Invented by Yochai Konig, David Konig, Bank of America NA

The market for chatbots for automatic quality management of chat agents is rapidly growing, as businesses recognize the importance of delivering exceptional customer service. Chatbots have become an integral part of customer support, enabling companies to handle a large volume of inquiries efficiently. However, ensuring consistent quality in chat interactions can be challenging, especially when dealing with a large team of chat agents. This is where chatbots for automatic quality management come into play. Chatbots for automatic quality management are designed to monitor, evaluate, and improve the performance of chat agents in real-time. These intelligent systems analyze chat conversations, identify areas for improvement, and provide feedback to agents, enabling them to enhance their skills and deliver better customer experiences. With the help of artificial intelligence (AI) and natural language processing (NLP) technologies, these chatbots can assess various aspects of chat interactions, such as response time, accuracy, tone, and adherence to company guidelines. One of the key advantages of using chatbots for automatic quality management is their ability to provide immediate feedback to chat agents. Instead of relying on manual evaluations, which can be time-consuming and subjective, these chatbots offer instant insights into agent performance. Agents can receive feedback on their strengths and weaknesses, enabling them to make necessary adjustments in real-time. This not only helps in improving customer satisfaction but also boosts agent morale and productivity. Furthermore, chatbots for automatic quality management can also assist in training new chat agents. By analyzing successful chat interactions, these chatbots can identify best practices and provide guidance to new agents. This helps in reducing the learning curve and ensures that new agents are equipped with the necessary skills to handle customer inquiries effectively. The market for chatbots for automatic quality management is witnessing significant growth due to the increasing demand for efficient customer support solutions. Businesses across various industries, including e-commerce, banking, and telecommunications, are adopting these chatbots to streamline their customer service operations. The ability to monitor and improve agent performance in real-time allows companies to maintain consistent quality standards and deliver exceptional customer experiences. Moreover, chatbots for automatic quality management also offer cost-saving benefits. By automating the evaluation process, companies can reduce the need for manual quality assurance teams, resulting in significant cost savings. Additionally, these chatbots can identify areas where agents require additional training, enabling companies to allocate resources more efficiently. As the market continues to evolve, chatbots for automatic quality management are expected to become more sophisticated. Advancements in AI and NLP technologies will enable these chatbots to provide more accurate evaluations and personalized feedback to chat agents. They will also be able to analyze customer sentiment and emotions, allowing companies to further enhance their customer service strategies. In conclusion, the market for chatbots for automatic quality management of chat agents is growing rapidly, driven by the need for efficient customer support solutions. These chatbots offer real-time monitoring, evaluation, and feedback to chat agents, enabling them to improve their performance and deliver exceptional customer experiences. With the potential for cost savings and advancements in AI technology, the future of chatbots for automatic quality management looks promising.

The Bank of America NA invention works as follows

The method includes selecting a topic to interact with a human contact center agent, identifying a dialog-tree associated with that topic, and engaging in an automated communication with the human contact center agent using the identified dialog-tree.

Background for Chat bots for automatic quality management of chat agents

Modern contact centers, in general, are staffed by agents or employees that serve as the interface between an organisation, like a company, with external entities, like customers. Human sales agents in contact centers can help customers make purchasing decisions, and they may also receive orders from customers. Human support agents in contact centers can help customers with problems relating to products and services offered by an organization. Contact center agents may interact with outside entities, such as customers, through speech-voice (e.g. telephone calls, voice over IP, or VoIP), video (e.g. video conferencing), or text (e.g. emails, text chats, or text messages).

Quality monitoring is the process by which contact centers evaluate agents to ensure that they are providing high-quality service when assisting customers. A quality monitoring process monitors the performance of agents by evaluating their interactions with customers. This includes assessing whether they were polite, courteous, efficient and knowledgeable.

The information in the Background section may not be prior art, but is meant to enhance understanding of the background.

One or more aspects” of an example embodiment of the present invention relate to a system and a method of utilizing an automated chat program in order to simulate human customers for quality management purposes, while interacting with human agents.

The following is an example of an embodiment of a method to automate quality management for agents in a contact centre: selecting a topic to interact with a human contact center agent, identifying a dialog-tree associated with that topic, and engaging the human contact center agent in an automated communication based on this identified dialog-tree. Receiving an input from the agent, the processor identifies a node in the dialog tree associated to the input, selecting an automated phrase in response to the node, and then outputting the phrase.

The method can also include: determining whether the agent’s input is semantically equal to that of the target input by the processor; calculating a score for the human agent on the basis of the determining; and displaying feedback to the user based upon the calculated score.

In one embodiment, feedback could include a summary of the agent’s strengths and/or weaknesses.

In one embodiment, the method can also include the following: the processor may invoke a coaching session with the human agent on the basis of the feedback.

In one embodiment, the automated phrases may be selected from the plurality of phrases provided to agents by customers during a current dialog status, relating to a selected topic, in interactions with the contact center.

In one embodiment, selecting the automated phrase can further include: identifying by the processor the frequency of each of a plurality phrases for the dialog state in use; and selecting by the processor one of the plurality based on that identified frequency.

In one embodiment, the contact center may select the topic based on an optimization criterion.

In one embodiment, the selected topic is based on the performance of the agent in interactions with customers who are interested in the topic.

In one embodiment, the topic can be chosen based on the performance of the human agent in previous automated communication sessions.

In one embodiment, an automated communication session can be a chat session using text.

The system is based on a dialog tree that has been identified as being associated with a selected topic. It also includes a processor and a memory. When executed by the processor the instructions cause the processor: to select a topic to interact with a human contact center agent; to identify the dialog tree; to engage in an automated conversation with the agent using the dialog tree; to select an automated phrase in response to identifying this node, and to output the phrase.

The instructions can further instruct the processor to perform the following: identify a target input for the human agent associated with the current node identified; compare the input of the agent to that of the target input; determine if the input is semantically equal to the input of the target agent; calculate a score based on this determining; then output feedback based upon the calculated score.

In one embodiment, feedback could include a summary of the agent’s strengths and/or weaknesses.

In one embodiment, the instructions could also cause the processor: to invoke a coaching for the human agent on the basis of the feedback.

In one embodiment, the automated phrases may be selected from the plurality of phrases provided to agents by customers during a current dialog status, relating to a selected topic, in interactions with the contact center.

In one embodiment, selecting the automated phrase can further include identifying the frequency of each of a plurality phrases for the dialog state currently in use; and selecting a phrase from the plurality based on that identified frequency.

In one embodiment, the contact center may select the topic based on an optimization criterion.

Click here to view the patent on Google Patents.