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 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 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 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: A server system may receive two or more Quality of Service (QoS) dimensions for the multi-objective optimization model, wherein the two or more QoS dimensions include at least a first QoS dimension and a second QoS dimension. The server system may maximize the multi-objective optimization model along the first QoS dimension, wherein the maximizing includes selecting one or more pipelines for the multi-objective optimization model in the software architecture that meet QoS expectations specified for the first QoS dimension and the second QoS dimension, wherein an ordering of the pipelines is dependent on which QoS dimensions were optimized and de-optimized and to what extent, wherein the multi-objective optimization model is partially de-optimized along the second QoS dimension in order to comply with the QoS expectations for the first QoS dimension, and whereby there is a tradeoff between the first QoS dimension and the second QoS dimension.

Abstract: Dose control device for a handheld pen-type injectable-drug delivery device, the handheld pen-type injectable-drug delivery device comprising an elongate body with a proximal and distal extremity, a longitudinal axis extending from the proximal extremity to the distal extremity, and a rotatable dose setting wheel located at the proximal extremity, wherein the dose control device comprises a magnetic field producing mechanism located at the proximal extremity of the elongate body; one or more magnetic field sensors in communication with a data processing unit located on an outer surface of, or inside, the elongate body; and a clutch assembly configured to selectively move the magnetic field producing mechanism from a first, engaged, position, to a second, disengaged, position.

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: A server system may receive two or more Quality of Service (QoS) dimensions for the multi-objective optimization model, wherein the two or more QoS dimensions include at least a first QoS dimension and a second QoS dimension. The server system may maximize the multi-objective optimization model along the first QoS dimension, wherein the maximizing includes selecting one or more pipelines for the multi-objective optimization model in the software architecture that meet QoS expectations specified for the first QoS dimension and the second QoS dimension, wherein an ordering of the pipelines is dependent on which QoS dimensions were optimized and de-optimized and to what extent, wherein the multi-objective optimization model is partially de-optimized along the second QoS dimension in order to comply with the QoS expectations for the first QoS dimension, and whereby there is a tradeoff between the first QoS dimension and the second QoS dimension.

Abstract: The present invention relates to a metered dose inhaler (MDI) device comprising a mechanically activated visual cue representing a number of doses expelled from, or doses remaining in, a drug-containing cartridge or canister locatable within a body of said MDI, wherein the visual cue is visible through at least a portion of the body of said MDI, and a removable data processing system configured to verify the dose of drug administered and/or inhaled, wherein the MDI device further comprises a correlation system configured to correlate physically released doses from the drug containing cartridge or canister with the administration and/or inhalation data stored in the removable data processing system.

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.