Abstract: Clinical content analytics engines and associated processes are described. An engine receives a clinical decision support document, accesses corresponding reference content, identifies and extracts medical intervention content from the clinical decision support document, segments extracted medical intervention content into a first plurality of segments including at least a first segment comprising a first set of text, determines if the first segment corresponds to at least a first item included in the reference content, the first item comprising a second set of text comprising terminology different than that found in the first set of text, and in response to determining that the first segment corresponds to the first item included in the reference content, causing a report to include an indication that the first segment corresponds to the first item included in the reference content.

Abstract: Clinical content analytics engines and associated processes are described. An engine receives a clinical decision support document, accesses corresponding reference content, identifies and extracts medical intervention content from the clinical decision support document, segments extracted medical intervention content into a first plurality of segments including at least a first segment comprising a first set of text, determines if the first segment corresponds to at least a first item included in the reference content, the first item comprising a second set of text comprising terminology different than that found in the first set of text, and in response to determining that the first segment corresponds to the first item included in the reference content, causing a report to include an indication that the first segment corresponds to the first item included in the reference content.

Abstract: Clinical content analytics engines and associated processes are described. An engine receives a clinical decision support document, accesses corresponding reference content, identifies and extracts medical intervention content from the clinical decision support document, segments extracted medical intervention content into a first plurality of segments including at least a first segment comprising a first set of text, determines if the first segment corresponds to at least a first item included in the reference content, the first item comprising a second set of text comprising terminology different than that found in the first set of text, and in response to determining that the first segment corresponds to the first item included in the reference content, causing a report to include an indication that the first segment corresponds to the first item included in the reference content.

Abstract: A method for retrieving information spread across a plurality of different ontologies, including: defining a meta-ontology, wherein the meta-ontology includes high-level properties and their mappings to specific properties defined in a plurality of different ontologies; receiving a question, wherein the question is associated with a high-level property; and providing an answer to the question, wherein the answer is determined by using the meta-ontology.

Abstract: Clinical content analytics engines and associated processes are described. An engine receives a clinical decision support document, accesses corresponding reference content, identifies and extracts medical intervention content from the clinical decision support document, segments extracted medical intervention content into a first plurality of segments including at least a first segment comprising a first set of text, determines if the first segment corresponds to at least a first item included in the reference content, the first item comprising a second set of text comprising terminology different than that found in the first set of text, and in response to determining that the first segment corresponds to the first item included in the reference content, causing a report to include an indication that the first segment corresponds to the first item included in the reference content.

Abstract: Adaptive remote maintenance of rolling stocks is provided by machine-learning (28) of rules. Existing rules or models are automatically updated. Machine learning (28) is applied to establish a more efficient rule set. Rules may be replaced, generalized, or otherwise adapted based on interaction (26) by the dispatchers with the results of the current rules. The acceptance or discarding of an event by the dispatcher is used as a ground truth for supervised machine learning (28) of a new rule. The machine learning (28) uses user feedback to update the rule set.

Abstract: In a framework for acquiring and analyzing data from a network of sensors, plug-in software interfaces are used to provide scalability and flexibility. Data collection set-up data is exchanged through one or more first plug-in software interfaces with data collection devices, to configure the processor to collect measurement data from the data collection devices. Analysis set-up data is exchanged through one or more second plug-in software interfaces with one or more data analysis software packages, to configure the processor to provide a predefined subset of the measurement data to the data analysis software packages and to accept analysis results from the data analysis software packages. Measurement data and analysis results are subsequently exchanged through the plug-in interfaces.

Abstract: A system generates medical knowledge base information by searching at least one repository of medical information to identify sentences including a received medical term. A data processor searches the identified sentences to identify sentences including a medical term different to the received term in response to a predetermined repository of medical terms and excludes sentences without a term different to the received term, to provide remaining multiple term sentences. The data processor groups different terms of individual sentences of the multiple term sentences to provide grouped terms, determines whether a medically valid relationship occurs between different terms of an individual group of terms of the grouped terms by using predetermined sentence structure and syntax rules and outputs data representing grouped terms having a medically valid relationship.

Abstract: Adaptive remote maintenance of rolling stocks is provided by machine-learning (28) of rules. Existing rules or models are automatically updated. Machine learning (28) is applied to establish a more efficient rule set. Rules may be replaced, generalized, or otherwise adapted based on interaction (26) by the dispatchers with the results of the current rules. The acceptance or discarding of an event by the dispatcher is used as a ground truth for supervised machine learning (28) of a new rule. The machine learning (28) uses user feedback to update the rule set.

Abstract: Methods and systems based on Dedicated Short Range Communications (DSRC) determine that a vehicle occupies a parking spot. A road-side system checks in the vehicle having an on-board DSRC system into a parking system. The system provides the on-board DSRC system with a parking rate and the on-board DSRC system provides the road-side system with payment data. A final parking fee is determined and is charged after the vehicle has left the parking spot. Parking rates are determined dynamically based on existing and/or expected conditions. A planning system provides a vehicle with a parking spot at a scheduled time. Navigation data is provided to the vehicle to reach the scheduled parking spot. Traffic and environmental policies are enforced by applying the methods and systems.

Abstract: A system generates medical knowledge base information by searching at least one repository of medical information to identify sentences including a received medical term. A data processor searches the identified sentences to identify sentences including a medical term different to the received term in response to a predetermined repository of medical terms and excludes sentences without a term different to the received term, to provide remaining multiple term sentences. The data processor groups different terms of individual sentences of the multiple term sentences to provide grouped terms, determines whether a medically valid relationship occurs between different terms of an individual group of terms of the grouped terms by using predetermined sentence structure and syntax rules and outputs data representing grouped terms having a medically valid relationship.

Abstract: Systems and methods for monitoring parking spots are disclosed. A system includes at least one vehicle and a remote computer that are in communication with each other. A vehicle includes a camera that generates image data, a location device that generates geographic coordinates of the vehicle, a computing device that receives the image data from the camera and the geographic coordinates of the vehicle and optionally data from a laser scanner that is calibrated with the camera and is connected to a smartphone that transmits data to the remote computer. Image data generated by the camera is processed with a reference image and data from the laser scanner to determine an occupation status of the parking spot. The occupation status is transmitted by the remote computer to a second vehicle.

Abstract: A method for brokering information includes receiving an initiator produced system activity, scoring a relevance of the system activity with each of a plurality of subscriber-specified thresholds, and transmitting an activity response to a subscriber activity feed in response to the system activity, the subscriber activity feed selected according to the relevance of a corresponding subscriber-specified threshold.

Abstract: A method for retrieving information spread across a plurality of different ontologies, including: defining a meta-ontology, wherein the meta-ontology includes high-level properties and their mappings to specific properties defined in a plurality of different ontologies; receiving a question, wherein the question is associated with a high-level property; and providing an answer to the question, wherein the answer is determined by using the meta-ontology.

Abstract: In a framework for acquiring and analyzing data from a network of sensors, plug-in software interfaces are used to provide scalability and flexibility. Data collection set-up data is exchanged through one or more first plug-in software interfaces with data collection devices, to configure the processor to collect measurement data from the data collection devices. Analysis set-up data is exchanged through one or more second plug-in software interfaces with one or more data analysis software packages, to configure the processor to provide a predefined subset of the measurement data to the data analysis software packages and to accept analysis results from the data analysis software packages. Measurement data and analysis results are subsequently exchanged through the plug-in interfaces.

Abstract: The invention provides a method for semantic relation extraction, wherein on the basis of an annotated training corpus having tokens with associated relational labels each indicating a relation between the respective token and a selectable key entity semantic relation between said key entity and other entities are directly extracted from unstructured text using a probabilistic extraction model.

Abstract: At least one genotype-phenotype relationship is extracted based on genotype data of a group of genes for different organisms of a group of organisms. A first database stores genotype data of each organism of the group of organisms. For each organism a genotype vector is stored having a vector component for each gene of the group of genes. A second database stores phenotype data of each organism of the group of organisms. For each organism a phenotype vector is stored having a vector component for each phenotype feature of a group of phenotype features of the organism. A calculation unit uses a machine learning process to classify organisms with different phenotypes depending on the genotype vectors stored in the first database and the phenotype vectors stored in the second database to extract the genotype-phenotype relationship.

Abstract: Method for selecting at least one potential marker molecule indicating an user defined phenotype feature of an organic object, comprising the steps of providing genotype data of genes of a group of organic objects and phenotype data of said group of organic objects, categorizing said genotype data and said phenotype data to generate categorized data of said group of organic objects, relating statistically said phenotype feature with the generated categorized data to extract genes having a strong statistical relationship with said phenotype feature, wherein the extracted genes and proteins corresponding to said extracted genes are selected as potential marker molecules.

Abstract: A causal network is used, which describes the regulatory genetic network of a cell such that nodes of the causal network represent genes of the regulatory genetic network and connectors of the causal network represent regulatory interactions between the genes of the regulatory genetic network. This causal network is adapted to the regulatory genetic network using a structure learning method. Using prior knowledge about a selected regulatory interaction between two genes, an a-priori information is determined for the connector representing the selected regulatory interaction. The a-priori information is taken into account for adapting the causal network to the regulatory genetic network using a structure learning method.