runbook or the script based on the script success or the script failure.

Generating a score for a runbook or a script

The present disclosure relates to system(s) and method(s) for assisting a user to resolve a hardware issue and a software issue. The system identifies, a target cluster, associated with a new ticket received from the user, from the set of clusters. Further, the system recommends one or more runbook scripts, from a runbook repository, associated with the new ticket. The system further identifies a new runbook script, corresponding to the new ticket, from a set of external repositories. Further, the system executes at least one of the one or more runbook scripts or the new runbook script, associated with the new ticket. The system further generates a document based on the execution of the one or more runbook scripts or the new runbook script, thereby assisting the user to resolve a target issue.

System and method for assisting user to resolve a hardware issue and a software issue

Disclosed is a system for removing bugs present in a software code. A determination module determines a usage pattern of a software code by using an Artificial Neural Network (ANN) technique. A comparison module compares the usage pattern with a set of pre-stored usage patterns of software applications similar to the software code. An execution module executes a set of test suites, on the software code, associated to at least one software application of the software applications, when a usage pattern of the at least one software application is matched with the usage pattern of the software code. An identification module identifies a code snippet comprising the bug. A recommendation module recommends a code patch, corresponding to the code snippet, from a ranked list of code patches determined by a Deep RNN technique. Further, a replacement module replaces the code snippet with the code patch thereby removing the bug.

Automated bug fixing

Disclosed is a system for facilitating reusability of a code snippet during development of a software application. Initially, a plurality of tokens is extracted, by using an Artificial Intelligence (AI) based syntactic analysis, from a sequence of lines of code entered by a developer. Further, each token of the plurality of tokens is converted into a vector by using a neural word embedding technique. Subsequently, a context of the plurality of tokens is determined by using a deep autoencoder neural network technique. Furthermore, at least one code snippet is recommended from a plurality of code snippets corresponding to the context. To do so, the context is compared with a plurality of contexts by using a Deep Recurrent Neural Network (Deep RNN) technique. Upon comparison, a confidence score is computed for each code snippet. Finally, the at least one code snippet is selected based on the confidence score.

Code reusability

A system and method is provided to rank contributors to a knowledge base. In an automated operation, a ranking value is calculated for each of a plurality of knowledge-based contributors based both on document information and on personal network information pertaining to the respective contributor. The document information identifies relationships being documents in the knowledge base and the particular contributor, indicating, for example, whether the contributor authored the document, read the document, or rated the document. The personal network information indicates personal connections in the knowledge base between the respective contributors.

Automated ranking of contributors to a knowledge base

A system and method to manage configuration of an information system comprises an automated risk evaluation for a requested change of a configuration item in an information system. Change history information indicative of the effects of multiple historical changes to respective configuration items may accessed, and the automated risk evaluation may be based at least in part on the change history information. The requested change may be authorized or as part based on an assessment of a risk value which is calculated in the automated risk evaluation. Additional apparatus, systems, and methods are described.

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Noida, India

Navin Sabharwal