Abstract: The present disclosure provides methods and systems for managing autonomous vehicle data. The method may comprise: (a) collecting said autonomous vehicle data from the autonomous vehicle, wherein the autonomous vehicle data has a size of at least 1 terabyte; (b) processing the autonomous vehicle data to generate metadata corresponding to the autonomous vehicle data, wherein the autonomous vehicle data is stored in a database; (c) using at least a portion of the metadata to retrieve a subset of the autonomous vehicle data from the database, which subset of the autonomous vehicle data has a size less than the autonomous vehicle data; and (d) storing or transmitting the subset of the autonomous vehicle data.

Abstract: The present disclosure provides methods and systems for managing autonomous vehicle data. The method may comprise: (a) collecting said autonomous vehicle data from the autonomous vehicle, wherein the autonomous vehicle data has a size of at least 1 terabyte; (b) processing the autonomous vehicle data to generate metadata corresponding to the autonomous vehicle data, wherein the autonomous vehicle data is stored in a database; (c) using at least a portion of the metadata to retrieve a subset of the autonomous vehicle data from the database, which subset of the autonomous vehicle data has a size less than the autonomous vehicle data; and (d) storing or transmitting the subset of the autonomous vehicle data.

Abstract: A system and method for managing vehicle data of a vehicle might comprise a predictive model repository storing predictive models applicable to vehicle data, a decision engine for determining whether collected vehicle data constitutes a recordable event based on the predictive models, and a data repository storing vehicle data subsets upon the decision engine determining the occurrence of the recordable event. A vehicle data subset might include a vehicle data type, a recordable event type, and an indication of a priority level for the recordable event. A communication module might schedule transmission of a transmission dataset corresponding to the vehicle data subset for the recordable event, wherein a scheduling of the transmission is based upon the priority level of the recordable event. A data transmission module might transmit the transmission dataset to a remote computer system based on instructions provided by the communication module.

Abstract: A personalized transportation plan for multi-modal transportation can be generated from an origin location, a destination location, and a route component server. A route data record might comprise a plurality of route components from the origin to the destination, represented in a route component record comprising indications of a route component start location, a route component end location, and a transportation mode for the route component. Route data records can be filtered based on user constraints determined from a user profile database. The filtered plurality of route data records can be used, with a transportation provider database, to determine a set of transportation option records for a given route component record wherein transportation option records indicate a provider of transportation from the route component start location for the route component record to the route component end location for the route component record. Bid messages can be generated for route components.

Abstract: The present disclosure provides methods and systems for predicting a trip intent or destination while a user is traveling along a route. The method may comprise: (a) receiving a starting geographic location of the route and data about an identity of the user; (b) retrieving a trained classifier based at least in part on the data about the identity of the user; (c) using the trained classifier to predict the trip intent or destination based on the starting geographic location; and (d) while the user is traveling in a terrestrial vehicle along at least a portion of said route, presenting one or more transactional options to the user on an electronic device, wherein the one or more transactional options are identified based on the trip intent or destination predicted in (c).

Abstract: The present disclosure provides methods and systems for managing autonomous vehicle data. The method may comprise: (a) collecting said autonomous vehicle data from the autonomous vehicle, wherein the autonomous vehicle data has a size of at least 1 terabyte; (b) processing the autonomous vehicle data to generate metadata corresponding to the autonomous vehicle data, wherein the autonomous vehicle data is stored in a database; (c) using at least a portion of the metadata to retrieve a subset of the autonomous vehicle data from the database, which subset of the autonomous vehicle data has a size less than the autonomous vehicle data; and (d) storing or transmitting the subset of the autonomous vehicle data.

Abstract: The present disclosure provides methods and systems for facilitating commerce while a user is traveling along a travel route. A method for facilitating commerce while a user is traveling along a travel route may comprise: (a) at a server, receiving a starting geographic location and a destination geographic location of the user; (b) using the starting geographic location and the destination geographic location to generate the travel route for the user, which travel route is directed from the starting geographic location to the destination geographic location; (c) using the server to identify one or more transactional options for the user along the route; and (d) while the user is traveling in a terrestrial vehicle along at least a portion of the travel route, presenting the one or more transactional options to the user on an electronic device.

Abstract: Data mining system including a user interface 102, a plurality of data sources 114, at least one top-down data analysis module 104 and at least one bottom-up data analysis module 104' in cooperative communication with each other and with the user interface 102, and a server processor 106 in communication with the data sources 114 and with the data analysis modules 104, 104'. Data mining method involving the integration of top-down and bottom-up data mining techniques to extract 208 predictive models from a data source 114. A data source 114 is selected 200 and used to construct 202 a target data set 108. A data analysis module is selected 203 and module specific parameters are set 205. The selected data analysis module is applied 206 to the target data set based on the set parameters. Finally, predictive models are extracted 208 based on the target data set 108.

Abstract: A new expert system performs a redesign in connection with an original design. The expert system comprises a discrepancy determination component that identifies a discrepancy between operation of the original design and a desired operation. A redesign component including at least one redesign module associated with a discrepancy generates a redesign in response to the original design and the identified discrepancy. Finally, a redesign generation component generates a redesign module in response to a previously-identified discrepancy and design, the redesign module thereafter being used by the redesign component.

Abstract: A case-based retrieval system and method for using same for retrieving justifiably relevant cases from a case library in which past problem solving experiences (cases) are stored. The system obtains the symptoms of a new problem. A set of cases are initially retrieved from the case library based on the symptoms of the new problem. It is then determined for each initially retrieved case using a casual explanation for the case whether the case is justifiably relevant to the new problem, with only justifiably relevant cases being returned to the user of the system.

Abstract: A new system design tool receives a design representation from a designer, simulates it and compares the simulated operation of the design representation with the desired operation as provided by the operator and identifies causes of discrepancies therebetween. The redesign component performs redesign operations on the design representation in response to the discrepancies. The redesign component includes a plurality of redesign modules each including a redesign solution selection module which receives the identification of causes of discrepancies and the design representation and identifies a redesign solution in response thereto. Each redesign module, in turn, includes at least one redesign operator module which includes a plurality of redesign operators controlled by a redesign control module. The redesign operators each perform a redesign operation in connection with the design representation.

Abstract: A new expert system performs a redesign in connection with an original design. The expert system comprises a discrepancy determination component that identifies a discrepancy between operation of the original design and a desired operation. A redesign component including at least one redesign module associated with a discrepancy generates a redesign in response to the original design and the identified discrepancy. Finally, a redesign generation component generates a redesign module in response to a previously-identified discrepancy and design, the redesign module thereafter being used by the redesign component.

Abstract: An expert system includes a blackboard, a plurality of knowledge sources, a control knowledge source and a control module. The blackboard stores data used during an execution cycle. Each knowledge source includes rules for performing selected operations in connection with the data in the blackboard. The control knowledge source includes selection rules for selecting among the knowledge sources. The control module performing an execution cycle including an eligibility determination phase to identify one or more of the knowledge sources, a knowledge source selection phase using the selection rules in said control knowledge source to select one of the identified knowledge sources, and an action phase to process a rule of the selected knowledge source.

Abstract: An expert system for performing a redesign operation in connection with an original design. A working memory includes a plurality of blackboards each for storing a design representation and desired operational characteristics. A simulation component comprises a plurality of simulators each for generating simulated operational characteristics in connection with a design representation on one of said blackboards. A diagnostic component identifies operational discrepancies between the simulated operational characteristics and the desired operational characteristics and the causes of the operational characteristics. A redesign component generates a redesign of the design representations on the blackboards in response to the original design and the identified dicrepancies and discrepancy causes.