Abstract: A method for secure storage of cybersecurity data in a blockchain includes: identifying, by a processor of a processing server, a device profile for a computing device; encrypting, by the processor of the processing server, the device profile into an encrypted device profile using a public key of a first cryptographic key pair; encrypting, by the processor of the processing server, the encrypted device profile into a converted device profile via quantum cryptography using a first configuration key; and transmitting, by a transmitter of the processing server, the converted device profile to a blockchain node in a blockchain network.

Abstract: System and methods for bone fusion are disclosed. The systems and method are directed to forming complementary surfaces on bones on either side of an interphalangeal joint and limiting the relative movement thereof so as to promote fusion of the joint.

Abstract: In one embodiment a Hardware Server Module (HSM) (10) implementing a distributed quorum authentication enforcement is provided, whereby user access to a resource (40) on the device (10) is enforced via an API gateway (16). The HSM comprises one or more resources, a separate resource manager API for accessing the one or more resources, an enforcement module for enforcing access to the one or more resources via the API gateway according to a quorum policy, and a quorum manager for generating and storing a quorum request in a database. The API gateway (16) can be a RESTful API using HTTP requests to produce and consume data related to quorum services via at least one of a GET, PUT, POST, PATCH and DELETE command type. Other embodiments are disclosed.

Abstract: Systems and methods for visualization monitoring data from a cloud-based system include obtaining the monitoring data, wherein the monitoring data is based on transactions associated with a plurality of users of the cloud-based system; providing a Graphical User Interface (GUI); obtaining a plurality of filter selections for a plurality of filter types; and displaying a visualization comprising a Sankey diagram of the monitoring data with nodes in the Sankey diagram including each of the plurality of filter types and links between the nodes indicative of the transactions in the monitoring data. The monitoring data can be for one or more of cloud security service transactions, application access via a Zero Trust Network Access (ZTNA) service, and user experience metrics.

Abstract: Systems and methods used in a cognitive assistant for detecting human emotions from speech audio signals is described. The system obtains audio signals from an audio receiver and extracts human speech samples. Subsequently, it runs a machine learning based classifier to analyze the human speech signal and classify the emotion observed in it. The user is then notified, based on their preferences, with a summary of the emotion detected. Notifications can also be sent to other systems that have been configured to receive them. Optionally, the system may include the ability to store the speech sample and emotion classification detected for future analysis. The system's machine learning classifier is periodically re-trained based on labelled audio speech data and updated.

Abstract: Systems and methods include intercepting traffic on the user device; forwarding the traffic to a cloud-based system for security processing therein; and, responsive to unavailability of the cloud-based system preventing the forwarding, performing local security processing of the traffic at the user device including determining whether the traffic is allowed based on a cache at the user device, forwarding the traffic separate from the cloud-based system when it is allowed, and blocking the traffic when it is not allowed.

Abstract: Systems and methods for troubleshooting and performance analysis of a cloud-based service include receiving metrics over time from a plurality of analyzers, wherein the metrics include service-related metrics and network-related metrics related to a cloud-based service, wherein each analyzer of the plurality of analyzers is executed at one of a user device accessing the cloud-based service and in the cloud-based service, and wherein at least one analyzer is executed in the cloud-based service; analyzing the metrics to determine a status of the cloud-based service over the time; and identifying issues related to the cloud-based service utilizing the analyzed metrics over the time, wherein the issues include any of an issue on a particular user device, an issue in a network between a particular user device and the cloud service, and an issue within the cloud service.

Abstract: In one embodiment a Hardware Server Module (HSM) (10) implementing a distributed quorum authentication enforcement is provided, whereby user access to a resource (40) on the device (10) is enforced via an API gateway (16). The HSM comprises one or more resources, a separate resource manager API for accessing the one or more resources, an enforcement module for enforcing access to the one or more resources via the API gateway according to a quorum policy, and a quorum manager for generating and storing a quorum request in a database. The API gateway (16) can be a RESTful API using HTTP requests to produce and consume data related to quorum services via at least one of a GET, PUT, POST, PATCH and DELETE command type. Other embodiments are disclosed.

Abstract: Techniques for deep tracing of one or more users via a cloud-based system include receiving a request from an administrator to actively troubleshoot a user; causing a user device associated with the user to create a deep tracing session based on the request; assisting the user device in performing one or more traces of a plurality of traces to a destination; receiving results from any of the plurality of traces and results from metrics collected at the user device; and displaying a network map between the user device and the destination.

Abstract: Mobile device security, device management, and policy enforcement are described in a cloud-based system where the “cloud” is used to pervasively enforce security and policy and perform device management regardless of device type, platform, location, etc. A method includes receiving one or more mobile profiles for one or more mobile devices each associated with a user from an enterprise; responsive to enrollment of a mobile device of the one or more mobile devices, communicating to the mobile device; determining an associated mobile profile of the one or more mobile profiles for the mobile device; and configuring the mobile device based on the associated mobile profile.

Abstract: Mobile device security, device management, and policy enforcement are described in a cloud-based system where the “cloud” is used to pervasively enforce security and policy and perform device management regardless of device type, platform, location, etc. A method includes receiving one or more mobile profiles for one or more mobile devices each associated with a user from an enterprise; responsive to enrollment of a mobile device of the one or more mobile devices, communicating to the mobile device; determining an associated mobile profile of the one or more mobile profiles for the mobile device; and configuring the mobile device based on the associated mobile profile.

Abstract: Mobile device security, device management, and policy enforcement are described in a cloud-based system where the “cloud” is used to pervasively enforce security and policy and perform device management regardless of device type, platform, location, etc. A method includes receiving one of a mobile profile and an application for an enterprise and a cloud-based system; installing the one of the mobile profile and the application on the mobile device; connecting to a network using the one of the mobile profile and the application; and having traffic content inspected and policy enforced thereon to/from the mobile device and the network via the cloud-based system.

Abstract: Upon selection of an item, several acquisition options may be available for a user to acquire that item. The attributes of these options vary based on price, delivery speed, and so forth. Differences between a reference option and other options are determined. These differences are used to determine a caption identifier. Caption output data is determined based the caption identifier and presented within a user interface with a corresponding control. The control allows the user to acquire the item using the corresponding option. The caption provides the user with a concise and clear reason as to why a particular option is beneficial to the user. For example, a “Willing to wait?” caption shows an acquisition option for which the item is available at lower cost but longer delivery time than the reference option. Services may obtain the caption output from a caption generator using an application programming interface (API).

Abstract: Systems and methods used in a cognitive assistant for detecting human emotions from speech audio signals is described. The system obtains audio signals from an audio receiver and extracts human speech samples. Subsequently, it runs a machine learning based classifier to analyze the human speech signal and classify the emotion observed in it. The user is then notified, based on their preferences, with a summary of the emotion detected. Notifications can also be sent to other systems that have been configured to receive them. Optionally, the system may include the ability to store the speech sample and emotion classification detected for future analysis. The system's machine learning classifier is periodically re-trained based on labelled audio speech data and updated.

Abstract: Techniques for deep tracing of one or more users via a cloud-based system include receiving a request from an administrator to actively troubleshoot a user; causing a user device associated with the user to create a deep tracing session based on the request; assisting the user device in performing one or more traces of a plurality of traces to a destination; receiving results from any of the plurality of traces and results from metrics collected at the user device; and displaying a network map between the user device and the destination.

Abstract: Systems and methods for visualization monitoring data from a cloud-based system include obtaining the monitoring data, wherein the monitoring data is based on transactions associated with a plurality of users of the cloud-based system; providing a Graphical User Interface (GUI); obtaining a plurality of filter selections for a plurality of filter types; and displaying a visualization comprising a Sankey diagram of the monitoring data with nodes in the Sankey diagram including each of the plurality of filter types and links between the nodes indicative of the transactions in the monitoring data. The monitoring data can be for one or more of cloud security service transactions, application access via a Zero Trust Network Access (ZTNA) service, and user experience metrics.

Abstract: Mobile device security, device management, and policy enforcement are described in a cloud-based system where the “cloud” is used to pervasively enforce security and policy and perform device management regardless of device type, platform, location, etc. A method includes receiving one of a mobile profile and an application for an enterprise and a cloud-based system; installing the one of the mobile profile and the application on the mobile device; connecting to a network using the one of the mobile profile and the application; and having traffic content inspected and policy enforced thereon to/from the mobile device and the network via the cloud-based system.

Abstract: Systems and Methods for assisting the hearing-impaired are described. The methods rely on obtaining audio signals from the ambient environment of a hearing-impaired person. The audio signals are analyzed by a machine learning model that can classify audio signals into audio categories (e.g. Emergency, Animal Sounds) and audio types (e.g. Ambulance Siren, Dog Barking) and notify the user leveraging a mobile or wearable device. The user can configure notification preferences and view historical logs. The machine learning classifier is periodically trained externally based on labelled audio samples. Additional system features include an audio amplification option and a speech to text option for transcribing human speech to text output.