Enabling Data-Driven and Empathetic Interactions: A Context-Aware 3D Virtual Agent in Mixed Reality for Enhanced Financial Customer Experience
- URL: http://arxiv.org/abs/2410.12051v1
- Date: Tue, 15 Oct 2024 20:41:10 GMT
- Title: Enabling Data-Driven and Empathetic Interactions: A Context-Aware 3D Virtual Agent in Mixed Reality for Enhanced Financial Customer Experience
- Authors: Cindy Xu, Mengyu Chen, Pranav Deshpande, Elvir Azanli, Runqing Yang, Joseph Ligman,
- Abstract summary: We introduce a novel system designed to enhance customer service in the financial and retail sectors through a context-aware 3D virtual agent.
Our approach focuses on enabling data-driven and empathetic interactions that ensure customer satisfaction.
- Score: 0.18846515534317265
- License:
- Abstract: In this paper, we introduce a novel system designed to enhance customer service in the financial and retail sectors through a context-aware 3D virtual agent, utilizing Mixed Reality (MR) and Vision Language Models (VLMs). Our approach focuses on enabling data-driven and empathetic interactions that ensure customer satisfaction by introducing situational awareness of the physical location, personalized interactions based on customer profiles, and rigorous privacy and security standards. We discuss our design considerations critical for deployment in real-world customer service environments, addressing challenges in user data management and sensitive information handling. We also outline the system architecture and key features unique to banking and retail environments. Our work demonstrates the potential of integrating MR and VLMs in service industries, offering practical insights in customer service delivery while maintaining high standards of security and personalization.
Related papers
- CRMArena: Understanding the Capacity of LLM Agents to Perform Professional CRM Tasks in Realistic Environments [90.29937153770835]
We introduce CRMArena, a benchmark designed to evaluate AI agents on realistic tasks grounded in professional work environments.
We show that state-of-the-art LLM agents succeed in less than 40% of the tasks with ReAct prompting, and less than 55% even with function-calling abilities.
Our findings highlight the need for enhanced agent capabilities in function-calling and rule-following to be deployed in real-world work environments.
arXiv Detail & Related papers (2024-11-04T17:30:51Z) - MetaTrading: An Immersion-Aware Model Trading Framework for Vehicular Metaverse Services [94.61039892220037]
We present a novel immersion-aware model trading framework that incentivizes metaverse users (MUs) to contribute learning models for augmented reality (AR) services in the vehicular metaverse.
Considering dynamic network conditions and privacy concerns, we formulate the reward decisions of MSPs as a multi-agent Markov decision process.
Experimental results demonstrate that the proposed framework can effectively provide higher-value models for object detection and classification in AR services on real AR-related vehicle datasets.
arXiv Detail & Related papers (2024-10-25T16:20:46Z) - A Deep Reinforcement Learning Approach for Security-Aware Service Acquisition in IoT [2.765106384328772]
We propose a complete framework that defines suitable levels of privacy and security requirements in the acquisition of services in the Internet of Things.
Through the use of a Reinforcement Learning based solution, a user agent, inside the environment, is trained to choose the best smart objects granting access to the target services.
arXiv Detail & Related papers (2024-04-04T08:00:12Z) - Deployment of Advanced and Intelligent Logistics Vehicles with Enhanced Tracking and Security Features [0.0]
This study focuses on the implementation of modern and intelligent logistics vehicles equipped with advanced tracking and security features.
The core component of this implementation is the incorporation of state-of-the art tracking mechanisms, enabling real-time monitoring of vehicle locations and movements.
The proposed system aims to revolutionize logistics management, providing a seamless and secure experience for both customers and service providers.
arXiv Detail & Related papers (2024-02-19T04:44:24Z) - The Security and Privacy of Mobile Edge Computing: An Artificial Intelligence Perspective [64.36680481458868]
Mobile Edge Computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network's edge.
This paper provides a survey of security and privacy in MEC from the perspective of Artificial Intelligence (AI)
We focus on new security and privacy issues, as well as potential solutions from the viewpoints of AI.
arXiv Detail & Related papers (2024-01-03T07:47:22Z) - Towards Ubiquitous Semantic Metaverse: Challenges, Approaches, and
Opportunities [68.03971716740823]
In recent years, ubiquitous semantic Metaverse has been studied to revolutionize immersive cyber-virtual experiences for augmented reality (AR) and virtual reality (VR) users.
This survey focuses on the representation and intelligence for the four fundamental system components in ubiquitous Metaverse.
arXiv Detail & Related papers (2023-07-13T11:14:46Z) - Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM [62.62684911017472]
Federated learning (FL) enables devices to jointly train shared models while keeping the training data local for privacy purposes.
We introduce a VFL framework with multiple heads (VIM), which takes the separate contribution of each client into account.
VIM achieves significantly higher performance and faster convergence compared with the state-of-the-art.
arXiv Detail & Related papers (2022-07-20T23:14:33Z) - System Design for a Data-driven and Explainable Customer Sentiment
Monitor [2.490457152391676]
We present a framework for a data-driven decision support system which combines IoT and enterprise data to model customer sentiment.
The framework is applied in a real-world case study with a major medical device manufacturer.
arXiv Detail & Related papers (2021-01-11T18:29:50Z) - Situation Awareness and Information Fusion in Sales and Customer
Engagement: A Paradigm Shift [10.307548042529874]
Situation Awareness (SA) is at the center of effective sales and customer engagement in this new era.
We argue that Information Fusion (IF) is the key for developing the next generation of decision support systems for digital and AI transformation.
arXiv Detail & Related papers (2020-05-30T21:53:26Z) - Unsupervised Model Personalization while Preserving Privacy and
Scalability: An Open Problem [55.21502268698577]
This work investigates the task of unsupervised model personalization, adapted to continually evolving, unlabeled local user images.
We provide a novel Dual User-Adaptation framework (DUA) to explore the problem.
This framework flexibly disentangles user-adaptation into model personalization on the server and local data regularization on the user device.
arXiv Detail & Related papers (2020-03-30T09:35:12Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.