Wells Fargo Bank, N.a.

Vinothkumar Venkataraman

Naveen Gururaja Yeri

Paul Davis

Yang Yang

Ashutosh Verma

Rahul Ignatius

Rameshchandra Bhaskar Ketharaju

Matt Hord

Ananth Kendapadi

Tyler Case

Manpreet Singh

Ananya Bandyopadhyay

Divakar Vijayan

Nihar Swain

Shitiz Gupta

Ramesh Babu

Ramesh Babu Sarvesetty

Sandeep Pradhan

Brandon P Castaing

Thejas M Bhat

Mehdi Allahyari

Anusha Yenduri

Suranjana Sarkar

Vivek Kalyanaraman

Renuka Mannem

Saurav Kumar

An example computer system for authenticating a user can include: one or more processors; and non-transitory computer-readable storage media encoding instructions which, when executed by the one or more processors, causes the computer system to create: an analytical engine programmed to track activities of the user outside of a metaverse and within the metaverse; a query engine programmed to generate a series of questions for the user based upon the activities of the user outside of the metaverse and within the metaverse; and an authentication engine programmed to authenticate the user based upon responses to the series of questions provided by the user.

Authentication in metaverse

Systems, apparatuses, methods, and computer program products are disclosed for predicting an emotion in real-time based on a multimodal input including at least (i) an amount of keystrokes over a period of time and (ii) text. An example method may include receiving, by a communications circuitry, a multimodal input from a user including at least (i) an amount of keystrokes over a period of time and (ii) text. The example method may further include generating, by a trained machine learning model of an emotion prediction circuitry and using the multimodal input, an EmotionPrint for the user. The example method may finally include determining, by the emotion prediction circuitry and using the EmotionPrint, a next action.

Systems and methods for predicting an emotion based on a multimodal input

Systems, apparatuses, methods, and computer program products are disclosed for predicting an entity and intent based on captured speech. An example method includes capturing speech and converting the speech to text. The example method further includes causing generation of one or more entities and one or more intents based on the speech and the text. The example method further includes determining a next action based on each of the one or more entities and each of the one or more intents.

Systems and methods for determining a next action based on entities and intents

An example method includes detecting, by context analysis circuitry, occurrence of a triggering condition. The example method also includes scheduling, by context analysis circuitry and based on the occurrence of the triggering condition, retraining of a model. The example method also includes generating, by data grafting circuitry and in response to scheduling the retraining of the model, a context-relevant training data set based on a target context vector. The example method also includes retraining, by model training circuitry, the model using the context-relevant training data set to mitigate deterioration of performance of the model.

Systems and method for data grafting to enhance model robustness

Systems and methods for data grafting to enhance model robustness

Computer-automated systems and methods that retain requests for information until documents containing the information are released at a later date. Release of the documents can trigger natural language understanding of the documents to be automatically performed and responses to the requests to be generated. In some examples, the responses include recommendations for taking or not taking financial actions relating to the request and the information.

Responses to requests for information

Systems, apparatuses, methods, and computer program products are disclosed for mitigating deterioration of modeling system performance. An example method includes detecting, by context analysis circuitry, occurrence of a triggering condition. The example method also includes determining, by context analysis circuitry and in response to detecting the occurrence of the triggering condition, a destination modeling solution based on a regime strategy for the modeling system. The example method also includes switching, by regime deployment circuitry and based on the regime strategy, from a source modeling solution to a destination modeling solution.

Systems and methods for using regime switching to mitigate deterioration in performance of a modeling system

An automated teller machine ('ATM') receives, from a user computing device via a first wireless communication session, a unique transaction identifier (“UTN”). The ATM transmits the UTN to a bank computing system via a network. The ATM receives, from the bank computing system responsive to transmitting the UTN, transaction details and a first security code associated with the UTN. The ATM receives, from the user computing device via a second wireless communication session, a second security code. Responsive to determining that the first security code matches the second security code, the ATM processes a transaction in accordance with the transaction details.

Contactless user authentication and authorization

Systems, apparatuses, methods, and computer program products are disclosed for predicting an emotion based on speech and text derived from the speech. An example method includes capturing speech and generating hidden vectors based on the speech and text derived from the speech. The example method further includes generating a word aligned hidden vector based on the speech hidden vector and text hidden vector. The example method further includes generating a hidden vector based on the word aligned hidden vector and the text hidden vector. The example method further includes, after further processing of the final vector, generating one or more probabilities corresponding to one or more emotions. The example method finally includes determining a next action based on the generated one or more probabilities corresponding to one or more emotions.

Systems and methods for determining a next action based on a predicted emotion by weighting each portion of the action's reply

Systems, apparatuses, methods, and computer program products are disclosed for predicting an entity, intent, and emotion based on weighted and segmented portions of captured speech. An example method includes capturing speech and separating the speech and/or text derived from the speech into a plurality of discrete segments. The example method further includes determining or generating metadata, the metadata an entity, an intent, an emotion, and a weight associated with each of the entity, the intent, the emotion. The example method further includes determining a next action based on the determined or generated entity, intent, and emotion of each of the plurality of discrete segments.

Systems and methods for determining a next action based on weighted predicted emotions, entities, and intents

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Bengaluru, India

Vinothkumar Venkataraman