Abstract: A medical wearable matching system is provided that pre-assesses a fit of a wearable article of medical device for a patient. The matching system may facilitate fitting and selection of a class of wearable articles for a patient. The matching system may also evaluate potential effects of the fit on operations of the medical device. Patient specific data, such as measurements, are fed into a fit prediction artificial intelligence (“AI”) model. The fitting AI model is trained with fitting data of previous patients to predict a class of wearable medical article that is likely, above a threshold amount, to provide a target fit for the patient. The patient specific information may also be fed into an adverse potential AI model. The adverse potential AI model is trained with patient experience data describing adverse operations of a wearable article worn by prior patients.

Abstract: A system, method, and computer-readable medium are disclosed for intelligent User workload orchestration for virtual meetings. A virtual meeting for multiple users is initiated. Voice data of multiple users is received and converted to text data. Machine learning processes the text data to action items to be performed by specific users (i.e., meeting attendees) in real-time. Minutes of the meeting and action items (in the form of real-time and passive recommendations) are generated and provided. In order to perform the action items, determination is made as to which third party applications support the action items and intelligent user workload orchestration is employed.

Abstract: A dynamic controlling framework that is adapted to dynamically optimize existing routing decisions in an optimized link state routing (OLSR) platform to favor higher-bandwidth, lower-latency links in the active link database. The dynamic controlling framework includes a link probing tool that is in communication with OLSR platform where the link probing tool is adapted to dynamically measure estimated bandwidth, latency scores, or both estimated bandwidth and latency scores of active links in the active link database of the OLSR platform. The dynamic controlling framework includes a plug-in framework that is in communication with the OLSR platform and the link probing tool where the plugin framework is adapted to receive at least one link quality multiplier (LQM) metric computed by the link probing tool and to combine the at least one LQM metric with the OLSR platform's existing link quality (LQ) metric for enhancing route selection by the OLSR platform.

Abstract: A medical device troubleshooting system is provided to interact with and assist a user in resolving operational problems with a medical device. The troubleshooting system may detect an operational problem with the medical device and initiate a troubleshooting session to output a series of different tailored prompts for a user to rectify the glitch. The user prompts are selected and presented in an adaptive manner based on patient specific factors. Selection of prompts and prompt presentation may be responsive to user actions, changes in the patient, and/or the condition of the operational problem. The troubleshooting system checks the condition of the operational problem after presentation of each user prompt and determines a next user prompt accordingly, or ends the session. When the operational problem persists after ending of the session, an external support resource may be contacted.

Abstract: A dynamic controlling framework that is adapted to dynamically optimize existing routing decisions in an optimized link state routing (OLSR) platform to favor higher-bandwidth, lower-latency links in the active link database. The dynamic controlling framework includes a link probing tool that is in communication with OLSR platform where the link probing tool is adapted to dynamically measure estimated bandwidth, latency scores, or both estimated bandwidth and latency scores of active links in the active link database of the OLSR platform. The dynamic controlling framework includes a plug-in framework that is in communication with the OLSR platform and the link probing tool where the plugin framework is adapted to receive at least one link quality multiplier (LQM) metric computed by the link probing tool and to combine the at least one LQM metric with the OLSR platform's existing link quality (LQ) metric for enhancing route selection by the OLSR platform.

Abstract: A medical wearable matching system is provided that pre-assesses a fit of a wearable article of medical device for a patient. The matching system may facilitate fitting and selection of a class of wearable articles for a patient. The matching system may also evaluate potential effects of the fit on operations of the medical device. Patient specific data, such as measurements, are fed into a fit prediction artificial intelligence (“AI”) model. The fitting AI model is trained with fitting data of previous patients to predict a class of wearable medical article that is likely, above a threshold amount, to provide a target fit for the patient. The patient specific information may also be fed into an adverse potential AI model. The adverse potential AI model is trained with patient experience data describing adverse operations of a wearable article worn by prior patients.

Abstract: A system, method, and computer-readable medium are disclosed for intelligent User workload orchestration for virtual meetings. A virtual meeting for multiple users is initiated. Voice data of multiple users is received and converted to text data. Machine learning processes the text data to action items to be performed by specific users (i.e., meeting attendees) in real-time. Minutes of the meeting and action items (in the form of real-time and passive recommendations) are generated and provided. In order to perform the action items, determination is made as to which third party applications support the action items and intelligent user workload orchestration is employed.