Patent classifications
G06F15/76
DATA PROCESSING SYSTEMS AND METHODS FOR BUNDLED PRIVACY POLICIES
Data processing systems and methods, according to various embodiments, are adapted for determining an applicable privacy policy based on various criteria associated with a user and the associated product or service. User and product criteria may be obtained automatically and/or based on user input and analyzed by a privacy policy rules engine to determine the applicable policy. Text from the applicable policy can then be presented to the user. A default policy can be used when no particular applicable policy can be identified using by the rules engine. Policies may be ranked or prioritized so that a policy can be selected in the event the rules engine identifies two, conflicting policies based on the criteria.
DATA PROCESSING SYSTEMS AND METHODS FOR BUNDLED PRIVACY POLICIES
Data processing systems and methods, according to various embodiments, are adapted for determining an applicable privacy policy based on various criteria associated with a user and the associated product or service. User and product criteria may be obtained automatically and/or based on user input and analyzed by a privacy policy rules engine to determine the applicable policy. Text from the applicable policy can then be presented to the user. A default policy can be used when no particular applicable policy can be identified using by the rules engine. Policies may be ranked or prioritized so that a policy can be selected in the event the rules engine identifies two, conflicting policies based on the criteria.
Virtualized Multicore Systems With Extended Instruction Heterogeneity
A system on a chip may include a plurality of data plane processor cores sharing a common instruction set architecture. At least one of the data plane processor cores is specialized to perform a particular function via extensions to the otherwise common instruction set architecture. Such systems on a chip may have reduced physical complexity, cost, and time-to-market, and may provide improvements in core utilization and reductions in system power consumption.
Virtualized Multicore Systems With Extended Instruction Heterogeneity
A system on a chip may include a plurality of data plane processor cores sharing a common instruction set architecture. At least one of the data plane processor cores is specialized to perform a particular function via extensions to the otherwise common instruction set architecture. Such systems on a chip may have reduced physical complexity, cost, and time-to-market, and may provide improvements in core utilization and reductions in system power consumption.
Acceleration System and Dynamic Configuration Method Thereof
An acceleration system includes a plurality of modules. Each of the plurality of modules includes at least one central processing unit, at least one graphics processing unit, at least one field programmable gate array, or at least one application specific integrated circuit. At least one of the plurality of modules includes at least another of the plurality of modules such that the acceleration system is structural and nested.
Acceleration System and Dynamic Configuration Method Thereof
An acceleration system includes a plurality of modules. Each of the plurality of modules includes at least one central processing unit, at least one graphics processing unit, at least one field programmable gate array, or at least one application specific integrated circuit. At least one of the plurality of modules includes at least another of the plurality of modules such that the acceleration system is structural and nested.
Cluster computing
In some embodiments, a computer cluster system comprises a plurality of nodes and a software package comprising a user interface and a kernel for interpreting program code instructions. In certain embodiments, a cluster node module is configured to communicate with the kernel and other cluster node modules. The cluster node module can accept instructions from the user interface and can interpret at least some of the instructions such that several cluster node modules in communication with one another and with a kernel can act as a computer cluster.
Cluster computing
In some embodiments, a computer cluster system comprises a plurality of nodes and a software package comprising a user interface and a kernel for interpreting program code instructions. In certain embodiments, a cluster node module is configured to communicate with the kernel and other cluster node modules. The cluster node module can accept instructions from the user interface and can interpret at least some of the instructions such that several cluster node modules in communication with one another and with a kernel can act as a computer cluster.
System and method for multiclass classification of images using a programmable light source
An apparatus, system and process for identifying one or more different tissue types are described. The method may include applying a configuration to one or more programmable light sources of an imaging system, where the configuration is obtained from a machine learning model trained to distinguish between the one or more different tissue types captured in image data. The method may also include illuminating a scene with the configured one or more programmable light sources, and capturing image data that includes one or more types of tissue depicted in the image data. Furthermore, the method may include analyzing color information in the captured image data with the machine learning model to identify at least one of the one or more different tissue types in the image data, and rendering a visualization of the scene from the captured image data that visually differentiates tissue types in the visualization.
System and method for multiclass classification of images using a programmable light source
An apparatus, system and process for identifying one or more different tissue types are described. The method may include applying a configuration to one or more programmable light sources of an imaging system, where the configuration is obtained from a machine learning model trained to distinguish between the one or more different tissue types captured in image data. The method may also include illuminating a scene with the configured one or more programmable light sources, and capturing image data that includes one or more types of tissue depicted in the image data. Furthermore, the method may include analyzing color information in the captured image data with the machine learning model to identify at least one of the one or more different tissue types in the image data, and rendering a visualization of the scene from the captured image data that visually differentiates tissue types in the visualization.