A system for multimodal generative AI for personalizing service interactions

Abstract

System (100) for personalizing service interactions, wherein the system (100) comprises: an input capture module (104) configured to receive user input in at least one modality; a coding module (106); a multimodal embedding generation module (108) configured to generate cross-modal representations; a context analysis and intent inference module (110) configured to determine the user's intent and the context state; a response planning module (112) configured to generate a personalized response using generative artificial intelligence; an artificial intelligence analysis module (114) configured to generate interpretability information for the planned response; a review and validation module (116) configured to perform ethical and political validation; a response generation and delivery module (118) configured to produce a final multimodal response; and a feedback capture, learning and secure logging module (120) configured to update the system based on feedback.

Description

AREA

[0001] The present subject matter concerns a system for the personalization of service interactions using multimodal generative artificial intelligence, in particular with human-in-the-loop and explainable artificial intelligence. GENERAL STATE OF THE ART

[0002] The rapid expansion of digital platforms and online services has led to an increasing reliance on automated systems to manage service interactions across various sectors, including customer support, healthcare, education, banking, and e-commerce. Traditional solutions largely rely on rules and operate with predefined scripts and static decision trees, which, while efficient and scalable, lack the flexibility required for complex, context-dependent, or emotionally nuanced interactions. Advances in artificial intelligence have given rise to machine learning-based dialogue agents and recommendation systems that enable enhanced personalization by learning from historical data.However, most existing systems primarily process textual input and are limited to operating with a single modality, thus failing to capture the full range of human communication, which by its very nature includes speech, tone of voice, facial expressions, and visual signals. SUMMARY

[0003] The subject matter of the present invention is defined in the claims. BRIEF DESCRIPTION OF THE FIGURES Fig. Figure 1 illustrates the functioning of the system for personalizing service interactions using a multimodal generative framework for artificial intelligence according to the implementation of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0004] Traditional service interaction systems are based on rule-based logic or limited machine learning models and typically process input from a single modality, primarily text. Such systems struggle to understand complex user intent, emotional contexts, and situational nuances, resulting in rigid, less reliable, and insufficiently personalized service interactions.

[0005] To address these limitations, this work provides a system for personalizing service interactions using a multimodal generative AI framework that integrates multimodal processing, explainable artificial intelligence, and human-in-the-loop validation into a single operational pipeline. The system receives service interaction inputs in one or more modalities, including text, audio, and video, thus capturing verbal, vocal, and visual signals. The inputs are preprocessed and encoded into structured, machine-readable representations and transformed through feature fusion into unified multimodal embeddings to enable comprehensive contextual understanding. Based on the fused representations, the system determines user intent, the interaction context, and the personalization parameters.A personalized response is then generated using generative artificial intelligence capable of producing context-aware and adaptive outputs. Before delivery, the planned response undergoes explainable AI analysis to generate interpretability information that identifies factors influencing response generation. The response and interpretability information are reviewed through a human-in-the-loop validation process to ensure compliance with ethical principles, guidelines, and contextual appropriateness. Following validation, a final multimodal response is provided, and the interaction feedback is securely logged and used to update and continuously refine the system's learning parameters.

[0006] Fig.Figure 1 illustrates the functionality of System 100 for personalizing service interactions using a multimodal generative framework for artificial intelligence. A service interaction is initiated, and multimodal user input is acquired by Module 104 for initiating service interactions and capturing multimodal input. The captured input, which includes one or more texts, audio data, and videos, is passed to a preprocessing and coding module 106, which transforms the raw multimodal data into structured and machine-interpretable feature representations. The coded features are then provided to Module 108 for generating multimodal embeddings and feature fusion to create cross-modal embeddings and a unified contextual representation.Based on the fused representation, a context analysis and intent inference module 110 performs an attention-based analysis to determine the user's intent and the interaction context. A response planning module 112 then generates a personalized response using generative artificial intelligence techniques. The planned response is analyzed by an explainable artificial intelligence analysis module 114 to generate interpretability information associated with the response. The response and interpretability information are then passed to a human-in-the-loop review and validation module 116, where ethical, political, and compliance aspects are assessed, and the response is refined based on human input, if necessary. After validation, a response generation and delivery module 118 generates and delivers a final multimodal service response to the user.After transmission, a feedback capture, learning and secure logging module 120 captures the feedback on user interaction, securely logs the interaction data and updates the learning parameters of the system 100 to enable continuous adaptation and improvement for subsequent service interactions.

[0007] Although the implementations of a system for personalizing service interactions have been described with respect to specific functional modules and operating configurations, it should be noted that the attached claims are not necessarily limited to the specific modules, components, or arrangements described. Rather, the disclosed functional components and system configurations are provided as illustrative examples of implementations of the system for enabling personalized, context-aware, and ethically sound service interactions.

Claims

[1] System (100) for personalizing service interactions, wherein the system (100) comprises: an input capture module (104) configured to receive user input in at least one modality; a coding module (106); a multimodal embedding generation module (108) configured to generate cross-modal representations; a context analysis and intent inference module (110) configured to determine the user's intent and the context state; a response planning module (112) configured to generate a personalized response using generative artificial intelligence; an artificial intelligence analysis module (114) configured to generate interpretability information for the planned response; a review and validation module (116) configured to perform ethical and political validation; a response generation and delivery module (118) configured to produce a final multimodal response; and a feedback capture, learning and secure logging module (120) configured to update the system based on feedback. [2] System (100) according to claim 1, wherein the input acquisition module (104) receives text, audio and video inputs. [3] System (100) according to claim 1, wherein the coding module (106) normalizes, structures and converts multimodal input data into machine-interpretable feature representations.

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