Abstract: A phone tree traversal system includes an input device configured to receive a user call request, a memory, and one or more processors. The one or more processors analyze the user call request to identify an entity to call and an objective of the call, and obtain a map of a phone tree utilized by an automated call receiving system of the entity. The phone tree includes at least one node with multiple path segments that branch from the node, and each node includes a respective prompt. The one or more processors determine a route along the map to an endpoint of the phone tree associated with the objective. During the call, the one or more processors navigate the phone tree to reach the endpoint by submitting information in response to the respective prompt at each node along the route that is determined.

Abstract: A method that includes identifying a processing error, obtaining user input information for a determined period prior to identifying the processing error, determining steps undertaken during the determined period prior to identifying the processing error based on the input information, and obtaining context awareness information related to the one or more processors. The method also includes obtaining network user input information and network context awareness information related to the processing error from other electronic devices within a network, determining a reduced amount of steps from the steps undertaken during the determined period to identify the processing error to form an error reproduction plan by analyzing the network user input information and the network context awareness information in comparison to the user input information and context awareness information, and communicating the error reproduction plan to a user or third party.

Abstract: A method that includes identifying a processing error, obtaining user input information for a determined period prior to identifying the processing error, determining steps undertaken during the determined period prior to identifying the processing error based on the input information, and obtaining context awareness information related to the one or more processors. The method also includes obtaining network user input information and network context awareness information related to the processing error from other electronic devices within a network, determining a reduced amount of steps from the steps undertaken during the determined period to identify the processing error to form an error reproduction plan by analyzing the network user input information and the network context awareness information in comparison to the user input information and context awareness information, and communicating the error reproduction plan to a user or third party.

Abstract: In one aspect, a device may include at least one processor and storage accessible to the at least one processor. The storage may include instructions executable by the at least one processor to receive input from one or more sensors and, based on the input from the one or more sensors, determine that a trigger related to a user moving between viewing a first display location and a second display location is satisfied. Responsive to the determination, the instructions may be executable to move at least one graphical object presented on at least one display from the first display location to a third display location different from the first and second display locations.

Abstract: A software process is tested using an instance of a computer system. A first set of data and a second set of data are collected. The first set relates to resources used by the software process during the testing and the second set relates to conditions of the instance during the testing. A machine learning algorithm is trained with the first set of data and the second set of data, thereby generating a model relating to the resources used by the software process under the conditions of the instance. User input is received, the user input relates to expectations of a test plan for the software test. The test plan is created as a function of the model and the user input.

Abstract: In one aspect, a device may include at least one processor and storage accessible to the at least one processor. The storage may include instructions executable by the at least one processor to receive input from one or more sensors and, based on the input from the one or more sensors, determine that a trigger related to a user moving between viewing a first display location and a second display location is satisfied. Responsive to the determination, the instructions may be executable to move at least one graphical object presented on at least one display from the first display location to a third display location different from the first and second display locations.

Abstract: A phone tree traversal system includes an input device configured to receive a user call request, a memory, and one or more processors. The one or more processors analyze the user call request to identify an entity to call and an objective of the call, and obtain a map of a phone tree utilized by an automated call receiving system of the entity. The phone tree includes at least one node with multiple path segments that branch from the node, and each node includes a respective prompt. The one or more processors determine a route along the map to an endpoint of the phone tree associated with the objective. During the call, the one or more processors navigate the phone tree to reach the endpoint by submitting information in response to the respective prompt at each node along the route that is determined.

Abstract: Methods, systems, apparatus, and program products that can generate scaled-down load test models for testing real-world loads are disclosed herein. One method includes providing a test environment of a system including multiple nodes. The test environment includes virtual nodes corresponding to the system nodes and each virtual node functions under a virtual load similar to each corresponding node functioning under a real-world load. The method further includes utilizing a machine learning algorithm to repeatedly apply at least one virtual load to the virtual node(s) in the test environment until a scaled-down load test model mimicking the system under a pre-defined real-world load is generated. Here, the virtual load(s) applied to the virtual node(s) is/are comparatively smaller relative to each of corresponding real-world loads for the node(s) defining the pre-defined real-world load. Systems, apparatus, and program products that include and/or perform the methods are also disclosed herein.

Abstract: For detecting and resolving bad audio during conferencing, methods, apparatus, and systems are disclosed. One apparatus includes a processor and a memory that stores code executable by the processor. The processor detects bad audio for a conference call, the conference call involving a plurality of participants. The processor switches a first input stream to an analysis mode, where the bad audio corresponds to a first one of a plurality of input streams, the first input stream associated with a first participant. The processor sends a conference output channel to the first participant while in the analysis mode and concurrently analyzes the first input stream using a plurality of audio tools while in the analysis mode. The processor returns the first input stream to a conferencing mode in response to resolving the bad audio.

Abstract: Disclosed is an improved approach to implement clock alignments between a test subject and its corresponding controller device. Phase locking is performed for the clocks between the test subject and controller device via a training sequence to obtain the appropriate alignment(s). Alignment logic is included on both the testchip and the controller device to implement alignment.