Abstract: Aspects of call routing based on technical skills of users are discussed. Responses to a set of questions posed to a user are received to assess a technical skill level of the user. The user may be categorized in a category from among a plurality of categories based on the technical skill level and a decision may be provided to a route a call from the user to one of a human agent and a virtual agent based on the categorization.

Abstract: System for providing troubleshooting solutions for an electronic device is described. The system includes a query engine, that may receive a troubleshoot query from a user of the electronic device. The troubleshoot query indicates a device model of the electronic device and an issue with the electronic device. Further, the query engine may identify a context vector corresponding to the device model based on a mapping table and the device model. The context vector describes a relationship between the device model, a plurality of troubleshoot support documents, and functional features of the device model. Further, the system includes a machine learning engine to determine a troubleshooting solution for the issue based on the issue and the context vector corresponding to the device.

Abstract: Aspects of resolution of customer issues are discussed. A customer issue may be presented to a human agent as a query. The human agent may provide an agent response for the query. Based on the agent response, a simulated customer message may be determined. Iteratively, agent responses may be received and simulated customer messages may be provided to resolve the query. A sequence of agent responses and simulated customer messages usable to resolve the query may be probabilistically determined based on the agent responses and the simulated customer messages.

Abstract: Aspects of human assisted virtual agent support are discussed. A conversation between a user and a virtual agent may be monitored. A probability of the user abandoning the conversation may be predicted and a notification may be provided to a human agent to provide assistance in the conversation based on the probability.

Abstract: Examples herein provide a method. The method includes applying an electrical potential difference over a blood sample in a testing cassette of a microfluidic device, the cassette including a microfluidic channel through which the blood sample flows. The method includes measuring, over a duration of time, an electrical signal passing through the blood sample as the blood sample flows from a first end and coagulates at a second end of the microfluidic channel to obtain a measurement function as a function of time. The method also includes correlating the measurement function to a characteristic of the blood sample.

Abstract: Examples for providing automated user support are described herein. In an example, a query that a user is seeking to resolve is determined, based on real-time tracking of multi-modal inputs from the user on a user-support system. For the query, a resolution is provided to the user from a resolution database to provide automated user-support.

Abstract: The present subject matter relates to example to provide solutions using stochastic modelling. In one example, a plurality case logs corresponding to an issue may be analyzed to identify a plurality of resolution steps. In addition, a relationship between each of the plurality of resolution steps may be identified from the plurality of case logs to generate a knowledge representation. In one example, a relationship between resolution steps is determined using a stochastic modelling technique. Further, based on the knowledge representation, a primary solution for the issue may be generated to resolve the issue.

Abstract: Example implementations relate to a microfluidics sensing system. For example, a microfluidics sensing system may include a portable computing device to execute a microfluidics application, a microfluidic chip coupled to the portable computing device, the microfluidic chip including a microfluidic pumping and sensing region to perform a test on a biologic sample, and a printed circuit board (PCB) on a microfluidic reader to instruct the microfluidic pumping and sensing region to perform the test based on a command received from the microfluidics application.

Abstract: A method for handling multimode microfluidic data may output first signals from a first sensor on a microfluidic chip, may output second signals from a second sensor on the microfluidic chip and may output a single data stream from the microfluidic chip. The single data stream may include the first signals from the first sensor and the second signals from the second sensor. The method may further receive the single data stream from the microfluidic chip, route the first signals from the first sensor to a first data processing thread and route the second signals from the second sensor to a second data processing thread.

Abstract: Examples herein provide a method. The method includes applying an electrical potential difference over a blood sample in a testing cassette of a microfluidic device, the cassette including a microfluidic channel through which the blood sample flows. The method includes measuring, over a duration of time, an electrical signal passing through the blood sample as the blood sample flows from a first end and coagulates at a second end of the microfluidic channel to obtain a measurement function as a function of time. The method also includes correlating the measurement function to a characteristic of the blood sample.

Abstract: Fluid may be pumped within a microfluidic channel across a cell/particle sensor using a microscopic resistor. The microscopic resistor may be selectively actuated so as to heat the fluid within the microfluidic channel to a temperature below a nucleation energy of the fluid so as to regulate a temperature of the fluid for at least when the cell/particle sensor is sensing the fluid.

Abstract: A data receiver thread is continuously executed to receive in which signals indicating a fluid parameter. A predetermined time quantity of the signals is repeatedly buffered. Upon completion of the buffering of each predetermined time quantity of the signals, a data processing thread is initiated that executes on the just completed buffered predetermined time quantity of signals. Upon completion of each data processing thread, data from the just completed data processing thread is passed to a data plotting thread. Results of the data plotting thread are displayed on a portable electronic device while the data receiver thread is being executed.

Abstract: Example implementations relate to a microfluidics sensing system. For example, a microfluidics sensing system may include a portable computing device to execute a microfluidics application, a microfluidic chip coupled to the portable computing device, the microfluidic chip including a microfluidic pumping and sensing region to perform a test on a biologic sample, and a printed circuit board (PCB) on a microfluidic reader to instruct the microfluidic pumping and sensing region to perform the test based on a command received from the microfluidics application.

Abstract: Examples disclosed herein provide methods for selecting a frame from a set of frames. One example method includes obtaining a set of frames and, for each frame from the set of frames, determining whether frame meets a quality condition. The example method further includes assigning a quality score for each frame from the set of frames, and selecting a frame from the set of frames, where the selected frame has a higher quality score than other frames from the set of frames.