International Business Machines Corporation

Vmware, Inc.

Omnissa, LLC

Airia Ltd.

Narayana Nethralaya Foundation

Globalfoundries Inc.

Rohit Pradeep Shetty

Arun Ramakrishnan

Logeswaran T Rajamanickam

Ashrith Shetty

Erich Peter Stuntebeck

Ramanandan Nambannor Kunnath

Ramani Panchapakesan

Hariharan Krishna

Albert Manhee Chu

Suman Aluvala

Daryl Michael Seitzer

Sharun Varghese Samuel

Shree Harsha Shedigumme

Adam Samuel Chow

Gaurav Nigam

Ravish Chawla

Vinay G Rajagopal

Chaoting Xuan

Albee Jhoney

Nixon Cheaz

Anthony M Diaz

Craig Farley Newell

Richard A Gebhardt

Sudharsan Thumatti Sathiamoorthy

Gaurav Gupta

Ashish Kumar Agrawal

Pavan Rajkumar Rangain

Ronald A Piro

Pranav Ashok Shenoy

Thomas W Wyckoff

Pushkal Maheshwari

Hariharan L Narayanan

Krishna C Shastry

Umesh Kumar B Balegar

Nalini Vidapankal

Anuj Dubey

Maruthi Latha

Anurag A Sinha

Michael Richard Ouellette

Sudheesh S Kairali

George Maria Braceras

Mujahid Muhammad

Kevin William Gorman

Anindya Neogi

Amit Jain

Geetha Adinarayan

Amit Kumar Yadav

Kar-Fai Tse

Abhijit Sinha Roy

Indumathy Gururao

Umesh Kumar Balaraj Balegar

Pon Venkatesh M

Sheetal Deenadayalan

Shree Harsha S

Harshini Mariswamy

Umesh Kumar Balaraj

Sebastian Theodore Ventrone

Nataraj Nagaratnam

Joseph Andrew Iadanza

Dilip Krishnaswamy

Sreekanth Ramakrishna Iyer

Nazmul Habib

Douglas Willard Stout

Erich Stuntebeck

Christopher Victor Lazzaro

Vijay R Kalangumvathakkal

Spencer Reagan

Arjun Kochhar

Keith A Ford

Rahul Parwani

John Manton

Michael E Head

Jordana H Kerr

Neelima Bojja

Rajan Raman

Ravirajan Rajan

Chris Joseph Rebeor

Harini Kantareddy

Maithreyi Gopalarao

Gopikrishnan Chelliah

Jane S Tu

Manveer Singh Sandhu

Satya R P Choppakatla

Amit Meel

Saurabh Dravid

Sudharsan Thurnatti Sathiamoorthy

Sharun Varghese Samual

Tejas Thvar

Max Blinder

Subramanian Kathiresan

Alex Cherian

Bhujang Shetty

Katkeri Bhujang Shetty

Logeswaram T Rajamanickam

Various examples are disclosed for a zero-trust authentication model for virtual desktop infrastructure (VDI) sessions. Upon user authentication of a VDI session, security posture assessments can be performed that analyze an ongoing or continuous state of the user, client device, or network conditions. Remedial measures or mitigation procedures can be performed to address potential non-compliance of the VDI session.

Zero-trust virtual desktop infrastructure authentication

Systems and methods are described for increasing web browser security on a user device managed by a device management system. In an example, the user device can use an unmanaged web browser to access secure enterprise content using a browser extension provided by the enterprise. When a user attempts to access secure content from an unmanaged browser, the device management system can communicate with the extension and a management application on the user device to authenticate the user and verify that the user device complies with certain policies. In one example, the device management system can include an extension recommendation engine that analyzes user browsing data and recommends browser extensions for the user. Based on policies, the device management system can recommend the extension to the user or force installation of the extension on the user device.

Securing web browsing on a managed user device

The present disclosure relates to translation of voice commands using machine learning. Command text corresponding to a voice command can be received, and at least one error can be identified in the command text. A comparison can be performed between the at least one error and at least one lexical pattern corresponding to a user associated with the voice command. Modified command text can be generated based at least in part on the comparison between the at least one error and the at least one lexical pattern. The modified command text can be determined to fail to comprise an additional error.

Translation of voice commands using machine learning

The invention provides a rules engine that manages user requests within an interface integrating multiple AI platforms and AI Models. Upon receiving a query, the engine assigns scores based on factors like management rules, user preferences, and contextual information. Determinative scores, such as those enforcing strict enterprise policies, can override others, leading the engine to block or reroute the query. If no score is determinative, the engine forwards the query and associated prompts to a specialized routing AI Model for contextual analysis. Based on this analysis, the rules engine directs the query to the most appropriate AI Model or defaults to the user-designated AI Model. This system balances user intent with rule enforcement, optimizing query processing across various AI platforms while ensuring compliance with enterprise guidelines.

Rules engine for dynamic contextual routing to artificial intelligence models

Deriving input restrictions for artificial intelligence agents

Disclosed are various approaches for remote working experience optimization. In some examples, a management service receives task execution data and corresponding user data for multiple client devices. The management service inputs target user context data along with the task execution data and the corresponding user data into a prediction model to identify a task and a task schedule that indicates a time to download and cache task content. Instructions are transmitted for a management agent to download and cache the task content according to the task schedule.

Remote working experience optimization systems

Disclosed are various examples identifying time-sensitive tasks being performed by a user of a client device that may be affected by a network connectivity issue associated with the client device and alerting the user interacting with the client device of the identified time-sensitive tasks. Network connectivity data associated one or more client devices can be used to identify patterns and context in network connectivity loss based at least in part on location, time, date, etc. Additionally, user context data associated with a user can be analyzed to predict whether the user will experience any connectivity issues and identify any tasks or action items being performed by the user interacting with the client device that may be considered time-sensitive. A user can be notified of a potential connectivity issue that may affect the ability for the user to complete a time-sensitive task.

Time-sensitivity task identification service according to predicted connectivity issues

Systems and methods are described for accessing resources of a Unified Endpoint Management ('UEM') system through an enrolled device. In an example, an unenrolled device can be paired with an enrolled device. The unenrolled device can connect to the enrolled device on a local network. The enrolled device can verify the unenrolled device using a key provided during pairing. The unenrolled device can send requests for UEM resources to the enrolled device, which the enrolled device can send to a UEM server. The UEM server can send the requested UEM resources to the enrolled device, and the enrolled device can send the UEM resources to the enrolled device over the local network.

Accessing corporate resources through an enrolled user device

The system provides a permissions engine derives permissions for responses from artificial intelligence (AI) agents. The permissions are derived based on the access permissions of chunks used to generate the response, and the response is cached for reuse with users meeting the permissions requirements. The system can also derive an access permission level for a user attempting to access the cached result or other content. This can be based on a managed access profile that includes user behavior criteria and device criteria. Likewise, the system can validate and derive sensitivity levels of documents that are ingested for use in AI agents and discriminate between chunks to use in synthesizing results based on the derived permissions of the chunks and their relationship to the user.

Derived access permissions for inputs to artificial intelligence agents

The present invention provides a system for processing user queries through an artificial intelligence ('AI') pipeline, utilizing data chunking, question generation, and AI models to deliver contextually relevant responses. The system includes a server that ingests and chunks datasets, generates vector embeddings, and stores the data in one or more vector databases. A pipeline engine sends the chunked data to an AI model that generates potential questions tailored to different user personas. These questions, along with their corresponding data chunks, are stored in the database for future retrieval. When a user submits a query, the system semantically compares the query to the pre-generated question vectors and retrieves the most relevant question and associated data chunk. The query is then sent to an external AI model for final response generation. The system provides seamless interaction, delivering optimized, context-aware responses to user queries in real-time.

Artificial intelligence agent output through caching predicted inputs

Client application for dynamic contextual routing to artificial intelligence models

Examples described herein include systems and methods for authenticating a voice-activated device. An example method can include receiving, at an application server, a request from a user device to authenticate the voice-activated device. The application server can provide a first temporary key and session ID to the user device. The method can further include communicating the first temporary key from the user device to the voice-activated device, such as by reading it aloud or having the user device communicate the key in some manner. The voice-activated device can then provide the key to the application server, which generates a second temporary key and sends it back to the voice-activated device. The second temporary key can then be transferred to the user device, which closes the loop by providing the key back to the application server. The application server can then authenticate and provide access to the voice-activated device.

Two-way authentication for voice-activated devices

Disclosed are various embodiments for enabling an enrolled client device of a user to access an enterprise resource via a second enrolled client device of the user. One such method comprises launching, by the first client device, a peer-to-peer communication channel between the first client device and a second client device of the user that is online with the management server; transmitting, by the first client device, a peer-to-peer offline access mode request over the peer-to-peer communication channel for the first client device to be given access to an enterprise resource that is being managed by the management server, wherein the request includes instructions for the second client device to forward the request to the management server, wherein the request further includes enterprise resource identification and verification data showing that the first client device is in compliance with a compliancy policy of the management service.

Peer-to-peer offline access mode

Growing community of inventors

Bengaluru, India

Rohit Pradeep Shetty