Honda Motor Co., Ltd.

Nuance Communications, Inc.

Google Inc.

Deepak Ramachandran

Rakesh Gupta

Antoine Roland Raux

Adwait Ratnaparkhi

William Jarrold

Benjamin Douglas

Peter Frederick Patel-Schneider

Peter Yeh

Tania Bedrax-Weiss

Sarvjeet Singh

Peter Zei-Chan Yeh

Igor V Karpov

Yi Ma

Adam C Vogel

Neville Mehta

Stefan Krawczyk

Matthew Hoffman

Techniques are disclosed that enable the generation of candidate endorsements for recommended items of content using an ensemble of nominators. Various implementations include each nominator in the ensemble providing a candidate endorsement for each recommended item of content. Additionally or alternatively, an endorsement is selected to present to the user based on a score determined for each candidate endorsement.

Generating content endorsements using machine learning nominator(s)

In some natural language understanding (NLU) applications, results may not be tailored to the user's query. In an embodiment of the present invention, a method includes tagging elements of automated speech recognition (ASR) data based on an ontology stored in a memory. The method further includes indexing tagged elements to an entity of the ontology. The method further includes generating a logical form of the ASR data based on the tagged elements and the indexed entities. The method further includes mapping the logical form to a query to a respective corresponding database stored in the memory. The method further includes issuing the query to the respective corresponding databases. The method further includes presenting results of the query to the user via a display or a voice response system.

System and method for natural language driven search and discovery in large data sources

Presenting natural-language-understanding (NLU) results can include redundancies and awkward sentence structures. In an embodiment of the present invention, a method includes, responsive to receiving a result to a NLU query, loading a matching template of a plurality of templates stored in a memory. Each template has mask fields associated with at least one property. The method compares the properties of the mask fields of each of the templates to properties of the query and properties of the result, and selects the matching template. The method further completes the matching template by inserting fields of the result into corresponding mask fields of the matching template. The method may further suppress certain mask fields of the matching template to increase brevity and improve the naturalness of the response when appropriate based on the results of the NLU query. The method further presents the completed matching template to a user via a display.

A method is provided for representing and updating the state of a dialog involving a series of queries and commands to an artificial intelligence system. Each statement within the dialogue may be modeled as a relational tree spanning nodes corresponding to named entities within the statement. A data structure may be used to store each of these trees and to modify them as the dialog progresses. A subsequent statement in the dialog may be parsed and its contents used to update an ongoing search initiated within that dialog. Statements may be used for the update process despite being fragmentary or not corresponding to any predetermined grammar. An algorithm is disclosed for updating the trees within the data structure after a new statement is parsed.

State tracking over machine-learned relational trees in a dialog system

One or more embodiments of techniques or systems for modeling familiarity for a traveler are provided herein. Familiarity evidence can be received, indicative of how familiar a traveler is with an area or road segment, and based on a number of visits the traveler has made to that area. The familiarity evidence can be used to generate one or more familiarity models indicative of a predicted familiarity of locations around the area. Familiarity models can be based on kernels, graph distances, Markov random fields (MRFs), etc. When route directions are generated from an origin location to a destination location, one or more of the directions can be provided based on one or more of the familiarity models. For example, if a familiarity model indicates that a traveler is familiar with a route, driving directions of the route can be adapted to be more succinct.

Familiarity modeling

An utterance is received from a user specifying a location attribute and a landmark. A set of candidate locations is identified based on the specified location attribute, and a confidence score can be determined for each candidate location. A set of landmarks is identified based on the specified landmark, and confidence scores can be determined for the landmarks. An associated kernel model is generated for each landmark. Each kernel model is centered at the location of the associated landmark on a map, and the amplitude of the kernel model can be based on landmark attributes, landmark confidence scores, characteristics of the user, and the like. The candidate locations are ranked based on the amplitudes of overlapping kernel models at the candidate locations, and can also be ranked based on confidence scores associated with the candidate locations. A candidate location is selected and presented to the user based on the candidate location ranking.

Landmark-based location belief tracking for voice-controlled navigation system

A powertrain of a hybrid electric vehicle (HEV) is controlled. A first value &#x3b1;and a second value &#x3b1;are determined. &#x3b1;represents a proportion of an instantaneous power requirement (P) supplied by an engine of the HEV. &#x3b1;controls a recharging rate of a battery of the HEV. A determination is performed, based on &#x3b1;and &#x3b1;, regarding how much engine power to use (P) and how much battery power to use (P). Pand Pare sent to the powertrain.

Hybrid vehicle fuel efficiency using inverse reinforcement learning

An action is performed in a spoken dialog system in response to a user's spoken utterance. A policy which maps belief states of user intent to actions is retrieved or created. A belief state is determined based on the spoken utterance, and an action is selected based on the determined belief state and the policy. The action is performed, and in one embodiment, involves requesting clarification of the spoken utterance from the user. Creating a policy may involve simulating user inputs and spoken dialog system interactions, and modifying policy parameters iteratively until a policy threshold is satisfied. In one embodiment, a belief state is determined by converting the spoken utterance into text, assigning the text to one or more dialog slots associated with nodes in a probabilistic ontology tree (POT), and determining a joint probability based on probability distribution tables in the POT and on the dialog slot assignments.

Belief tracking and action selection in spoken dialog systems

The present invention provides a method for learning a policy used by a computing system to perform a task, such delivery of one or more objects by the computing system. During a first time interval, the computing system determines a first state, a first action and a first reward value. As the computing system determines different states, actions and reward values during subsequent time intervals, a state description identifying the current sate, the current action, the current reward and a predicted action is stored. Responsive to a variance of a stored state description falling below a threshold value, the stored state description is used to modify one or more weights in the policy associated with the first state.

Smoothed sarsa: reinforcement learning for robot delivery tasks

The present invention provides a method for tracking entities, such as people, in an environment over long time periods. A region-based model is generated to model beliefs about entity locations. Each region corresponds to a discrete area representing a location where an entity is likely to be found. Each region includes one or more positions which more precisely specify the location of an entity within the region so that the region defines a probability distribution of the entity residing at different positions within the region. A region-based particle filtering method is applied to entities within the regions so that the probability distribution of each region is updated to indicate the likelihood of the entity residing in a particular region as the entity moves.

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Mountain View, CA, United States of America

Deepak Ramachandran