Abstract: Adaptive navigation techniques are disclosed that allow navigation systems to learn from a user's personal driving history. As a user drives, models are developed and maintained to learn or otherwise capture the driver's personal driving habits and preferences. Example models include road speed, hazard, favored route, and disfavored route models. Other attributes can be used as well, whether based on the user's personal driving data or driving data aggregated from a number of users. The models can be learned under explicit conditions (e.g., time of day/week, driver ID) and/or under implicit conditions (e.g., weather, drivers urgency, as inferred from sensor data). Thus, models for a plurality of attributes can be learned, as well as one or more models for each attribute under a plurality of conditions. Attributes can be weighted according to user preference. The attribute weights and/or models can be used in selecting a best route for user.

Abstract: Adaptive navigation techniques are disclosed that allow navigation systems to learn from a user's personal driving history. As a user drives, models are developed and maintained to learn or otherwise capture the driver's personal driving habits and preferences. Example models include road speed, hazard, favored route, and disfavored route models. Other attributes can be used as well, whether based on the user's personal driving data or driving data aggregated from a number of users. The models can be learned under explicit conditions (e.g., time of day/week, driver ID) and/or under implicit conditions (e.g., weather, drivers urgency, as inferred from sensor data). Thus, models for a plurality of attributes can be learned, as well as one or more models for each attribute under a plurality of conditions. Attributes can be weighted according to user preference. The attribute weights and/or models can be used in selecting a best route for user.

Abstract: Adaptive navigation techniques are disclosed that allow navigation systems to learn from a user's personal driving history. As a user drives, models are developed and maintained to learn or otherwise capture the driver's personal driving habits and preferences. Example models include road speed, hazard, favored route, and disfavored route models. Other attributes can be used as well, whether based on the user's personal driving data or driving data aggregated from a number of users. The models can be learned under explicit conditions (e.g., time of day/week, driver ID) and/or under implicit conditions (e.g., weather, drivers urgency, as inferred from sensor data). Thus, models for a plurality of attributes can be learned, as well as one or more models for each attribute under a plurality of conditions. Attributes can be weighted according to user preference. The attribute weights and/or models can be used in selecting a best route for user.

Abstract: Adaptive navigation techniques are disclosed that allow navigation systems to learn from a user's personal driving history. As a user drives, models are developed and maintained to learn or otherwise capture the driver's personal driving habits and preferences. Example models include road speed, hazard, favored route, and disfavored route models. Other attributes can be used as well, whether based on the user's personal driving data or driving data aggregated from a number of users. The models can be learned under explicit conditions (e.g., time of day/week, driver ID) and/or under implicit conditions (e.g., weather, drivers urgency, as inferred from sensor data). Thus, models for a plurality of attributes can be learned, as well as one or more models for each attribute under a plurality of conditions. Attributes can be weighted according to user preference. The attribute weights and/or models can be used in selecting a best route for user.

Abstract: Adaptive navigation techniques are disclosed that allow navigation systems to learn from a user's personal driving history. As a user drives, models are developed and maintained to learn or otherwise capture the driver's personal driving habits and preferences. Example models include road speed, hazard, favored route, and disfavored route models. Other attributes can be used as well, whether based on the user's personal driving data or driving data aggregated from a number of users. The models can be learned under explicit conditions (e.g., time of day/week, driver ID) and/or under implicit conditions (e.g., weather, drivers urgency, as inferred from sensor data). Thus, models for a plurality of attributes can be learned, as well as one or more models for each attribute under a plurality of conditions. Attributes can be weighted according to user preference. The attribute weights and/or models can be used in selecting a best route for user.

Abstract: Various methods, systems, and apparatus for implementing aspects of a digital mapping system are disclosed. One such method includes sending a location request from a client-side computing device to a map tile server, receiving a set of map tiles in response to the location request, assembling said received map tiles into a tile grid, aligning the tile grid relative to a clipping shape, and displaying the result as a map image. Direction control or zoom control objects may be included as interactive overlays on the displayed map image. The displayed map image may also include route or location overlays.

Abstract: Adaptive navigation techniques are disclosed that allow navigation systems to learn from a user's personal driving history. As a user drives, models are developed and maintained to learn or otherwise capture the driver's personal driving habits and preferences. Example models include road speed, hazard, favored route, and disfavored route models. Other attributes can be used as well, whether based on the user's personal driving data or driving data aggregated from a number of users. The models can be learned under explicit conditions (e.g., time of day/week, driver ID) and/or under implicit conditions (e.g., weather, drivers urgency, as inferred from sensor data). Thus, models for a plurality of attributes can be learned, as well as one or more models for each attribute under a plurality of conditions. Attributes can be weighted according to user preference. The attribute weights and/or models can be used in selecting a best route for user.

Abstract: Adaptive navigation techniques are disclosed that allow navigation systems to learn from a user's personal driving history. As a user drives, models are developed and maintained to learn or otherwise capture the driver's personal driving habits and preferences. Example models include road speed, hazard, favored route, and disfavored route models. Other attributes can be used as well, whether based on the user's personal driving data or driving data aggregated from a number of users. The models can be learned under explicit conditions (e.g., time of day/week, driver ID) and/or under implicit conditions (e.g., weather, drivers urgency, as inferred from sensor data). Thus, models for a plurality of attributes can be learned, as well as one or more models for each attribute under a plurality of conditions. Attributes can be weighted according to user preference. The attribute weights and/or models can be used in selecting a best route for user.

Abstract: Adaptive navigation techniques are disclosed that allow navigation systems to learn from a user's personal driving history. As a user drives, models are developed and maintained to learn or otherwise capture the driver's personal driving habits and preferences. Example models include road speed, hazard, favored route, and disfavored route models. Other attributes can be used as well, whether based on the user's personal driving data or driving data aggregated from a number of users. The models can be learned under explicit conditions (e.g., time of day/week, driver ID) and/or under implicit conditions (e.g., weather, drivers urgency, as inferred from sensor data). Thus, models for a plurality of attributes can be learned, as well as one or more models for each attribute under a plurality of conditions. Attributes can be weighted according to user preference. The attribute weights and/or models can be used in selecting a best route for user.

Abstract: Adaptive navigation techniques are disclosed that allow navigation systems to learn from a user's personal driving history. As a user drives, models are developed and maintained to learn or otherwise capture the driver's personal driving habits and preferences. Example models include road speed, hazard, favored route, and disfavored route models. Other attributes can be used as well, whether based on the user's personal driving data or driving data aggregated from a number of users. The models can be learned under explicit conditions (e.g., time of day/week, driver ID) and/or under implicit conditions (e.g., weather, drivers urgency, as inferred from sensor data). Thus, models for a plurality of attributes can be learned, as well as one or more models for each attribute under a plurality of conditions. Attributes can be weighted according to user preference. The attribute weights and/or models can be used in selecting a best route for user.

Abstract: A conversation server hosts a conversation having a plurality of participants, the conversation server enables a first client to display at least a portion of a conversation to a first participant. The conversation server receives a notification the portion of the conversation was viewed by the first participant at a first time while in a first state. After receiving a notification that the portion of the conversation has ceased to be viewed by the first participant, the conversation is edited to a second state at a second time. At a third time that is after the second time, the conversation server sends, to the first client, information enabling the first client to display a markup of the portion of the conversation that is indicative of one or more edits that transition the portion of the conversation from the first state to the second state.

Abstract: Adaptive navigation techniques are disclosed that allow navigation systems to learn from a user's personal driving history. As a user drives, models are developed and maintained to learn or otherwise capture the driver's personal driving habits and preferences. Example models include road speed, hazard, favored route, and disfavored route models. Other attributes can be used as well, whether based on the user's personal driving data or driving data aggregated from a number of users. The models can be learned under explicit conditions (e.g., time of day/week, driver ID) and/or under implicit conditions (e.g., weather, drivers urgency, as inferred from sensor data). Thus, models for a plurality of attributes can be learned, as well as one or more models for each attribute under a plurality of conditions. Attributes can be weighted according to user preference. The attribute weights and/or models can be used in selecting a best route for user.

Abstract: A server system hosts a plurality of conversations. For a respective conversation the server system receives a first sequence of edits and a second sequence of edits to a respective content unit of the conversation from different participants of the conversation. The server system converts the first and second sequences of edits into respective first and second merged sequences of edits. The server system responds to a determination that the first merged sequence of edits and the second merged sequence of edits meet predefined conflict criteria by transforming the first and second merged sequences of edits. The server system sends the respective transformed sequences of edits to respective participants so that respective participants can apply either the first sequence of edits followed by the second transformed sequence of edits or applying the second sequence of edits followed by the first transformed sequence of edits to produce a same result.

Abstract: A client system displays a user interface with a display region and a minimized window region. A plurality of windows are displayed in the display region, including a first window and a second window. The client system detects a first input that corresponds to a request to change a state of one or more of the windows; and in response to detecting the first input, the client system ceases to display the first window in the display region, and displays a representation of the first window in the minimized window region. After ceasing to display the first window, the client system detects a second input that corresponds to a request to view content of the first window; and in response to detecting the second input, the client displays an interactive overlay that includes content of the first window and partially overlaps the display region.

Abstract: A computer system displays: a view window including an initial portion of a document, a scroll icon at an initial scroll icon location on the display, and a marker icon that is distinct from the scroll icon. The computer system detects a scrolling input and in response moves the scroll icon to a target scroll icon location on the display wherein the target scroll icon location corresponds to a target portion of the document. In some embodiments, the computer system scrolls the document towards the target portion of the document and concurrently moves the marker icon towards a target marker icon location on the display that is proximate to the target scroll icon location, where the scrolling speed of the document is determined based on the distance between the marker icon and the scroll icon.

Abstract: A method, at a computing device having one or more processors and memory storing programs for execution by the one or more processors, identifies a conversation including a plurality of participants. A sequence of edits to a content unit in a plurality of content units of the conversation is obtained from a first participant in the plurality of participants to produce a revised content unit. While a second participant of the plurality of participants is providing or editing a portion of the content unit, or viewing the portion of the content unit, the following conditions are imposed. When the second participant is a team member, the conversation is updated for the second participant with the revised content unit by providing the revised content unit to the second participant. When the second participant is a non-team member, updating the conversation for the second participant with the revised content unit is delayed.

Abstract: Systems and methods of hosting electronic conversations are provided in which a plurality of conversations, each having an identified set of participants, is maintained. Responsive to a search query from a first participant of a first conversation in the plurality of conversations, a second conversation in the plurality of conversations is identified as relevant to the search query. All or a portion of the second conversation is formatted for display to the first participant. Each conversation in the plurality of conversations, including the second conversation, is an instant messaging conversation. Participants in each conversation in the plurality of conversations are instant messaging participants.

Abstract: A method, at a computing device having one or more processors and memory storing programs for execution by the one or more processors, identifies a conversation including a plurality of participants. A sequence of edits to a content unit in a plurality of content units of the conversation is obtained from a first participant in the plurality of participants to produce a revised content unit. While a second participant of the plurality of participants is providing or editing a portion of the content unit, or viewing the portion of the content unit, the following conditions are imposed. When the second participant is a team member, the conversation is updated for the second participant with the revised content unit by providing the revised content unit to the second participant. When the second participant is a non-team member, updating the conversation for the second participant with the revised content unit is delayed.

Abstract: Systems and methods of hosting electronic conversations are provided in which a plurality of conversations, each having an identified set of participants, is maintained. Responsive to a search query from a first participant of a first conversation in the plurality of conversations, a second conversation in the plurality of conversations is identified as relevant to the search query. All or a portion of the second conversation is formatted for display to the first participant. Each conversation in the plurality of conversations, including the second conversation, is an instant messaging conversation. Participants in each conversation in the plurality of conversations are instant messaging participants.

Abstract: A server system hosts a plurality of conversations. For a respective conversation the server system receives units of content from respective participants in the conversation. The server system receives a sequence of edits to a respective content unit of the conversation from at least one participant other than an initial author of the content unit to produce a revised content unit. The server system stores a respective timestamp for each distinct edit in the sequence of edits to the content unit, including distinct timestamps for at least first and second edits to the content unit. The server system updates the conversation with the revised content unit and automatically provides the updated conversation to the one or more server systems hosting conversations for the participants in the conversation.