Abstract: Provided is a process, including: obtaining a first training dataset, training a first machine-learning model on the first training dataset, obtaining a set of candidate question sequences, forming virtual subject-entity records, forming a second training dataset, training a second machine-learning model, and storing the adjusted parameters of the second machine-learning model in memory.

Abstract: Provided is a process, including: obtaining a first training dataset, training a first machine-learning model on the first training dataset, obtaining a set of candidate question sequences, forming virtual subject-entity records, forming a second training dataset, training a second machine-learning model, and storing the adjusted parameters of the second machine-learning model in memory.

Abstract: Provided is a process, including: obtaining a first training dataset, training a first machine-learning model on the first training dataset, obtaining a set of candidate question sequences, forming virtual subject-entity records, forming a second training dataset, training a second machine-learning model, and storing the adjusted parameters of the second machine-learning model in memory.

Abstract: Provided is a process, including: obtaining a first training dataset, training a first machine-learning model on the first training dataset, obtaining a set of candidate question sequences, forming virtual subject-entity records, forming a second training dataset, training a second machine-learning model, and storing the adjusted parameters of the second machine-learning model in memory.

Abstract: Provided is a process, including: obtaining a first training dataset, training a first machine-learning model on the first training dataset, obtaining a set of candidate question sequences, forming virtual subject-entity records, forming a second training dataset, training a second machine-learning model, and storing the adjusted parameters of the second machine-learning model in memory.

Abstract: Provided is a process including: receiving a data token to be passed from a first node to a second node; retrieving machine learning model attributes from a collection of one or more of the sub-models of a federated machine-learning model; determining based on the machine learning model attributes, that the data token is learning relevant to members of the collection of one or more of the sub-models and, in response, adding the data toke to a training set to be used by at least some members of the collection of one or more of the sub-models; determining a collection of data tokens to transmit from the second node to a third node of the set of nodes participating in a federated machine-learning model; and transmitting the collection of data tokens.

Abstract: Provided is a process, including: obtaining a first training dataset, training a first machine-learning model on the first training dataset, obtaining a set of candidate question sequences, forming virtual subject-entity records, forming a second training dataset, training a second machine-learning model, and storing the adjusted parameters of the second machine-learning model in memory.

Abstract: Injection molding system (20, 220, 320, 420) having hydraulic circuit (50, 250, 350, 450) for motivating hydraulic actuator (36, 38) comprising: pump motor (52); and digital displacement pump (54, 354A, 354B, 454A, 454B) having: piston assemblies (102A, 102B, 102C, 102D) actuatable by pump motor (52), piston assembly including: pair of inlet and outlets operable to be individually opened and closed at selective rate independently of actuation of each piston assembly; at least one hydraulic actuator (36, 38) operably coupled on each of rod side (64, 66) and cylinder side (58, 74) to first subset of the plurality of piston assemblies (102B, 102D) and second subset of the plurality of piston assemblies (102A, 102C), respectively.

Abstract: Injection molding system (20, 220, 320, 420) having hydraulic circuit (50, 250, 350, 450) for motivating hydraulic actuator (36, 38) comprising: pump motor (52); and digital displacement pump (54, 354A, 354B, 454A, 454B) having: piston assemblies (102A, 102B, 102C, 102D) actuatable by pump motor (52), piston assembly including: pair of inlet and outlets operable to be individually opened and closed at selective rate independently of actuation of the each piston assembly; at least one hydraulic actuator (36, 38) operably coupled on each of rod side (64, 66) and cylinder side (58, 74) to first subset of the plurality of piston assemblies (102B, 102D) and second subset of the plurality of piston assemblies (102A, 102C), respectively.