Hcl Technologies Limited

Hewlett Packard Enterprise Development LP

Skyhigh Networks, Inc.

Amit Agrawal

Navin Sabharwal

Nagendra Babu Rapaka

Ravi Suhane

Shailendra Singh Suriyal

Sekhar R Sarukkai

Gopal Gupta

Raghunandan Prabhakar

Abhinesh Mishra

Prasad Raghavendra Somasamudram

Sreenatha Balaganoor

Some examples relate to using a wireless packet for activating a wireless local area network capability on a user device. In an example, a user device receives a wireless packet comprising carrier network identification information from a low energy wireless broadcast enabled access point of a carrier network. In response to determining that the carrier network identification information in the wireless packet matches with carrier network information on the user device, the user device provides a notification message to activate a wireless local area network capability on the user device. After the wireless local area network capability is activated, the user device establishes a network connection with the access point and access the wireless local area network of the carrier network via the network connection.

Wireless packet for activating a wireless local area network capability on a user device

Some examples relate to a verifying the authenticity of IKE exchange messages in a VPN. In an example, a VPN client sends a VPN server profile request message to a VPN server for establishing a VPN connection. In response, the VPN server provides a VPN server profile to the VPN client. The VPN server profile comprises a cryptographic public key associated with a server certificate of the VPN server. The VPN client stores the VPN server profile and sends an IKE message to the VPN server. In response to the IKE message, the VPN server sends a signed IKE response message. Upon receipt, the VPN client verifies the authenticity of the signed IKE response message. If the signed IKE message is successfully verified, the VPN client sends an identity of the VPN client to the VPN server.

Verifying the authenticity of internet key exchange messages in a virtual private network

Some examples relate to a pre-shared key based virtual private network. In an example, a VPN server generates a unique pre-shared key (PSK) corresponding to an identity of a VPN client. The VPN server creates a mapping between the identity and the unique PSK of the VPN client, and stores it in a database. The VPN server shares the unique PSK with the VPN client. In response to receiving an IKE packet comprising an encrypted identity of the VPN client, the VPN server decrypts the encrypted identity of the VPN client from the IKE packet to determine the identity of the VPN client associated with the IKE packet. The VPN server retrieves the unique PSK corresponding to the identity of the VPN client associated with the IKE packet from the mapping stored in the database. The VPN server establishes a VPN connection with the VPN client.

Pre-shared key based virtual private network

In some examples, a first user device receives, from a second user device, a provisioning reference and an access token, the provisioning reference and the access token obtained by the second user device as part of a provisioning procedure performed by the second user device with a server. The first user device accesses the server using the provisioning reference and the access token to provision the first user device. The first user device receives, from the server, a network profile in response to the access token. The first user device connects to a network using the network profile.

Device provisioning with a network profile

Systems and methods related to a VPN controller are provided. In some embodiments, a first VPN controller is configured to establish a VPN tunnel with a client endpoint, wherein the VPN tunnel is established using an authentication process of the client endpoint, route a L2 request to a second VPN controller via an established communication tunnel between the first VPN controller and the second VPN controller by identifying a Generic Routing Encapsulation (GRE) header of the L2 request and based on the GRE header of the L2 request, directing the L2 request to a responsive L2 device accessible by the second VPN controller, receive an encapsulated L2 response from the second VPN controller identifying acceptance of the L2 request, and enable an electronic communication between the client endpoint and the responsive L2 device at least via the VPN tunnel between the client endpoint and the first VPN controller.

ISO layer-two connectivity using ISO layer-three tunneling

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)

Examples relate to a BC network including a plurality of network devices deployed in a network. The plurality of network devices includes an authoritative network device that generates a transaction in a distributed ledger. The transaction includes location information of a new public key certificate to be deployed in each of the network devices. In order to verify the transaction, a network device of the plurality of network devices verifies, using a smart contract, whether the new public key certificate is valid and whether the new public key certificate is different from a previously recorded public key certificate in the distributed ledger. In response to successful verification by at least a predefined number of network devices of the plurality of network devices, each of the network device record the transaction in the distributed ledger.

Updating public key certificates in network devices using a blockchain network

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.

Growing community of inventors

Noida, India

Amit Agrawal