Abstract: A system for predicting variability of travel time for a trip at a particular time may utilize a machine learning model including latent variables that are associated with the trip. The machine learning model may be trained from historical trip data that is based on location-based measurements reported from mobile devices. Once trained, the machine learning model may be utilized for predicting variability of travel time. A process may include receiving an origin, a destination, and a start time associated with a trip, obtaining candidate routes that run from the origin to the destination, and predicting, based at least in part on the machine learning model, a probability distribution of travel time for individual ones of the candidate routes. One or more routes may be recommended based on the predicted probability distribution, and a measure of travel time for the recommended route(s) may be provided.

Abstract: A system for predicting variability of travel time for a trip at a particular time may utilize a machine learning model including latent variables that are associated with the trip. The machine learning model may be trained from historical trip data that is based on location-based measurements reported from mobile devices. Once trained, the machine learning model may be utilized for predicting variability of travel time. A process may include receiving an origin, a destination, and a start time associated with a trip, obtaining candidate routes that run from the origin to the destination, and predicting, based at least in part on the machine learning model, a probability distribution of travel time for individual ones of the candidate routes. One or more routes may be recommended based on the predicted probability distribution, and a measure of travel time for the recommended route(s) may be provided.

Abstract: Methods are described for ideally joining human and machine computing resources to solve tasks, based on the construction of predictive models from case libraries of data about the abilities of people and machines and their collaboration. Predictive models include methods for folding together human contributions, such as voting, with machine computation, such as automated visual analyses, as well as the routing of tasks to people based on prior performance and interests. An optimal distribution of tasks to selected participants of the plurality of participants is determined according to a model that considers the demonstrated competencies of people based on a value of information analysis that considers the value of human computation and the ideal people for providing a contribution.

Abstract: Methods are described for ideally joining human and machine computing resources to solve tasks, based on the construction of predictive models from case libraries of data about the abilities of people and machines and their collaboration. Predictive models include methods for folding together human contributions, such as voting, with machine computation, such as automated visual analyses, as well as the routing of tasks to people based on prior performance and interests. An optimal distribution of tasks to selected participants of the plurality of participants is determined according to a model that considers the demonstrated competencies of people based on a value of information analysis that considers the value of human computation and the ideal people for providing a contribution.

Abstract: Sensing, learning, inference, and route analysis methods are described that center on the development and use of models that predict road speeds. In use, the system includes a receiver component that receives a traffic system representation, the traffic system representation includes velocities for a plurality of road segments over different contexts. A predictive component analyzes the traffic system representation and automatically assigns velocities to road segments within the traffic system representation, thereby providing more realistic velocities for different contexts where only statistics and/or posted speed limits were available before.

Abstract: Learning, inference, and decision making with probabilistic user models, including considerations of preferences about outcomes under uncertainty, may be infeasible on portable devices. The subject invention provides systems and methods for pre-computing and storing policies based on offline preference assessment, learning, and reasoning about ideal actions and interactions, given a consideration of uncertainties, preferences, and/or future states of the world. Actions include ideal real-time inquiries about a state, using pre-computed value-of-information analyses. In one specific example, such pre-computation can be applied to automatically generate and distribute call-handling policies for cell phones. The methods can employ learning of Bayesian network user models for predicting whether users will attend meetings on their calendar and the cost of being interrupted by incoming calls should a meeting be attended.

Abstract: The present invention relates to a system and methodology to facilitate collaboration and communications between entities such as between parties to a communication, automated applications and components, and/or combinations thereof. The systems and methods of the present invention include a service that supports collaboration and communication by learning predictive models that provide forecasts of one or more aspects of a user's presence and availability. Presence forecasts include a user's current location or future locations at different levels of location precision and of the availability to users of different devices or applications. Availability assessments include inferences about the cost of interrupting a user in different ways and a user's current or future access to one or more communication channels that may be supported by one or more devices with appropriate capabilities.

Abstract: Learning, inference, and decision making with probabilistic user models, including considerations of preferences about outcomes under uncertainty, may be infeasible on portable devices. The subject invention provides systems and methods for pre-computing and storing policies based on offline preference assessment, learning, and reasoning about ideal actions and interactions, given a consideration of uncertainties, preferences, and/or future states of the world. Actions include ideal real-time inquiries about a state, using pre-computed value-of-information analyses. In one specific example, such pre-computation can be applied to automatically generate and distribute call-handling policies for cell phones. The methods can employ learning of Bayesian network user models for predicting whether users will attend meetings on their calendar and the cost of being interrupted by incoming calls should a meeting be attended.

Abstract: Learning, inference, and decision making with probabilistic user models, including considerations of preferences about outcomes under uncertainty, may be infeasible on portable devices. The subject invention provides systems and methods for pre-computing and storing policies based on offline preference assessment, learning, and reasoning about ideal actions and interactions, given a consideration of uncertainties, preferences, and/or future states of the world. Actions include ideal real-time inquiries about a state, using pre-computed value-of-information analyses. In one specific example, such pre-computation can be applied to automatically generate and distribute call-handling policies for cell phones. The methods can employ learning of Bayesian network user models for predicting whether users will attend meetings on their calendar and the cost of being interrupted by incoming calls should a meeting be attended.

Abstract: An electronic device (100) provides tactile feedback provided by a low cost, thin piezoelectric actuator (142) giving tactile feedback emulating a click like feed. The electronic device (100) comprises a chassis plate (122) having a periphery secured to a housing (102, 104) and comprising a flexible material having a first planer side (123), and a second planer side (125) opposed to the first planer side (123). An input device (110) has a planer side (111) positioned adjacent to and in contact with to the first planer side (123) of the chassis (122) and extends through an opening (108) in the housing (102, 104). One or more piezoelectric actuators (142) are secured to the second planer side (125) and within the periphery of the chassis plate (122). Electronic circuitry (208) positioned within the housing (102, 104) drives the piezoelectric actuators (142) in response to the input device (110) being actuated. The input provided to the input device (110) is sensed by the electronic circuitry (208).

Abstract: A system and methodology is provided for improving directory operations within a system providing an electronic hierarchical directory of items. The system includes a component which analyzes probabilities and utilities associated with determining potential target directories for storing and accessing data, and a component for building a subset of the potential target directories that are predicted to be the target directory. The probabilities and/or utilities are functions of expected navigation costs associated with traversing from a displayed directory to at least one of the potential target directories. Methods in accordance with the present invention can be coupled with displays of substructures that format the substructures into a coherent hierarchical view.

Abstract: Sensing, learning, inference, and route analysis methods are described that center on the development and use of models that predict road speeds. In use, the system includes a receiver component that receives a traffic system representation, the traffic system representation includes velocities for a plurality of road segments over different contexts. A predictive component analyzes the traffic system representation and automatically assigns velocities to road segments within the traffic system representation, thereby providing more realistic velocities for different contexts where only statistics and/or posted speed limits were available before.

Abstract: Methods and architectures for context-sensitive reminding and service facilitating are disclosed. The architectures monitor user context and activity, senses or infers relevant reminders, goals, such as those that come from a growing need of the user that should be fulfilled, and computes best reminders, and recommend plans on fulfilling need(s) in an optimum way. Statistical models of a user's knowledge and recall in different settings may be employed. Facilities, services, and merchants can be identified along a route that the user can take, and cost-benefit analysis is performed for determining which merchant(s) to select to fulfill the need(s). Routes may be created as opportunistic modifications of trips underway. Merchants can respond back with offers of sale to the user for all available needed items, and the user can respond with acceptance or denial of the offers. Merchants can also respond in a bidding fashion in order to gain user's patronage.

Abstract: The present invention relates to a system and methodology to facilitate collaboration and communications between entities such as between parties to a communication, automated applications and components, and/or combinations thereof. The systems and methods of the present invention include a service that supports collaboration and communication by learning predictive models that provide forecasts of one or more aspects of a user's presence and availability. Presence forecasts include a user's current location or future locations at different levels of location precision and of the availability to users of different devices or applications. Availability assessments include inferences about the cost of interrupting a user in different ways and a user's current or future access to one or more communication channels that may be supported by one or more devices with appropriate capabilities.

Abstract: One or more models of memorability are provided that facilitate various computer-based applications including those centering on the storage, retrieval, and processing of information, applications that remind people about items they risk not recalling or overlooking, and facilitating communications of reminders. In one application, the models are used to help compose and navigate large personal stores of information about a user's activities, communications, images, and other content. In another application, views of files in directories are extended with the addition of memory landmarks, and a means for controlling the number of landmarks provided via changing a threshold on inferred memorability. Another application centers on the use of models of memorability to select subsets of images from larger sets representing events, for display in a slide show or ambient photo display.