Abstract: A method for generating an identification pattern, a terminal device, and the like are provided. The method includes: obtaining a first image (110), where the first image is used to represent any one of a Chinese character, an English character, and an Arabic numeral character; performing transformation on the first image based on a contour line of the first image to obtain a second image (120), where the second image includes the first image and a plurality of contour lines; and generating an identification pattern of a software program based on the second image (130). This application can improve a display effect of the identifier pattern.

Abstract: A system and method for predicting performance for a contact center via machine learning includes invoking, by a processor, an interaction between a contact center resource and an end user, and recording, by the processor, the interaction. The processor automatically analyzes the recorded interaction for identifying attributes associated with the interaction. The processor provides the identified attributes to a machine learning model which predicts a performance score based on the identified attributes. The performance score is compared against a threshold score, and a recommendation is output by the processor based on the comparing. The end user may be a candidate contact center agent considered for hiring, and the recommendation may be to hire the candidate or advance the candidate to a next step of an interview process.

Abstract: Systems and methods are shown for routing task objects to multiple agents that involve receiving and storing real-time sensor data for multiple agents, receiving tasks and, for each task, create a task object representing the task and placing the task object in an input buffer. For each task object in the input buffer, utilize the real-time sensor data to identify one or more of the multiple agents as suitable for assignment to the task, apply a routing strategy to the task object to further select one of the multiple agents identified as suitable for assignment of the task object based on sensor data, and route the task object from the input buffer to a workbin corresponding to the selected agent.

Abstract: Systems and methods are shown for routing task objects to multiple agents that involve receiving and storing real-time sensor data for multiple agents, receiving tasks and, for each task, create a task object representing the task and placing the task object in an input buffer. For each task object in the input buffer, utilize the real-time sensor data to identify one or more of the multiple agents as suitable for assignment to the task, apply a routing strategy to the task object to further select one of the multiple agents identified as suitable for assignment of the task object based on sensor data, and route the task object from the input buffer to a workbin corresponding to the selected agent.

Abstract: Systems and methods are shown for routing task objects to multiple agents that involve receiving and storing real-time sensor data for multiple agents, receiving tasks and, for each task, create a task object representing the task and placing the task object in an input buffer. For each task object in the input buffer, utilize the real-time sensor data to identify one or more of the multiple agents as suitable for assignment to the task, apply a routing strategy to the task object to further select one of the multiple agents identified as suitable for assignment of the task object based on sensor data, and route the task object from the input buffer to a workbin corresponding to the selected agent.