Capital One Services, LLC

Alexandra Coman

Erik Thomas Mueller

Rui Zhang

Margaret Mayer

Liwei Dai

Zachary Kulis

Sara Mikulic

Vinay Igure

Chihyen Yang

Jihoon Jay Song

The disclosed technology involves autonomously identifying goals and sub-goals from a user utterance and generating responses to the user based on the goals and sub-goals.

Systems and methods for generating automated natural language responses based on identified goals and sub-goals from an utterance

Methods and systems are described for generating dynamic conversational responses sensitive to different emotional contexts using machine learning models. The dynamic conversational responses may be generated in real time and reflect the likely emotional context by detecting socially close entities and events in user input.

Modifying dynamic conversational responses based on detecting references to socially close people

Systems and methods for identifying irregularities during an automated user interaction are disclosed. The system may receive a communication and extract a perceived irregularity from the communication. The system may generate a first explanatory hypothesis associated with the perceived irregularity having an associated confidence measurement. The system may selectively retrieve user information based on the generated hypothesis and generate an investigational strategy associated with the hypothesis. In response to the investigational strategy, the system may receive a user communication, and the system may update the confidence measurement based on the user communication. When the confidence measurement exceeds the predetermined confidence threshold the system may validate the perceived irregularity as a true irregularity and provide a computer-generated dialogue response indicative of a proposed resolution of the irregularity. When no existing hypothesis has a confidence measurement exceeding the threshold, the system may generate a novel hypothesis to be validated.

Systems and methods using natural language processing to identify irregularities in a user utterance

Conversational agents (CAs) may analyze language input and generate and output a response to a user. For example, when receiving a user's support request, the CA may determine whether to conduct self-disclosure by including information about the CA's 'self' in a response to the user. For example, based on performing sentiment analysis of a support request user input, the CA may determine that the user is expressing negative emotions. Based on the user's expression of negative emotions, the CA may perform self-disclosure as part of generating a response to the user. A CA that is configured to engage in self-disclosure, for instance by including information about a CA's self in an output response, may increase users' acceptance of the CA, which may make a user more likely to trust and/or interact with a CA.

Self-disclosing artificial intelligence-based conversational agents

Systems and methods are provided herein for autonomously determining and resolving a customer's perceived discrepancy during a customer service interaction. The method can include receiving an incoming communication from a customer; extracting a perceived state and an expected state (possibly of a product or service) based on the incoming communication; determining a perceived discrepancy between the perceived and expected states of the customer; retrieving customer information; extracting a current state of the customer from the retrieved customer information, verifying, by a rule-based platform, the discrepancy; generating a response based on the discrepancy after comparing the perceived stated with the current state, where the response may include a confirmation or a correction related to the discrepancy and a personalized explanation describing the current state of the customer; and outputting, for presentation to the customer, the response.

Systems and methods for automated discrepancy determination, explanation, and resolution with personalization

A computing device may execute a conversational agent that may receive language input. The conversational agent may analyze the language input based on configured goals to determine conclusions regarding the language input. The conversational agent may determine whether to modify the truth of one or more of the conclusions, and whether to include or omit the one or more conclusions or modified conclusions in an output response. The conversational agent may also store justifications for including or omitting each conclusion or modified conclusion. The conversational agent may output a response that indicates the conclusions and/or modified conclusions that were selected for output. A user may request that the conversational agent output the justifications for generating the output response. The conversational agent may output the justifications based on receiving the request.

Sincerity-aware artificial intelligence-based conversational agents

Systems and methods are provided herein for autonomously determining and resolving a customer's perceived discrepancy during a customer service interaction. The method can include receiving an incoming communication from a customer; extracting, by a Natural Language Processing (NLP) device, a perceived state and an expected state of a product or service based on the incoming communication; determining by a discrepancy determination device, a discrepancy between the perceived and expected state of the product or service; verifying, by a rule-based platform, the discrepancy; generating a response based on the discrepancy, the response comprising one or more of: a fact pattern response related to the perceived discrepancy and a confirmation or correction of a verified discrepancy; and outputting, for presentation to the customer, the response.

Systems and methods for automated discrepancy determination, explanation, and resolution

Consistent with the disclosed embodiments, systems and methods are provided herein for autonomously responding to customer problems. In one example implementation, a system performing a method is provided. The system may receive a customer utterance associated with a customer and define, based on the customer utterance, a first customer narrative comprising a first customer goal. The system may also determine whether the first customer narrative is sufficient to identify a first customer problem corresponding to at least a first problem of a plurality of problems. Responsive to determining that the first customer narrative is sufficient, the system may identify a first response corresponding with the first problem. The system may also customize the first response for the customer based on at least the first customer narrative, and execute the customized response.

Systems and methods for understanding and solving customer problems by extracting and reasoning about customer narratives

Consistent with the disclosed embodiments, systems and methods are provided herein for autonomously generating a thoughtful gesture for a customer. In one example implementation of the disclosed technology, a method is provided that includes receiving incoming customer dialogue and determining, based on the customer dialogue, customer information including one or more of: customer preferences, customer biographical information, and customer current life circumstances. The method also includes generating, based on the customer information, gesture-specific information-eliciting utterances for additional dialogue with the customer and identifying one or more response opportunities based on additional incoming customer dialogue responsive to sending the gesture-specific information-eliciting utterances to the customer. Further, the method includes generating a thoughtful gesture based on the identified one or more response opportunities and outputting, for presentation to the customer, the thoughtful gesture.

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Tysons Corner, VA, United States of America

Alexandra Coman