Abstract: The system obtains a first representation of a troubleshooting guide associated with a first issue experienced by a first device. The first representation includes a first plurality of nodes and a first plurality of edges. A node indicates a troubleshooting procedure, and an edge indicates a result of the troubleshooting procedure, such as a testing step or repair step. The system determines a subset of the first plurality of edges indicating the repair step and converts the subset of the first plurality of edges into multiple repair nodes to obtain a second representation. A repair node indicates the repair step resolving the first issue. The second representation includes the first plurality of nodes and the multiple repair nodes. Based on the second representation, the system trains an artificial intelligence to provide a most likely repair step to resolve a second issue without requiring performance of the testing step.

Abstract: A seat having a frame and a chair component coupled with the frame. The chair component includes a front side and a rear side such that the front side has a bucket-shaped portion configured to seat a passenger and the rear side is a seatback portion that is rear-facing. The seatback portion further includes a retaining clip configured to receive an electronic device and a lipped surface configured to retain a hanging personal belonging.

Abstract: The system obtains an indication of multiple troubleshooting guides associated with a first issue experienced by a first device and device information describing the first device. The system receives an input including device information describing the second device and an indication of a second issue experienced by the second device, where the second device is different from the first device. Based on the input and the indication of the multiple troubleshooting guides, the system generates a distribution of probabilities associated with the multiple troubleshooting procedures. Based on the distribution of probabilities, the system selects a most likely troubleshooting procedure including a repair step or the testing step associated with the second issue. The system suggests the most likely troubleshooting procedure to perform as a resolution to the second issue experienced by the second device.

Abstract: Systems and methods for a vehicle containing a structural column are disclosed. The vehicle includes: a structural column positioned between a first and second interior surface of the vehicle. The vehicle may further include a seating assembly attached to a first surface of the structural column, wherein the seating assembly comprises a seating surface and a seat back. In addition, the structural column may be configured to redistribute a force load to at least one other structural component in the vehicle. Other aspects are described and claimed.

Abstract: The system receives an input indicating a device and an indication of an issue associated with the device and a natural language input describing a troubleshooting procedure performed to resolve the issue. Based on the input indicating the device, and the indication of the issue associated with the device, the system maps the natural language input into a predetermined troubleshooting procedure associated with the device. The system receives an input indicating a result among the multiple results associated with the predetermined troubleshooting procedure. Based on the result of the predetermined troubleshooting procedure, the system determines a troubleshooting procedure to perform. The troubleshooting procedure includes a repair step associated with the issue or a testing step associated with the issue and is different from the troubleshooting procedure performed to resolve the issue. The system suggests the troubleshooting procedure to an operator associated with the device.

Abstract: The system obtains a first input indicating a machine experiencing an issue and one or more issues with the machine. Based on the first input, the system provides a first plurality of relevance indicators and a first plurality of troubleshooting procedures. The system receives a second input including a performed troubleshooting procedure among the first plurality of troubleshooting procedures. The system receives an indication of a result associated with the performed troubleshooting procedure. Based on the indication of the result associated with the performed troubleshooting procedure, the system generates a second plurality of relevance indicators associated with a second plurality of troubleshooting procedures, where the second plurality of relevance indicators indicates a second troubleshooting procedure to perform next.

Abstract: The system receives an input indicating a device and an indication of an issue associated with the device and a natural language input describing a troubleshooting procedure performed to resolve the issue. Based on the input indicating the device, and the indication of the issue associated with the device, the system maps the natural language input into a predetermined troubleshooting procedure associated with the device. The system receives an input indicating a result among the multiple results associated with the predetermined troubleshooting procedure. Based on the result of the predetermined troubleshooting procedure, the system determines a troubleshooting procedure to perform. The troubleshooting procedure includes a repair step associated with the issue or a testing step associated with the issue and is different from the troubleshooting procedure performed to resolve the issue. The system suggests the troubleshooting procedure to an operator associated with the device.

Abstract: The system obtains an indication of multiple troubleshooting guides associated with a first issue experienced by a first device and device information describing the first device. The system receives an input including device information describing the second device and an indication of a second issue experienced by the second device, where the second device is different from the first device. Based on the input and the indication of the multiple troubleshooting guides, the system generates a distribution of probabilities associated with the multiple troubleshooting procedures. Based on the distribution of probabilities, the system selects a most likely troubleshooting procedure including a repair step or the testing step associated with the second issue. The system suggests the most likely troubleshooting procedure to perform as a resolution to the second issue experienced by the second device.

Abstract: The system obtains a first representation of a troubleshooting guide associated with a first issue experienced by a first device. The first representation includes a first plurality of nodes and a first plurality of edges. A node indicates a troubleshooting procedure, and an edge indicates a result of the troubleshooting procedure, such as a testing step or repair step. The system determines a subset of the first plurality of edges indicating the repair step and converts the subset of the first plurality of edges into multiple repair nodes to obtain a second representation. A repair node indicates the repair step resolving the first issue. The second representation includes the first plurality of nodes and the multiple repair nodes. Based on the second representation, the system trains an artificial intelligence to provide a most likely repair step to resolve a second issue without requiring performance of the testing step.

Abstract: The system obtains an input indicating a machine experiencing an issue and one or more issues with the machine. Based on the input, the system obtains a graph applicable to the one or more issues associated with the machine. The system calculates a first plurality of scores associated with multiple nodes in the graph. Based on the first plurality of scores, the system suggests a first troubleshooting procedure among the multiple troubleshooting procedures to perform. The system receives an indication of a performed troubleshooting procedure and a result associated with the performed troubleshooting procedure. Based on the performed troubleshooting procedure and the result associated with the performed troubleshooting procedure, the system calculates a second plurality of scores associated with the graph and provides an indication of the second plurality of scores, where the indication of the second plurality of scores suggests a second troubleshooting procedure to perform.

Abstract: The system obtains SOH of a first component, where the SOH indicates a difference between a current state of the first component and a state of a rated new component. The system obtains historical use of the first component. Based on the SOH and the historical use, the system determines a future SOH of the first component. The system obtains an indication of a requested SOH of the first component. Based on the indication of the requested SOH and the future SOH of the first component, the system determines a first time indicating when the first component will need to be replaced. The system determines a second time indicating when the second component is available to replace the first component, where the second time occurs before the first time. The system requests a delivery of the second component when the second time is substantially close to the first time.

Abstract: A method for dynamically creating and validating a predictive analytics model to transform data into actionable insights the method comprising: identifying an event, selectively tagging, based on analytics expertise, at least one time series data area (data area), where the identified event occurred, comparing data area where the identified event is tagged with the data area where the identified event is not tagged, building, based on analytics expertise, the predictive analytics model embodying the classification generated by selective tagging, displaying the visual indicia generated by executing the predictive analytics model, and validating the predictive model based on a feedback from at least one domain expert.

Abstract: A seat comprising a seatback, a cushion, a screen, and a track system including a rail and an attachment mechanism, wherein the cushion is secured to the seatback via the track system and the cushion is configured to move along the seatback in a vertical direction.

Abstract: A seat having a frame and a chair component coupled with the frame. The chair component includes a front side and a rear side such that the front side has a bucket-shaped portion configured to seat a passenger and the rear side is a seatback portion that is rear-facing. The seatback portion further includes a retaining clip configured to receive an electronic device and a lipped surface configured to retain a hanging personal belonging.

Abstract: A method for dynamically creating and validating a predictive analytics model to transform data into actionable insights the method comprising: identifying an event, selectively tagging, based on analytics expertise, at least one time series data area (data area), where the identified event occurred, comparing data area where the identified event is tagged with the data area where the identified event is not tagged, building, based on analytics expertise, the predictive analytics model embodying the classification generated by selective tagging, displaying the visual indicia generated by executing the predictive analytics model, and validating the predictive model based on a feedback from at least one domain expert.

Abstract: A method for loading a payload carrier of a machine includes receiving, from a camera on the machine, a two-dimensional image of an interior of the payload carrier as material is loaded into the payload carrier. The method further includes filtering the image to identify a contour of the loaded material and determining an area of the contour. The method further includes controlling a display device indicate the determined area.

Abstract: A method for loading a payload carrier of a machine includes receiving, from a camera on the machine, a two-dimensional image of an interior of the payload carrier as material is loaded into the payload carrier. The method further includes sectioning the two-dimensional image into a plurality of regions, and determining, for individual of the regions, whether the region includes a representative of fill material. The method may also include determining a fill volume based on the regions having fill and those not having fill.

Abstract: A method for loading a payload carrier of a machine includes receiving, from a camera on the machine, a two-dimensional image of an interior of the payload carrier as material is loaded into the payload carrier. The method further includes sectioning the two-dimensional image into a plurality of regions, and determining, for individual of the regions, whether the region includes a representative of fill material. The method may also include determining a fill volume based on the regions having fill and those not having fill.

Abstract: A method for loading a payload carrier of a machine includes receiving, from a camera on the machine, a two-dimensional image of an interior of the payload carrier as material is loaded into the payload carrier. The method further includes filtering the image to identify a contour of the loaded material and determining an area of the contour. The method further includes controlling a display device indicate the determined area.

Abstract: A method for tracking workers at a worksite is disclosed. The method includes acquiring a video of the worksite and detecting a movement of objects in the video. Each object is represented by a plurality of pixels. The method includes identifying an item associated with at least one object by clustering a set of pixels of the plurality of pixels based on a color of the set of pixels. Next, the method includes determining the item as a hard hat based on at least a shape of the set of pixels, and identifying the object as a specific worker based on the color of the set of pixels. The method also includes generating a movement data associated with the specific worker based on a tracking of the hard hat.