Abstract: A computer system routes contact center interactions. Interactions between contact center agents and contact center queries that are received at a contact center are monitored. A ranking model is trained according to the categories of the contact center queries and the interaction scores of each handled query using machine learning. The ranking model is tested according to various metrics to ensure that the ranking model ranks the agents according to one or more selected business outcomes. A net score may be determined for each contact center agent for each query category based on a predicted interaction score and one or more non-interaction features. Incoming queries may then be routed to an appropriate contact center agent based on the category of the incoming query. Embodiments may further include a method and program product for routing contact center interactions in substantially the same manner described above.

Abstract: Aspects of the present disclosure provide for a novel system and method where users can promote, subscribe, license, train collaboratively, and/or trade AI models in real-time over an AI trusted model network that represents a group of trusted users across platform agnostic ways. The system and methods described herein may effectively provide a marketplace to collaboratively develop and reliably disseminate developed AI models to other users who may have similar needs. The system may include an AI user device server (AI-UDS), an AI trusted model network (AI-TMN), an AI secure marketplace system (AI-SMPS), an AI collaborative training platform (AI-CTP), an AI secure transaction fulfillment system (AI-STFS), and an AI model license subscription management platform (AI-LSMP).

Abstract: Aspects of the present disclosure are presented for an AI solution model processing infrastructure that allows owners of machines that perform AI model training and inference to rent the processing resources of the AI machines as a service to the one or more subscribers on an as-needed basis. This allows a subscriber to dynamically rent the service to run one or more AI models to train or infer in a given place/location to accomplish particular AI goals without owning an AI solution model processing-capable machine at a given location. Rather than going through the hard work, resource utilization and cost of doing setting one's own AI compute processing, the rental service described herein will provide a pay as you need scheme to run one's AI model in a rented environment.

Abstract: Aspects of the present disclosure are presented for an AI system featuring specially designed AI hardware that incorporates security features to provide iron clad trust and security to run AI applications/solution models. Presented herein are various security features for AI processing, including: a trust and integrity verifier of data during operation of an AI solution model; identity and trust establishment between an entity and the AI solution model; secure isolation for a virtual AI multilane system; a real-time attack detection and prevention mechanism; and built in detection mechanisms related to rogue security attack elements insertion during manufacturing. Aspects also include security to implement an AI network interconnecting multiple user devices in an AI environment.

Abstract: An artificial intelligence (AI) system is disclosed. The AI system provides an AI system lane processing chain, at least one AI processing block, a local memory, a hardware sequencer, and a lane composer. Each of the at least one AI processing block, the local memory coupled to the AI system lane processing chain, the hardware sequencer coupled to the AI system lane processing chain, and the lane composer is coupled to the AI system lane processing chain. The AI system lane processing chain is dynamically created by the lane composer.

Abstract: An artificial intelligence (AI) system is disclosed. The AI system provides an energy efficient hyper parallel and pipelined temporal and spatial scalable secure AI hardware with minimized external memory access. One or more than one re-configurable AI compute engine blocks may be interconnected via one or more high speed interconnect busses to enable an AI processing chain and data exchange between themselves. A hardware sequencer is disclosed to enable an AI processing chain execution driven by dynamically composed AI processing chains.

Abstract: Aspects of the disclosure are presented for an elegant mechanism to allow for AI training using an AI system that is platform agnostic and eliminates the need for multi processors, e.g., CPU, VMs, OS & GPU based full stack software AI frameworks. The AI system may utilize an asynchronous or file system interface, allowing for a send/drop interface of an input data file to automatically run training or inference of an AI solution model. Existing AI solutions would require multi machine learning, deep learning frameworks, and/or one or more SDKs to run to run on CPU, GPU and accelerator environments. The present disclosures utilize special AI hardware that does not rely on such conventional implementations.

Abstract: Aspects of the present disclosure are presented for a power management system of a multilane AI system architecture. The system may include an orchestrator configured to control power and other operations of a lane. An uber orchestrator manages the overall system, and may know all of the multilane systems within the AI virtual multilane system that need to be active at a given frequency and power envelope for given price, and performance constraints. The orchestrator of each lane knows the compute/logic blocks that need to be active for a given AI app model AI processing chain execution. The orchestrator may be configured to send commands to turn off power to certain components that are not utilized in performing an AI execution sequence, deactivate operation to the lane when its functions are completed, and also modulate the clock frequency of a lane to fit the computation demands while minimizing power usage.

Abstract: Aspects of the present disclosure are presented for an autonomous adaptive AI self-learning, training and inferencing system and method that would provide extremely cost effective and energy efficient broad based AI solutions/applications that are personalized/customizable. In some embodiments, a proposed component is an intelligent sense neuro memory cell unit (ISN-MCU). The ISN-MCU acts as the basic building block for AI adaptive learning. Each ISN-MCU is capable of receiving input from the surrounding environment and then learning or making an inference about the received data. With enough time or many more ISN-MCUs in combination, the AI system may be capable of learning for what it was programmed for in real time and in a memory and time efficient manner.

Abstract: Techniques are provided for storage tier verification checks. A determination is made that a mount operation of an aggregate of a set of volumes stored within a multi-tier storage environment has completed. A first metafile and a second metafile are maintained to track information related to the storage of objects of a volume of the aggregate within a remote object store that is a tier of the multi-tier storage environment. A distributed verification is performed between the first metafile and the second metafile to identify an inconsistency. Accordingly, the first metafile and the second metafile are reconciled to address the inconsistency so that storage information within the first metafile and the second metafile are consistent.

Abstract: A refrigerator appliance and ice bin assembly therefor are provided herein. The ice bin may include a bin body, an ice sweep, a sweep gear, and a drive gear. The bin body may define a storage volume to receive ice therein. The ice sweep may be positioned below the storage volume. The sweep gear may be rotatable about a sweep axis. The drive gear may be positioned in mechanical communication with the sweep gear. The drive gear may be rotatable about a drive axis, the drive axis extending along a non-parallel angle relative to the sweep axis.

Abstract: A computer system routes contact center interactions. Interactions between contact center agents and contact center queries that are received at a contact center are monitored. A ranking model is trained according to the categories of the contact center queries and the interaction scores of each handled query using machine learning. The ranking model is tested according to various metrics to ensure that the ranking model ranks the agents according to one or more selected business outcomes. A net score may be determined for each contact center agent for each query category based on a predicted interaction score and one or more non-interaction features. Incoming queries may then be routed to an appropriate contact center agent based on the category of the incoming query. Embodiments may further include a method and program product for routing contact center interactions in substantially the same manner described above.

Abstract: Techniques are provided for storage tier verification checks. A determination is made that a mount operation of an aggregate of a set of volumes stored within a multi-tier storage environment has completed. A first metafile and a second metafile are maintained to track information related to the storage of objects of a volume of the aggregate within a remote object store that is a tier of the multi-tier storage environment. A distributed verification is performed between the first metafile and the second metafile to identify an inconsistency. Accordingly, the first metafile and the second metafile are reconciled to address the inconsistency so that storage information within the first metafile and the second metafile are consistent.

Abstract: A refrigerator appliance and ice bin assembly therefor are provided herein. The ice bin may include a bin body, an ice sweep, a sweep gear, and a drive gear. The bin body may define a storage volume to receive ice therein. The ice sweep may be positioned below the storage volume. The sweep gear may be rotatable about a sweep axis. The drive gear may be positioned in mechanical communication with the sweep gear. The drive gear may be rotatable about a drive axis, the drive axis extending along a non-parallel angle relative to the sweep axis.

Abstract: A device comprises a flame detector, a camera, a mounting device, and a network connection device. The camera has a first field of view that overlaps a second field of view of the flame detector. The mounting device comprises one or more motors to change the first field of view and the second field of view, and the network connection device is configured to provide communication between an output of the flame detector, an output of the camera, and a remote device.

Abstract: Dry liquid concentrates allow carbon nanotubes to be dispersed in rubber formulations under standard rubber processing conditions. The incorporation of carbon nanotubes can enhance the physical properties of the resulting rubber material in many ways, including creating a more resilient rubber which resists abrasion, tearing, and chipping.

Abstract: Dry liquid concentrates allow carbon nanotubes to be dispersed in rubber formulations under standard rubber processing conditions. The incorporation of carbon nanotubes can enhance the physical properties of the resulting rubber material in many ways, including creating a more resilient rubber which resists abrasion, tearing, and chipping.

Abstract: Presented herein are methods, non-transitory computer readable media, and devices triggering a metadata recovery process within a network storage system, which include: dividing metadata into metadata segments, wherein each of the metadata segments is tasked to perform a specific file system operation function, validating each of the metadata segments during the specific file system operation function; upon failure to validate at least one of the metadata segments, triggering an automatic repair process while maintaining the operation function tasked to the metadata segment, and upon finalizing the automatic repair process, resuming the specific file system operation function tasked to the metadata segment.

Abstract: Techniques are provided for storage tier verification checks. A determination is made that a mount operation of an aggregate of a set of volumes stored within a multi-tier storage environment has completed. A first metafile and a second metafile are maintained to track information related to the storage of objects of a volume of the aggregate within a remote object store that is a tier of the multi-tier storage environment. A distributed verification is performed between the first metafile and the second metafile to identify an inconsistency. Accordingly, the first metafile and the second metafile are reconciled to address the inconsistency so that storage information within the first metafile and the second metafile are consistent.

Abstract: A device comprises a flame detector, a camera, a mounting device, and a network connection device. The camera has a first field of view that overlaps a second field of view of the flame detector. The mounting device comprises one or more motors to change the first field of view and the second field of view, and the network connection device is configured to provide communication between an output of the flame detector, an output of the camera, and a remote device.