Abstract: An approach is provided for providing a navigation route for a requesting entity to meet other entities at flexible meeting locations. The approach, for example, involves receiving a request to generate a navigation route to visit a plurality of entities, wherein one or more mobile entities of the plurality of entities are capable of moving from a current location during the navigation route. The approach also involves computing respective isolines around each mobile entity based on a travel distance, a travel time, or a combination thereof that each mobile entity is ready to travel from the current location of each mobile entity during the navigation route. The approach further involves connecting the respective isolines around each mobile entity to determine the navigation route. The approach further involves providing the navigation route as an output.

Abstract: An approach is provided for extracting point-of-interest features based on depth sensor data captured by mobile devices. The approach involves, for instance, receiving a Light Detection and Ranging (LiDAR) scan of a location captured using a LiDAR sensor of a portable device. The approach also involves determining a context of the location, a point of interest (POI) associated with the location, or a combination thereof. The approach further involves selecting a feature recognition parameter based on the context. The feature recognition parameter performs a feature detection analysis of a scenery depicted in the scan. The approach further involves initiating the feature detection analysis of the scan based on the feature recognition parameter to identify a feature, an attribute of the feature, or a combination thereof in the scenery.

Abstract: Embodiments described herein relate to predicting the utilization of electric vehicle (EV) charge points. Methods may include: receiving an indication of a plurality of candidate locations for EV charge points; determining static map features of the plurality of candidate locations; inputting the plurality of candidate locations and static map features into a machine learning model, where the machine learning model is trained on existing EV charge point locations, existing EV charge point static map features, and existing EV charge point utilization; determining, based on the machine learning model, a predicted utilization of an EV charge point at the plurality of candidate locations; and generating a representation of a map including the plurality of candidate locations, where candidate locations of the plurality of candidate locations are visually distinguished based on a respective predicted utilization of an EV charge point at the candidate locations.

Abstract: Embodiments described herein relate to predicting the utilization of electric vehicle (EV) charge points. Methods may include: receiving an indication of a plurality of candidate locations for EV charge points; determining static map features of the plurality of candidate locations; inputting the plurality of candidate locations and static map features into a machine learning model, where the machine learning model is trained on existing EV charge point locations, existing EV charge point static map features, and existing EV charge point utilization; determining, based on the machine learning model, a predicted utilization of an EV charge point at the plurality of candidate locations; and generating a representation of a map including the plurality of candidate locations, where candidate locations of the plurality of candidate locations are visually distinguished based on a respective predicted utilization of an EV charge point at the candidate locations.

Abstract: A circular user interface (UI) that permits a user to select options and input subjective data (e.g., values and arrays) with a circular gesture-based interaction style in an easy, efficient and fast way. The UI is useful on circular devices that provide a display of, e.g., health and behavior measurements; it uses the circular shape of the device to guide the user and shows guiding animating elements on the display.

Abstract: Data about a person's activity and about the person's emotional state during the activity is logged in a history log. The history log may serve as a diary to this person. The history log of one or more persons may serve as a reference-profile for generating recommendations.

Abstract: Mobile electronic communication devices upload input data to a server via a data network. The data uploaded per device is indicative of a physiological or emotional state of the device's user, and of the user's geographical location. The server generates output data per individual user under combined control of the emotional state and geographic location of the individual user. The output data associated with multiple users is rendered as graphical symbols at one or more display monitors, thus being indicative of the geographical distribution of emotions of the population of users.