Abstract: A method of modifying a healthcare employee schedule includes receiving an indication that a pre-scheduled healthcare employee belonging to a first class of healthcare employees will be absent from a scheduled shift at a first healthcare facility, receiving a first plurality of patient health profiles for a first plurality of patients expected to visit the first healthcare facility during the scheduled shift, receiving a first plurality of employee profiles for a plurality of healthcare employees, simulating a first predicted outcome score for each employee of the plurality of healthcare employees using a simulator, selecting a preferred replacement employee from the plurality of healthcare employees based on the simulated first predicted outcome scores, and scheduling the preferred replacement employee during the scheduled shift.

Abstract: A method of adjusting mental state includes acquiring video data of an individual, extracting image data and audio data from the video data, extracting semantic text data from the audio data, identifying a first set of features, predicting a baseline mental state, identifying a target mental state, and simulating a predicted path from the baseline mental state to the target mental state. The baseline mental state is predicted based on the first set of features. The predicted path is simulated using a multidimensional mental state model, a plurality of actions, and a first computer-implemented machine learning model. The predicted path comprises one or more actions of the plurality of actions and corresponding changes to at least one of first and second dimensions of the multidimensional mental state model. An indication of the one or more actions of the predicted path is output to the individual.

Abstract: A fluorescence detection system, including apparatus and methods, suitable for qPCR and other fluorescence-based analyses. The system may comprise various components, including a stage, an illumination module, a detection module, and an optical relay structure. The stage may be configured to support a sample holder. The illumination module may include one or more discrete light sources configured to produce excitation light. The detection module may be configured to detect fluorescence emission light produced, in response to the excitation light, by a fluorescent sample positioned in the sample holder. The optical relay structure may include a beamsplitter assembly configured to direct the excitation light from the illumination module along an illumination path to the sample holder and to direct the fluorescence emission light from the sample holder along a response path to the imaging module. The system may enhance the quality of excitation light hitting samples in the sample holder.

Abstract: A product count of a number of physical products within a physical grouping of a plurality of the physical products is determined based on an image of the physical grouping. A cycle counter generates image coordinates for visible product surfaces within the image. The cycle counter generates three-dimensional virtual base coordinates for the expected locations of the plurality of the physical products within the physical grouping. The cycle counter determines the actual locations of the visible product surfaces within three-dimensional space based on a comparison of the image coordinates and the virtual base coordinates. The cycle counter determines the product count of the number of physical products within the physical grouping based on the actual locations.

Abstract: A method includes acquiring digital video data that portrays an interacting event, identifying a plurality of video features in the digital video data, analyzing the plurality of video features to create a relationship graph, determining a relationship score based on the relationship graph using a first computer-implemented machine learning model, and outputting the relationship score with a user interface. The interacting event comprises a plurality of interactions between a first individual and a second individual and each video feature of the plurality of video features corresponds to an interaction of the plurality of interactions. The relationship graph comprises a first node, a second node, and a first edge extending from the first node to the second node. The first node represents the first individual, the second node represents the second individual, and a weight of the first edge represents a relationship strength between the first individual and the second individual.

Abstract: A method includes acquiring digital video data that portrays an interacting event, identifying a plurality of features in the digital video data with a first computer-implemented machine learning model, analyzing the plurality of features to create a baseline relationship graph, determining a target relationship graph, generating one or more actions for increasing similarity between the baseline relationship graph and the target relationship graph, and outputting the one or more actions by a user interface. The one or more actions are generated using a simulator, a second computer-implemented machine learning model, and a plurality of actions. The second computer-implemented machine learning model is configured to relate actions of the plurality of actions to changes to relationship graphs, the simulator is configured to simulate changes to the baseline relationship graph using the second computer-implemented machine learning model and the plurality of actions.

Abstract: A method includes acquiring digital video data that portrays an interacting event, identifying a plurality of features in the digital video data, and analyzing the plurality of features to create a relationship graph. The relationship graph comprises a plurality of nodes and a plurality of edges, each node of the plurality of nodes represents an individual of the plurality of individuals, and each edge of the plurality of edges extends between two nodes of the plurality of nodes, and the plurality of edges represents a plurality of interactions of the interacting event. The method further includes identifying an edge of the plurality of edges as an anomalous edge, creating an output representative of the anomalous edge, and outputting the output representative of the anomalous edge. The anomalous edge is identified by a computer-implemented machine learning model configured to identify anomalous edges in relationship graphs.

Abstract: A product count of a number of physical products within a physical grouping of a plurality of the physical products is determined based on an image of the physical grouping. A cycle counter generates image coordinates for visible product surfaces within the image. The cycle counter generates three-dimensional virtual base coordinates for the expected locations of the plurality of the physical products within the physical grouping. The cycle counter determines the actual locations of the visible product surfaces within three-dimensional space based on a comparison of the image coordinates and the virtual base coordinates. The cycle counter determines the product count of the number of physical products within the physical grouping based on the actual locations.

Abstract: A method includes acquiring digital video data that portrays an interacting event, extracting image data, audio data, and semantic text data from the video data, analyzing the extracted data to identify a plurality of video features, and analyzing the plurality of video features to create a relationship graph. The interacting event comprises a plurality of interactions between plurality of individuals and the relationship graph comprises a plurality of nodes and a plurality of edges. Each node of the plurality of nodes represents an individual of the plurality of individuals, and each edge of the plurality of edges extends between two nodes of the plurality of nodes, and the plurality of edges represents the plurality of interactions. The method further comprises determining whether a first key feature is present in the relationship graph, wherein presence of the first key feature is predictive of a positive outcome of the interacting event.

Abstract: A method of identifying work conditions likely to cause employee resignation includes receiving a set of attributes for a nurse and receiving a plurality of shift variables. The set of attributes includes one or more attributes that describe the nurse and each shift variable of the plurality of shift variables describes a characteristic of a work condition in a nursing workplace, such that the plurality of shift variables describe a plurality of work conditions. The method further includes predicting a plurality of resignation likelihoods for the plurality of work conditions, identifying at least one work condition of the plurality of work conditions associated with a high likelihood of nurse resignation based on the plurality of resignation likelihoods, and outputting an indication of the at least one work condition.

Abstract: A method of creating a healthcare employee schedule includes receiving a first plurality of patient health profiles for a first plurality of patients, receiving a first plurality of employee profiles for a first plurality of healthcare employees, creating a first plurality of employee combinations, simulating a first predicted outcome score for each employee combination using a simulator, selecting a first preferred employee combination of the plurality of predicted employee combinations based on the simulated first predicted outcome scores, and scheduling healthcare employees of the first preferred employee combination during the first time period. Each employee combination of the first plurality of employee combinations includes at least two healthcare employees of the first plurality of healthcare employees.

Abstract: An example of a method of creating preferred work assignments for nursing workers includes receiving a first set of attributes for a first nurse including one or more attributes that describe the first nurse, receiving a plurality of shift constraints for a plurality of shift variables, generating a preferred work assignment, and outputting an indication of the preferred work assignment. The preferred work assignment is generated by optimizing, using an optimization algorithm, shift variables for the first nurse using a computer-implemented machine learning model, the first set of nurse attributes, and the plurality of shift constraints. The first preferred work assignment is predicted by the optimization algorithm to reduce a first resignation probability of the first nurse according to outputs from the computer-implemented machine learning model and the computer-implemented machine learning model is configured to relate resignation likelihood to shift variables and nurse attributes.

Abstract: A method of modifying a healthcare employee schedule includes receiving an indication that a pre-scheduled healthcare employee belonging to a first class of healthcare employees will be absent from a scheduled shift at a first healthcare facility, receiving a first plurality of patient health profiles for a first plurality of patients expected to visit the first healthcare facility during the scheduled shift, receiving a first plurality of employee profiles for a plurality of healthcare employees, simulating a first predicted outcome score for each employee of the plurality of healthcare employees using a simulator, selecting a preferred replacement employee from the plurality of healthcare employees based on the simulated first predicted outcome scores, and scheduling the preferred replacement employee during the scheduled shift.

Abstract: A coating analyzer is configured to receive electronic image data of a physical coating and to generate information regarding the pigments of the physical coating. The coating analyzer applies a computer vision model trained on baseline image data to the electronic image data. The coating analyzer assigns color values to the pigments forming the electronic image data and generates pigment groups based on the assigned color values. The pigment groups provide color palette data regarding the pigments forming the coating.

Abstract: A method of protecting user privacy online includes collecting, via a computer-based user data agent, user data generated by a user on a user device and identifying a first set of traits associated with the user data. The method further includes generating a user profile based on the first set of traits associated with the user data and generating an alternate persona defined by a second set of traits that is different from the first set of traits. The method further includes automatically performing online activities based on the alternate persona to produce artificial user data that obscures real user data.

Abstract: A method of predicting mental state includes extracting image data, audio data, and semantic text data from video data, where the video data portrays a first individual. The method further includes analyzing the image data to identify a first feature set, analyzing the audio data to identify a second feature set, analyzing the semantic text data to identify a third feature set, and predicting a mental state for the individual based on the first feature set, the second feature set, the third feature set, and a multidimensional mental state model. The predicted mental state is output. The multidimensional mental state model includes a first dimension, a second dimension, and a third dimension. The first dimension corresponds to a first aspect of mental state, the second dimension corresponds to a second aspect of mental state, and the third dimension corresponds to a third aspect of mental state.

Abstract: A method of predicting an group mental state according to the present disclosure includes acquiring video data of a first individual and a second individual, extracting first image data of the first individual from the video data, extracting first audio data of the first individual from the video data, extracting second image data of the first individual from the video data, extracting second audio data of the first individual from the video data, extracting first semantic text data from the first audio data, and extracting second semantic text data from the second audio data. The method further includes identifying a first set of features, identifying a second set of features, predicting a first mental state value, predicting a second mental state value, predicting a third mental state value, predicting a fourth mental state value, and generating first and second average mental state values.

Abstract: Pathways between reference locations in a physical system are generated based on a layout table. Nodes and edges of the directed graph are associated with cell locations of the layout table. The cell locations define features of the reference locations. Parameters of the nodes and edges are defined based on descriptors recalled from the cells associated with the nodes and edges. The nodes and edges are configured based on the descriptors. Path data regarding potential pathways is generated based on the defined nodes and edges.

Abstract: An electronic component assembly having thermal pads with thermal vias coupling an image sensor and a camera board fab is provided for heat dissipation. The electronic component assembly can include: a circuit board having at least one thermal pad disposed on a top surface of the circuit board; and an image sensor disposed on the top surface of the circuit board, having at least one conductive pad disposed at at least one corner of the image sensor. The at least one thermal pad is coupled to the at least one conductive pad of the image sensor and the at least one thermal pad is formed with a plurality of first thermal vias penetrating the thermal pad and the circuit board for transfer of heat of the image sensor.

Abstract: A method of adjusting mental state includes acquiring video data of a first individual, identifying a first set of features based on image, audio, and text data extracted from the video data, predicting a baseline mental state for the first individual based on the first set of features, identifying a target mental state, and simulating a predicted path from the baseline mental state to the target mental state. The predicted path is simulated using a multidimensional mental state model, a plurality of actions, and a computer-implemented machine learning model. The predicted path comprises one or more actions of the plurality of actions and corresponding changes to at least one of first and second dimensions of the multidimensional mental state model. The one or more actions of the predicted path are output to a second individual and are performable to adjust the baseline mental state of the first individual.