Abstract: Generating route guidance in near real-time. At least one example is a method of providing route guidance by way of a mobile device, the method comprising: acquiring an applet by the mobile device, the acquiring responsive to a trigger event, and the trigger event identifies a destination location; determining, by the applet executed on the mobile device, an initial location of the mobile device; sending, by the applet executed on the mobile device, to a server an indication of the initial location of the mobile device and an indication of the destination location; receiving, by the mobile device from the server, a map comprising the initial location and the destination location; determining, by the applet executed on the mobile device, a route from the initial location to the destination location; and providing, by the applet executed on mobile device, guidance along the route to the destination location.

Abstract: Automating conversion of drawings to indoor maps and plans. One example is a computer-implemented method of machine learning, the method comprising: receiving, by a device, a first set of updates to a first indoor map, the first indoor map previously created by a production machine-learning system having a production map accuracy; training, by a device, a supporting machine-learning system using the first set of updates to the first indoor map; and then applying, by a device, test data to the supporting machine-learning system, the applying results in first-evaluation indoor map with a first-evaluation map accuracy; and when the first-evaluation map accuracy is within a predetermined window above the production map accuracy refraining, by a device, from updating the production machine-learning system based on the first set of updates.

Abstract: Automating conversion of drawings to indoor maps and plans. One example is a computer-implemented method of determining a geo-location, the method comprising: determining a floor-level outline of a floor depicted in a CAD drawing; receiving an approximate geo-location of a building to which the CAD drawing applies; obtaining an overhead image of a target area encompassing the approximate geo-location, the overhead image comprising a plurality of buildings within the target area; identifying a plurality of building footprints within the target area; calculating, by a device, a plurality of distance functions that relate the floor-level outline to the each of the plurality of building footprints, the calculating creates a plurality of similarity scores; selecting a building footprint from plurality of building footprints, the selecting based on the plurality of similarity scores; and calculating a final geo-location of the building corresponding to the building footprint.

Abstract: Automating conversion of drawings to indoor maps and plans. One example is a computer-implemented method of creating an indoor map from a CAD drawing, the method comprising: preprocessing an original CAD drawing to create a modified CAD drawing, a text database containing text from the original CAD drawing, a CAD vector-image of the modified CAD drawing, and a CAD raster-image of the modified CAD drawing; creating a floor-level outline; sensing furniture depicted on the floor, the sensing creates set of furniture entities; identifying a room depicted in the CAD drawing; and creating the indoor map for a floor using the floor-level outline, the room-level outline and the room identity.

Abstract: Automating conversion of drawings to indoor maps and plans. One example is a computer-implemented comprising: preprocessing an original CAD drawing to create a modified CAD drawing, a text database containing text from the original CAD drawing, a CAD vector-image of the modified CAD drawing, and a CAD raster-image of the modified CAD drawing; determining a floor depicted in the CAD drawing, the determining results in a floor-level bounding line; sensing furniture depicted on the floor by applying the floor-level bounding line, the CAD vector-image, and the text database to machine-learning algorithms, the sensing results in a plurality of furniture entities and associated location information; identifying each room depicted in the CAD drawing, the identifying results in a plurality of room outlines; and creating an indoor map for the floor by combining the plurality of furniture entities and associated location information with the plurality of room outlines.

Abstract: Automating conversion of drawings to indoor maps and plans. One example is a computer-implemented comprising: preprocessing an original CAD drawing to create a modified CAD drawing, a text database containing text from the original CAD drawing, a CAD vector-image of the modified CAD drawing, and a CAD raster-image of the modified CAD drawing; determining a floor depicted in the CAD drawing, the determining results in a floor-level bounding line; sensing furniture depicted on the floor by applying the floor-level bounding line, the CAD vector-image, and the text database to machine-learning algorithms, the sensing results in a plurality of furniture entities and associated location information; identifying each room depicted in the CAD drawing, the identifying results in a plurality of room outlines; and creating an indoor map for the floor by combining the plurality of furniture entities and associated location information with the plurality of room outlines.

Abstract: Automating conversion of drawings to indoor maps and plans. One example is a computer-implemented method comprising: preprocessing a CAD drawing to create a text database containing text from the CAD drawing and associations of the text with locations within the CAD drawing; determining a floor depicted in the CAD drawing, the determining results in a floor-level outline; identifying a plurality of room-level outlines within the floor-level outline, the plurality of room-level outlines corresponds to a respective plurality of rooms; selecting a name of a first room from the plurality of rooms, the selecting based on text within the text database; and creating an indoor map including the name of the first room, the name of the first room associated with a location of the first room within the floor-level outline.

Abstract: Automating conversion of drawings to indoor maps and plans. One example is a computer-implemented method of machine learning, the method comprising: receiving, by a device, a first set of updates to a first indoor map, the first indoor map previously created by a production machine-learning system having a production map accuracy; training, by a device, a supporting machine-learning system using the first set of updates to the first indoor map; and then applying, by a device, test data to the supporting machine-learning system, the applying results in first-evaluation indoor map with a first-evaluation map accuracy; and when the first-evaluation map accuracy is within a predetermined window above the production map accuracy refraining, by a device, from updating the production machine-learning system based on the first set of updates.

Abstract: Automating conversion of drawings to indoor maps and plans. One example is a computer-implemented method of determining a geo-location, the method comprising: determining a floor-level outline of a floor depicted in a CAD drawing; receiving an approximate geo-location of a building to which the CAD drawing applies; obtaining an overhead image of a target area encompassing the approximate geo-location, the overhead image comprising a plurality of buildings within the target area; identifying a plurality of building footprints within the target area; calculating, by a device, a plurality of distance functions that relate the floor-level outline to the each of the plurality of building footprints, the calculating creates a plurality of similarity scores; selecting a building footprint from plurality of building footprints, the selecting based on the plurality of similarity scores; and calculating a final geo-location of the building corresponding to the building footprint.

Abstract: Automating conversion of drawings to indoor maps and plans. One example is a computer-implemented comprising: preprocessing an original CAD drawing to create a modified CAD drawing, a text database containing text from the original CAD drawing, a CAD vector-image of the modified CAD drawing, and a CAD raster-image of the modified CAD drawing; determining a floor depicted in the CAD drawing, the determining results in a floor-level bounding line; sensing furniture depicted on the floor by applying the floor-level bounding line, the CAD vector-image, and the text database to machine-learning algorithms, the sensing results in a plurality of furniture entities and associated location information; identifying each room depicted in the CAD drawing, the identifying results in a plurality of room outlines; and creating an indoor map for the floor by combining the plurality of furniture entities and associated location information with the plurality of room outlines.

Abstract: Automating conversion of drawings to indoor maps and plans. One example is a computer-implemented method of creating an indoor map from a CAD drawing, the method comprising: preprocessing an original CAD drawing to create a modified CAD drawing, a text database containing text from the original CAD drawing, a CAD vector-image of the modified CAD drawing, and a CAD raster-image of the modified CAD drawing; creating a floor-level outline; sensing furniture depicted on the floor, the sensing creates set of furniture entities; identifying a room depicted in the CAD drawing; and creating the indoor map for a floor using the floor-level outline, the room-level outline and the room identity.

Abstract: Automating conversion of drawings to indoor maps and plans. One example is a computer-implemented method of creating an indoor map from a CAD drawing, the method comprising: preprocessing an original CAD drawing to create a modified CAD drawing, a text database containing text from the original CAD drawing, a CAD vector-image of the modified CAD drawing, and a CAD raster-image of the modified CAD drawing; creating a floor-level outline; sensing furniture depicted on the floor, the sensing creates set of furniture entities; identifying a room depicted in the CAD drawing; and creating the indoor map for a floor using the floor-level outline, the room-level outline and the room identity.

Abstract: A method of authentication in ride hailing situations may include directing, by a first computing device of a passenger, the passenger to point a camera of the first computing device toward a location of a second computing device of a driver located in a vehicle, receiving data from the camera, wherein the data comprises streaming image frames including vehicle information pertaining to a vehicle included in the streaming image frames, analyzing the data to obtain the vehicle information, and determining whether the vehicle in the streaming image frames corresponds to an expected vehicle for the driver, the determination based on the vehicle information and expected vehicle information.

Abstract: Automating conversion of drawings to indoor maps and plans. One example is a computer-implemented method comprising: preprocessing an original CAD drawing and thereby creating a modified CAD drawing, a text database containing text from the modified CAD drawing, a CAD vector-image of the modified CAD drawing, and a CAD raster-image of the modified CAD drawing; creating a floor-level bounding line that encircles a floor depicted in the modified CAD drawing, the creating by applying the CAD raster-image, the CAD vector-image, and the text database to a floor-level machine-learning algorithm; applying an active contour model to an initial floor-level segmentation created from the floor-level bounding line to create an intermediate floor outline that delineates the floor; removing drawing-entities from the modified CAD drawing that are a predetermine distance away from the intermediate floor outline to create a final floor outline; and creating an indoor map using the final floor outline.

Abstract: Automating conversion of drawings to indoor maps and plans. One example is a computer-implemented method comprising: preprocessing an original CAD drawing to create a modified CAD drawing, a text database, a CAD vector-image, and a CAD raster-image; determining a floor depicted in the CAD drawing by applying the CAD raster-image, the CAD vector-image, and the text database to a floor-level machine-learning algorithm resulting in a floor-level outline; sensing furniture depicted on the floor by applying the floor-level outline, the CAD vector-image, and the text database to a furniture-level machine-learning algorithm resulting in a set of furniture entities; identifying each room depicted in the CAD drawing by applying the floor-level outline, the furniture entities, CAD vector-image, and the text database to room-level machine-learning algorithm resulting in a plurality of room-level outlines; and creating an indoor map by combining the furniture entities and the plurality of room-level outlines.

Abstract: Reducing the spread of contagions. At least one example embodiment is a computer-implemented method including: creating, by a server system, an indication of suspended micro-droplets of spittle in an area of a building; receiving, by the server system, an indication that a mobile device is moving toward the area of the building; and sending, by the server system, an alert to the mobile device, the alert indicating that entry into the area of the building is not safe based on the indication of suspended micro-droplets of spittle.

Abstract: A method of authentication in ride hailing situations may include transmitting, to a server by a first device of a passenger, a request for a ride to a destination, receiving an indication that a second device of a driver accepted the ride, and communicating first location data of each respective device to the other respective device. The method may also include determining a first distance between the first and second device based on the first location data of the first and second device. Responsive to determining that the first distance satisfies a threshold condition, the method may also include communicating, via a first point-to-point communication between the first and second device, second location data of each respective device to the other respective device. Further, the method may include presenting, based on the second location data, directions for the first and second device to reach each other.

Abstract: A method of authentication in ride hailing situations may include transmitting, to a server by a first device of a passenger, a request for a ride to a destination, receiving an indication that a second device of a driver accepted the ride, and communicating first location data of each respective device to the other respective device. The method may also include determining a first distance between the first and second device based on the first location data of the first and second device. Responsive to determining that the first distance satisfies a threshold condition, the method may also include communicating, via a first point-to-point communication between the first and second device, second location data of each respective device to the other respective device. Further, the method may include presenting, based on the second location data, directions for the first and second device to reach each other.

Abstract: Direction estimation in indoor and outdoor locations. At least some of the example embodiments are computer-implemented methods including: determining, by a processor of a mobile device, that the mobile device is approaching a portal between a first navigation domain and a second navigation domain; displaying, on a display device of the mobile device, a merged map concatenating a map of the first navigation domain with a map of the second navigation domain; showing location of the mobile device on the map of the first navigation domain prior to the mobile device transiting through the portal; and then showing location of the mobile device on the map of the second navigation domain.

Abstract: Estimating initial heading at start-up of navigation. At least some of the example embodiments are computer-implemented methods including: spawning a plurality of clone processes, each clone process given an identical location and speed, and each clone process given a unique direction; calculating, by each clone process, a respective position at the end of a frame period; terminating clone processes whose position at the end of the frame period is outside a predetermined threshold, the terminating results in remaining clone processes; and determining the heading of the mobile device from the remaining clone processes.