Adaptive and personalized navigation system
First Claim
1. A machine-readable storage medium encoded with instructions, that when executed by one or more processors, cause the processor to carry out a process for generating directions for use in navigation during a current driving session, the process comprising:
- storing a plurality of target attributes;
for at least a selection of the stored target attributes, storing a plurality of conditional variant models associated with the selected target attributes;
receiving a route request from a user, including a target destination;
generating a set of candidate routes;
computing a score for each candidate route based on one or more attribute models learned from previous user driving sessions, wherein computing comprises;
probabilistically determining for a target attribute that is one of the selected target attributes which of the plurality of conditional variant models associated with the target attribute currently corresponds to a condition of the target attribute; and
applying the determined conditional variant model to the target attribute; and
providing at least one scored route to the user.
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Accused Products
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.
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Citations
27 Claims
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1. A machine-readable storage medium encoded with instructions, that when executed by one or more processors, cause the processor to carry out a process for generating directions for use in navigation during a current driving session, the process comprising:
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storing a plurality of target attributes; for at least a selection of the stored target attributes, storing a plurality of conditional variant models associated with the selected target attributes; receiving a route request from a user, including a target destination; generating a set of candidate routes; computing a score for each candidate route based on one or more attribute models learned from previous user driving sessions, wherein computing comprises; probabilistically determining for a target attribute that is one of the selected target attributes which of the plurality of conditional variant models associated with the target attribute currently corresponds to a condition of the target attribute; and applying the determined conditional variant model to the target attribute; and providing at least one scored route to the user. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16)
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17. A method for generating directions for use in navigation during a current driving session, comprising:
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storing a plurality of target attributes; for at least a selection of the stored target attributes, storing a plurality of conditional variant models associated with the selected target attributes; receiving a route request from a user, including a target destination; generating a set of candidate routes; computing a score for each candidate route based on one or more attribute models learned from previous user driving sessions, wherein computing comprises; probabilistically determining for a target attribute that is one of the selected target attributes which of the plurality of conditional variant models associated with the target attribute currently corresponds to a condition of the target attribute; and applying the determined conditional variant model to the target attribute; and providing at least one scored route to the user. - View Dependent Claims (18, 19, 20, 21, 22, 23)
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24. A method for generating directions for use in navigation during a current driving session, comprising:
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storing a plurality of target attributes; for at least a selection of the stored target attributes, storing a plurality of conditional variant models associated with the selected target attributes; receiving a route request from a user, including a target destination; generating a set of candidate routes; assigning an attribute weight to each target attribute of each candidate route, based on attribute preferences of the user; computing a score for each candidate route based on the attribute weights and one or more attribute models learned from previous user driving sessions, wherein at least one of the one or more attribute models provides a summary statistic of attribute values that have been observed during previous driving sessions, and wherein the computing comprises; probabilistically determining for a target attribute that is one of the selected target attributes which of the plurality of conditional variant models associated with the target attribute currently corresponds to a condition of the target attribute; and applying the determined conditional variant model to the target attribute; and providing at least one scored route to the user. - View Dependent Claims (25, 26, 27)
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Specification