Abstract: A method includes receiving at least one data object and identifying, for a first aspect of the at least one data object, text strings of free form textual information having a first text string type. The method also includes generating updated free form textual information by removing the at least one text string having the first text string type. The method also includes generating one or more feature vectors based on the updated free form textual information using at least one of a unigram, a bigram, and a trigram. The method also includes using an artificial intelligence engine that uses at least one machine learning model configured to provide, using the one or more feature vectors, an output that includes at least one prediction indicating at least one resource domain and a weight value indicating a probability that the at least one resource domain corresponds to the free form textual information.

Abstract: Method and systems for product inventory are described. In one embodiment, an inventory surface unit retains a plurality of products in an unordered state. An inventory identification unit acquires information for an orientation, and location of the plurality of products on the inventory surface unit. The inventory identification unit is disposed above the inventory surface unit. An inventory pick unit is adapted to pick a product among the plurality of products from the inventory surface unit based on the orientation and location acquired by the inventory identification unit. The inventory pick unit is disposed proximal the inventory surface unit, and the inventory pick unit is communicatively connected to the inventory identification unit. Additional methods and systems are disclosed.

Abstract: The medication dispensing assembly includes a housing having an interior. A medication bottle is disposed in the interior with its cap assembly facing vertically downwardly. The cap assembly includes outer, middle, and inner pieces. The outer and inner pieces are fixed with one another, and the middle piece is fixed with the bottle. A drive unit is disposed in the interior of the housing and is configured to rotate the bottle and the middle piece of the cap assembly about a vertical axis. The three pieces of the cap assembly have respective medication openings, and rotating the bottle causes individual pills to travel first from the bottle into the inner piece, then into the middle piece, then around the vertical axis, and then outside of the cap assembly through the medication opening of the outer piece.

Abstract: A method for controlling secure delivery of a medication package includes receiving a medication delivery request to deliver a medication package to a first delivery location. The method also includes identifying one or more authenticated delivery locations corresponding to a recipient and determining whether the one or more authenticated delivery locations includes the first delivery location and, in response to a determination that the one or more authenticated delivery locations includes the first delivery location, instructing an unmanned aerial vehicle to transport the medication package from a starting location to the first delivery location. The method also includes, in response to the unmanned aerial vehicle communicating authentication data, determining whether the authentication data corresponds to the recipient.

Abstract: A medication dispensing system includes an automated dispensing device that includes cells with electronically activated locks. The device is configured to detect when medication counts in the cells fall below predetermined thresholds. The system further includes a plurality of first electronic devices that are associated with some of the cells and that have imagers. In response to any of the medication counts in the cells being below the predetermined threshold, the automated dispensing device is configured to automatically send a replenishment needed notification to the first electronic device associated with the cell. That first electronic device is configured to transmit a picture of a medication to a second electronic device. In response to a positive verification by a user of the second electronic device that the medication in the picture is the correct medication, the second electronic device is configured to transmit an unlock signal to the automated dispensing device.

Abstract: A monitoring system for mapping and monitoring inter-application communications in a computing ecosystem is described. The monitoring system provides consolidated visibility to computing ecosystems by providing end-to-end mapping and monitoring of inter-application communications and events, changes, incidents, and status information of applications, services, and systems. As described, the monitoring system is configured to (a) identify communication paths linking the host devices, (b) generate an ecosystem map based on the communication paths, (c) transmit a monitoring signal to the network, (d) receive a monitoring response from the host devices in response to the monitoring signal including at least a first status, (e) process the monitoring response with the ecosystem map to generate an active ecosystem map, and (f) display the active ecosystem map including the host devices and at least one status associated with the host devices.

Abstract: A system for integrating a telephone system and a computing system is provided. The system includes an interactive voice response (IVR) platform configured to: obtain a computer-readable command based on user input; determine a goal in the web application associated with the computer-readable command; and obtain information indicating a shortest navigation path to the goal, relative to other navigational paths to the goal in the web application. The system includes a cobrowse client configured to receive a document object model (DOM) of a current state of the web application from a cobrowse session for a web server hosting the web application. The IVR platform is configured to, based on the DOM, determine a next navigation action along the shortest navigation path; generate a user prompt based on the next navigation action; and monitor user progress to reach the goal in the web application using one or more updated DOMs.

Abstract: Methods and systems for selecting a machine learning algorithm are described. In one embodiment, one or more factors to be used by a plurality of machine learning algorithms in predicting a value of a required pharmacy element of a prescription are identified, each of the plurality of machine learning algorithms are trained to predict the value of the required pharmacy element using a first subset of previously received prescriptions, respective success rates for each of the plurality of machine learning algorithms at predicting respective known values of respective known required pharmacy elements for each of a second subset of the previously received prescriptions are determined, and a first of the plurality of machine learning algorithms having a highest success rate is selected to predict the value of the required pharmacy element of the prescription for a first predetermined period.

Abstract: Methods and systems for classifying and editing insurance claims records are described. In one embodiment, a method, which can be performed by a processor, includes determining that a first prescription drug prescribed to a first patient should be filled by a pharmacy, determining a first guidepost date and a second guidepost date related to the first prescription, determining a third guidepost date associated with a second prescription, comparing the third guidepost date to the first and second guidepost dates to determine whether the third guidepost date occurs after the first guidepost date and before the second guidepost date, and filling and dispensing the first prescription drug as a full prescription supply and the second prescription drug as a full prescription supply on the same dispense date when the third guidepost date occurs after the first guidepost date and before the second guidepost date.

Abstract: A method includes defining model attributes of a machine model that organizes feedback data into topic groups based on similarities in concepts in the feedback data. The model attributes include a topic model number that defines how many topic groups are to be created, a hyperparameter optimization alpha value, and/or a hyperparameter optimization beta value. The method also includes generating the machine model using the model attributes that are defined and the feedback data, and applying the machine model to the feedback data to divide different portions of the feedback data into the different topic groups based on contents of the feedback data, the topic model number, the hyperparameter optimization alpha value, and/or the hyperparameter optimization beta value.

Abstract: A computer system includes memory storing a database including a label text table for storing multiple label texts each associated with a national drug code (NDC), a fields table, and a field options table. A processor is configured to execute instructions including receiving drug input from a user indicative of a specified NDC, querying the database to obtain a label text associated with the specified NDC, at least one field associated with the obtained label text, and multiple field options associated with the obtained field, building a data structure, converting the data structure to a user interface for displaying the obtained label text, the obtained field, and the multiple obtained field options, receiving a user selection of one of the multiple obtained field options, and printing the label text including the updated field to a label and/or saving the label text including the updated field to a record database.

Abstract: Method starts with a processor receiving configuration settings including an identified task, a relationship data, and a criticality value. Processor initializes a communication session with an agent client device. The communication session is between a virtual caller associated with the system and the agent client device. Processor then processes an audio signal of the communication session to generate an agent utterance and generates a transcribed agent utterance based on the agent utterance using a speech-to-text processor. Processor generates a virtual caller utterance using a task-specific virtual caller neural network associated with the identified task. The virtual caller utterance can be generated based on the transcribed agent utterance. Processor then causes the virtual caller utterance to be played back in the communication session to the agent client device. Other embodiments are disclosed herein.

Abstract: A temperature-controlled medication packaging system includes a storage with a temperature-controlled interior containing at least one medication. The packaging system further includes at least one shipping container and at least one coolant that is sized to fit within the at least one shipping container. A first robot is adapted to retrieve the at least one medication from the storage and transport the at least one medication to a second robot. The second robot is adapted to receive the at least one medication from the first robot and to bring the at least one medication and the at least one shipping container and the at least one coolant together for packaging.

Abstract: The medication container includes a receptacle and a cap. The cap includes a gate that can be selectively opened to enable access to medications in the receptacle and closed to restrict access to medications in the receptacle. The gate has a locking mechanism that only unlocks to allow the gate to open from the closed position in response to an application of a downward force on a portion of the gate to resiliently deflect a portion of the gate. The cap further includes electronic components that are configured to monitor movement of medications through the cap and into and out of the receptacle, the electronic components including a memory that is configured to store data pertaining to such passages of medications.

Abstract: A method may include filling an order of a plurality of orders with a dosing filler system. The method may include receiving pharmaceutical orders including order for drugs used in multi-drug regimens using an order processing device. Each of the multi-drug regimen may have a plurality of scheduled dosing events. The method may include transporting containers to a dosing device, using the dosing device to dispense drugs for scheduled dosing events into the containers based on the received pharmaceutical orders, transporting the containers with the dispensed drugs to the container sealing device, using the container sealing device to seal the plurality of the containers with the dispensed drugs, transporting the dosage unit containers to the container identifier assembly, and using the container identifier assembly to identify the dosage unit containers based on the received pharmaceutical order.

Abstract: The shuttle system is configured to convey a container through a filling center. The shuttle system includes a shuttle and a base plate that is operably attached with the shuttle. A plurality of support members extend vertically upwardly from the base plate and surround a container receiving space. The support members are configured to directly contact side surfaces of the container and to support the container as the shuttle moves through the filling center without locking the container to the shuttle.

Abstract: A method includes generating an intervention model by determining principal components for features of a training set, associating each feature of the training set with a principal component, selecting features of the training set most highly correlated with principal components, training a machine learning model with at least some of the selected features, and saving the verified trained machine learning model as the intervention model. The method includes determining multiple channel-specific intervention expectations. Each channel-specific intervention expectation indicates a likelihood that the user will take action in response to an intervention being executed using the engagement channel corresponding to the channel-specific intervention expectation. The method includes selecting an intervention and scheduling the selected intervention for execution.

Abstract: The medication container includes a receptacle that has an inner space for holding medications. The cap assembly is coupled with the receptacle for retaining the medications in the inner space. The cap assembly includes at least one passage that can be selectively opened and closed and includes at least one medication sensor that is configured to detect any medications travelling through the passage and out of the receptacle in a contactless manner. A microprocessor is in electrical communication with the at least one medication sensor and with a memory. The microprocessor is configured to record data to the memory in response to the at least one medication sensor detecting a medication travelling through the passage. A wireless module is in electrical communication with the microprocessor for uploading the data to an external device.

Abstract: A method includes generating an intervention model by determining principal components for features of a training set, associating each feature of the training set with one of the principal components, selecting features of the training set most closely correlated with the principal components, performing a regression analysis on the selected features to determine a subset of the selected features that are most closely correlated with a model target, training a machine learning model with the subset, verifying the trained machine learning model with a verification set, and saving the verified trained machine learning model as the intervention model. The method includes determining an intervention expectation indicating a likelihood that the user will take action in response to an intervention being execute, determining a likelihood of a gap in care for the user, selecting and scheduling an intervention for execution based on the care gap likelihood and the intervention expectation.

Abstract: A pharmaceutical order processing system for processing a plurality of pharmaceutical containers includes a container repository, a container selector, an order consolidator transporter and an order consolidator. The container repository supports the pharmaceutical containers. The container selector has a picker to pick the pharmaceutical containers from the pharmaceutical container repository. The container selector includes a carriage supporting the picker and movable relative to the container repository to move the picker around the container repository. The order consolidator receives the pharmaceutical containers and places the pharmaceutical containers in a shipping package. The order consolidator transporter receives the pharmaceutical containers after they have been picked by the picker and transports the pharmaceutical containers toward the order consolidator.