Abstract: A method or system is capable of detecting operator behavior (“OB”) utilizing a virtuous cycle containing sensors, machine learning center (“MLC”), and cloud based network (“CBN”). In one aspect, the process monitors operator body language captured by interior sensors and captures surrounding information observed by exterior sensors onboard a vehicle as the vehicle is in motion. After selectively recording the captured data in accordance with an OB model generated by MLC, an abnormal OB (“AOB”) is detected in accordance with vehicular status signals received by the OB model. Upon rewinding recorded operator body language and the surrounding information leading up to detection of AOB, labeled data associated with AOB is generated. The labeled data is subsequently uploaded to CBN for facilitating OB model training at MLC via a virtuous cycle.

Abstract: A method or system is capable of detecting operator behavior (“OB”) utilizing a virtuous cycle containing sensors, machine learning center (“MLC”), and cloud based network (“CBN”). In one aspect, the process monitors operator body language captured by interior sensors and captures surrounding information observed by exterior sensors onboard a vehicle as the vehicle is in motion. After selectively recording the captured data in accordance with an OB model generated by MLC, an abnormal OB (“AOB”) is detected in accordance with vehicular status signals received by the OB model. Upon rewinding recorded operator body language and the surrounding information leading up to detection of AOB, labeled data associated with AOB is generated. The labeled data is subsequently uploaded to CBN for facilitating OB model training at MLC via a virtuous cycle.

Abstract: A method or system capable of managing automobile parking space (“APS”) using containerized sensors, machine learning center, and cloud based network is disclosed. A process, in one aspect, monitors the surrounding information observed by a set of onboard sensors of a vehicle as the vehicle is in motion. After selectively recording the surrounding information in accordance with instructions from a containerized APS model which is received from a machine learning center, an APS and APS surrounding information are detected when the vehicle is in a parked condition. Upon rewinding recorded surrounding information leading up to the detection of APS, labeled data associated with APS is generated based on APS and the recorded surrounding information. The process subsequently uploads the labeled data to the cloud based network for facilitating APS model training at the machine learning center via a virtuous cycle.

Abstract: A method or system is capable of detecting operator behavior (“OB”) utilizing a virtuous cycle containing sensors, machine learning center (“MLC”), and cloud based network (“CBN”). In one aspect, the process monitors operator body language captured by interior sensors and captures surrounding information observed by exterior sensors onboard a vehicle as the vehicle is in motion. After selectively recording the captured data in accordance with an OB model generated by MLC, an abnormal OB (“AOB”) is detected in accordance with vehicular status signals received by the OB model. Upon rewinding recorded operator body language and the surrounding information leading up to detection of AOB, labeled data associated with AOB is generated. The labeled data is subsequently uploaded to CBN for facilitating OB model training at MLC via a virtuous cycle.

Abstract: A method or system is capable of detecting operator behavior (“OB”) utilizing a virtuous cycle containing sensors, machine learning center (“MLC”), and cloud based network (“CBN”). In one aspect, the process monitors operator body language captured by interior sensors and captures surrounding information observed by exterior sensors onboard a vehicle as the vehicle is in motion. After selectively recording the captured data in accordance with an OB model generated by MLC, an abnormal OB (“AOB”) is detected in accordance with vehicular status signals received by the OB model. Upon rewinding recorded operator body language and the surrounding information leading up to detection of AOB, labeled data associated with AOB is generated. The labeled data is subsequently uploaded to CBN for facilitating OB model training at MLC via a virtuous cycle.

Abstract: A method or system is capable of detecting operator behavior (“OB”) utilizing a virtuous cycle containing sensors, machine learning center (“MLC”), and cloud based network (“CBN”). In one aspect, the process monitors operator body language captured by interior sensors and captures surrounding information observed by exterior sensors onboard a vehicle as the vehicle is in motion. After selectively recording the captured data in accordance with an OB model generated by MLC, an abnormal OB (“AOB”) is detected in accordance with vehicular status signals received by the OB model. Upon rewinding recorded operator body language and the surrounding information leading up to detection of AOB, labeled data associated with AOB is generated. The labeled data is subsequently uploaded to CBN for facilitating OB model training at MLC via a virtuous cycle.

Abstract: A method or system capable of managing automobile parking space (“APS”) using containerized sensors, machine learning center, and cloud based network is disclosed. A process, in one aspect, monitors the surrounding information observed by a set of onboard sensors of a vehicle as the vehicle is in motion. After selectively recording the surrounding information in accordance with instructions from a containerized APS model which is received from a machine learning center, an APS and APS surrounding information are detected when the vehicle is in a parked condition. Upon rewinding recorded surrounding information leading up to the detection of APS, labeled data associated with APS is generated based on APS and the recorded surrounding information. The process subsequently uploads the labeled data to the cloud based network for facilitating APS model training at the machine learning center via a virtuous cycle.

Abstract: A desk including a book holder positioned at a predetermined angle relative to the surface of the desk. In one embodiment the book holder is deployable, and the desk surface includes a recess into which the deployable book holder fits when the deployable book holder is in the closed position. The recess optionally includes a rubber surface to avoid slipping of the bottom edge of a book (such as a text book) when the book is being held up with the deployable book holder. In another embodiment a deployable book holder includes an aperture, and a recess includes an island. In various embodiments, a proximal end of a desk surface, closest to where a user sits, may be wider than the distal end to provide adequate arm room for the user.

Abstract: A desk including a book holder positioned at a predetermined angle relative to the surface of the desk. In one embodiment the book holder is deployable, and the desk surface includes a recess into which the deployable book holder fits when the deployable book holder is in the closed position. The recess optionally includes a rubber surface to avoid slipping of the bottom edge of a book (such as a text book) when the book is being held up with the deployable book holder. In another embodiment a deployable book holder includes an aperture, and a recess includes an island. In various embodiments, a proximal end of a desk surface, closest to where a user sits, may be wider than the distal end to provide adequate arm room for the user.