Abstract: A filter element, in particular an air filter element, has a filter media body surrounding a hollow interior and has a first end and an opposite second end. A first end cap is positioned on the first end of the filter media body, and a second end cap is positioned on the second end of the filter media body. The second end cap is a closed end cap. The second end cap has a plurality of spaced-apart inner adapter projections arranged thereon and projecting in direction of a central axis away from the first end cap. At least some of the inner adapter projections are positioned in a circular arrangement so as to surround a center of the second end cap. Such a filter element is arranged in a housing of a filter system provided with a receiver arrangement for the filter element.

Abstract: A server system can receive an input identifying a problem to generate a solution using a machine-learning application. The method selects a machine-learning model template from a plurality of templates based at least in part on the input. The method analyzes one or more formats of the customer data to generate a customer data schema based at least in part a data ontology that applies to the identified problem. The method determines whether the customer data schema is misaligned with one or more key features of the selected machine-learning model template. Based on this determination, the method analyzes the metadata for the selected machine-learning model template to determine what additional information is required to re-align the customer data with the data expectations. The method can include gathering the addition information required to re-align the customer data with the data expectations of the selected machine-learning model template.

Abstract: The present disclosure relates to systems and methods for an intelligent assistant (e.g., a chatbot) that can be used to enable a user to generate a machine learning system. Techniques can be used to automatically generate a machine learning system to assist a user. In some cases, the user may not be a software developer and may have little or no experience in either machine learning techniques or software programming. In some embodiments, a user can interact with an intelligent assistant. The interaction can be aural, textual, or through a graphical user interface. The chatbot can translate natural language inputs into a structural representation of a machine learning solution using an ontology. In this way, a user can work with artificial intelligence without being a data scientist to develop, train, refine, and compile machine learning models as stand-alone executable code.

Abstract: The present disclosure relates to systems and methods for an intelligent assistant (e.g., a chatbot) that can be used to enable a user to generate a machine learning system. Techniques can be used to automatically generate a machine learning system to assist a user. In some cases, the user may not be a software developer and may have little or no experience in either machine learning techniques or software programming. In some embodiments, a user can interact with an intelligent assistant. The interaction can be aural, textual, or through a graphical user interface. The chatbot can translate natural language inputs into a structural representation of a machine learning solution using an ontology. In this way, a user can work with artificial intelligence without being a data scientist to develop, train, refine, and compile machine learning models as stand-alone executable code.

Abstract: The invention relates to an automatic injector device comprising a single-use, disposable, drug delivery assembly comprising a housing and a syringe assembly located at least partially within the housing, said syringe assembly including a plunger, a pre-filled unit-dose drug containing chamber, and needle, said plunger, drug containing chamber and needle being configured and dimensioned to function as an injection syringe; a reusable motorized transmission assembly comprising a housing, a motor and transmission assembly located within the housing, said transmission assembly being configured and dimensioned to engage the plunger of said syringe in the drug delivery assembly and expel said unit dose drug from the drug containing chamber, into the needle and out of the drug delivery assembly; said single-use disposable drug delivery assembly and said reusable motorized transmission assembly are in substantial axial alignment along a longitudinal axis defined by the syringe, plunger, pre-filled unit-dose drug con

Abstract: The present disclosure relates to systems and methods for a machine-learning platform for the safe serialization of a machine-learning application. Individual library components (e.g., a pipeline, a microservice routine, a software module, and an infrastructure model) can be encrypted using one or more keys. The keys can be stored in a location different from the storage location of the machine-learning application. Prior to incorporation of the library component into a machine-learning model, one or more keys can be retrieved from the remote storage location to authenticate that the one or more encrypted library components are authentic. The process can reject any of the one or more component, when the encrypted library component fails authentication. If a component is rejected, the system can roll back to a previous, authenticated version of the library component. The authenticated library components can be compiled into machine-learning software.

Abstract: The present disclosure relates to systems and methods for an adaptive pipelining composition service that can identify and incorporate one or more new models into the machine learning application. The machine learning application with the new model can be tested off-line with the results being compared with ground truth data. If the machine learning application with the new model outperforms the previously used model, the machine learning application can be upgraded and auto-promoted to production. One or more parameters may also be discovered. The new parameters may be incorporated into the existing model in an off-line mode. The machine learning application with the new parameters can be tested off-line and the results can be compared with previous results with existing parameters. If the new parameters outperform the existing parameters as compared with ground-truth data, the machine learning application can be auto-promoted to production.

Abstract: The present disclosure relates to systems and methods for a machine learning platform that generates a library of components to generate machine learning models and machine learning applications. The machine learning infrastructure system allows a user (i.e., a data scientist) to generate machine learning applications without having detailed knowledge of the cloud-based network infrastructure or knowledge of how to generate code for building the model. The machine learning platform can analyze the identified data and the user provided desired prediction and performance characteristics to select one or more library components and associated API to generate a machine learning application. The machine learning can monitor and evaluate the outputs of the machine learning model to allow for feedbacks and adjustments to the model. The machine learning application can be trained, tested, and compiled for export as stand-alone executable code.

Abstract: The present disclosure relates to systems and methods for an intelligent assistant (e.g., a chatbot) that can be used to enable a user to generate a machine learning system. Techniques can be used to automatically generate a machine learning system to assist a user. In some cases, the user may not be a software developer and may have little or no experience in either machine learning techniques or software programming. In some embodiments, a user can interact with an intelligent assistant. The interaction can be aural, textual, or through a graphical user interface. The chatbot can translate natural language inputs into a structural representation of a machine learning solution using an ontology. In this way, a user can work with artificial intelligence without being a data scientist to develop, train, refine, and compile machine learning models as stand-alone executable code.

Abstract: Techniques for creating a custom metric type to be added to a set of metrics generated by a data monitoring platform at run-time are disclosed. A system receives values defining properties of a custom metric type based on a custom metric template and a custom schema template. The system generates an instruction set, based on the values associated with the custom metric template, for generating the custom metric type on an executing data monitoring system. The system validates the instruction set and the custom schema to verify that the definitions for the custom metric type and the custom schema may be executed by the data monitoring system. The system adds the custom metric type, at run-time, to a set of metrics generated by the data monitoring system.

Abstract: The present invention proposes a resident station of launching and receiving subsea pigs installed in place of THE PLET, in order to enable the application of subsea rigid pipes and the full compliance with the routine internal inspection requirement for monitoring internal corrosion, through bi-directional pigging, mitigating existing operational risks in this type of operation. The invention enables the safe bi-directional pigging of a non-pigable pipe by installing the proposed equipment at the end of the subsea rigid pipe, connecting the pipeline to the main branch in order to ensure shutdown of the pig until the flow is reversed, importing gas from the main branch pipeline, causing the pig to return and be received in the launcher/receiver residing at the FPSO. Thus, the equipment can safely guarantee the periodic and instrumented bi-directional pigging of the pipeline, acting as a launcher/receiver (FIG. 1) residing and aligned to the pipe.

Abstract: The present disclosure relates to systems and methods for a machine learning platform that generates a library of components to generate machine learning models and machine learning applications. The machine learning infrastructure system allows a user (i.e., a data scientist) to generate machine learning applications without having detailed knowledge of the cloud-based network infrastructure or knowledge of how to generate code for building the model. The machine learning platform can analyze the identified data and the user provided desired prediction and performance characteristics to select one or more library components and associated API to generate a machine learning application. The machine learning can monitor and evaluate the outputs of the machine learning model to allow for feedbacks and adjustments to the model. The machine learning application can be trained, tested, and compiled for export as stand-alone executable code.

Abstract: An add-on device for a metered dose inhaler, an observance improvement system, and a method for improving observance of use in metered dose inhalers, the add-on device comprising an observance system housing component comprising an observance system with at least one pressure sensor; a mouthpiece component configured to fit, surround and removably engage with an exterior surface of a mouthpiece outlet provided on the metered dose inhaler; wherein the observance system housing is configured to fit and removably engage with the mouthpiece component; and the mouthpiece component is specifically adapted to conform to the exterior surface of the mouthpiece outlet of the metered dose inhaler without obstructing delivery of a dose of drug through the outlet.

Abstract: The present disclosure relates to systems and methods for an adaptive pipelining composition service that can identify and incorporate one or more new models into the machine learning application. The machine learning application with the new model can be tested off-line with the results being compared with ground truth data. If the machine learning application with the new model outperforms the previously used model, the machine learning application can be upgraded and auto-promoted to production. One or more parameters may also be discovered. The new parameters may be incorporated into the existing model in an off-line mode. The machine learning application with the new parameters can be tested off-line and the results can be compared with previous results with existing parameters. If the new parameters outperform the existing parameters as compared with ground-truth data, the machine learning application can be auto-promoted to production.

Abstract: The present invention relates to an add-on device for a metered dose inhaler, an observance improvement system, and a method for improving observance of use in metered dose inhalers, the add-on device comprising an observance system housing component comprising an observance system with at least one pressure sensor; a mouthpiece component configured to fit, surround and removably engage with an exterior surface of a mouthpiece outlet provided on the metered dose inhaler; wherein said observance system housing is configured to fit and removably engage with said mouthpiece component; and said mouthpiece component is specifically adapted to conform to the exterior surface of the mouthpiece outlet of the metered dose inhaler without obstructing delivery of a dose of drug through said outlet.

Abstract: The present disclosure relates to systems and methods for an intelligent assistant (e.g., a chatbot) that can be used to enable a user to generate a machine learning system. Techniques can be used to automatically generate a machine learning system to assist a user. In some cases, the user may not be a software developer and may have little or no experience in either machine learning techniques or software programming. In some embodiments, a user can interact with an intelligent assistant. The interaction can be aural, textual, or through a graphical user interface. The chatbot can translate natural language inputs into a structural representation of a machine learning solution using an ontology. In this way, a user can work with artificial intelligence without being a data scientist to develop, train, refine, and compile machine learning models as stand-alone executable code.

Abstract: A dose monitoring system for mounting on an injection pen having a body and a dose setting wheel defined at the end of the body. The dose monitoring system includes a magnet configured to be attached to an outer surface of the dose setting wheel, a housing configured to be attached to an outer surface of the body of the injection pen, the housing includes an integrated control unit and at least one magnetometer in electrical connection with the integrated control unit, the integrated control unit, when the housing is mounted on the injection pen, being configured to register at least one magnetic field sensed by the at least one magnetometer when the magnet co-rotates with the dose setting wheel during setting of a dose by a user on the injection pen, the integrated control unit being further configured, when the housing is mounted on the injection pen, to calculate a dose set by the user of the injection pen from the at least one magnetic field registered with the integrated control unit.

Abstract: The present disclosure relates to systems and methods for using existing data ontologies for generating machine learning solutions for a high-precision search of relevant services to compose pipelines with minimal human intervention. Data ontologies can be used to create a combination of non-logic based and logic-based sematic services that can significantly outperform both kinds of selection in terms of precision. Quality of Service (QoS) and product Key Performance Indicator (KPI) constraints can be used as part of architecture selection in developing, training, validating, and improving machine learning models. For data sets without existing ontologies, one or more ontologies be generated and stored for future use.

Abstract: The present invention relates to a dose control system adapted for an injectable drug delivery device, the drug delivery device comprising a substantially elongate drug delivery body, at least one injectable drug held by the body, the body having a distal and proximal extremity, wherein the dose control system comprises three-dimensional magnetic field producing means for producing a magnetic field along three axes (x,y,z); magnetic field detection means configured to detect changes in at least the magnetic field produced by the three-dimensional magnetic field producing means; displacement detection means configured to measure a relative displacement or relative movement of the drug delivery device, and an integrated control unit, wherein the integrated control unit is connected to the magnetic field detection means, and to the displacement detection means, for processing information received from both the magnetic field detection means and the displacement detection means; wherein the three-dimensional magn

Abstract: A dose control device adapted to be removably mounted onto an exterior peripheral surface of an injectable drug delivery device, the drug delivery device including a substantially elongate drug delivery body, at least one injectable drug held by the body, the body having a distal and proximal extremity. The dose control device includes a first component configured to fit and substantially encase at least a portion of an exterior peripheral surface of the drug delivery device, and located at a proximal extremity of the drug delivery device; a second component configured to fit and substantially encase a corresponding remaining unencased portion of the exterior peripheral surface of the drug delivery device, and also located at a proximal extremity of the drug delivery device.