Description:FIELD OF THE INVENTION
The present invention relates to a system and method for adaptive multimodal fusion that leverages explainable natural language processing (NLP) to modulate contextual attention across diverse data modalities. It enables improved interpretability and performance in tasks involving integrated textual, visual, and/or audio inputs.
BACKGROUND OF THE INVENTION
Current Multimodal Artificial Intelligence systems, which integrate information from text (using Natural Language Processing - NLP) with other data sources like images or audio, struggle to transparently show how the specific meaning derived from the text dynamically guides their focus on relevant parts of the other data. These systems often operate like "black boxes," making it unclear why certain words or phrases cause the AI to prioritize specific visual features or sound segments. This fundamental lack of explainability and adaptive, text-driven control hinders user trust, makes debugging errors incredibly difficult, risks amplifying hidden biases learned from the data, and impedes efficient system improvement, highlighting a critical need for multimodal AI that can both adapt its focus based on linguistic context and clearly articulate this reasoning process.
Currently, while advanced multimodal AI models (like Transformer-based architectures used in visual search or question answering) utilize Natural Language Processing (NLP) input to implicitly guide attention across modalities like images or audio, no widespread commercial products explicitly solve the problem of dynamically controlling this fusion based on NLP semantics and providing clear explanations for that control. Present practices rely on these often opaque models, sometimes supplemented by general Explainable AI (XAI) tools (like LIME, SHAP, or attention map visualizations) applied post-hoc, which highlight important features but fail to elucidate the specific internal mechanism of how the text's meaning adaptively modulated the focus during processing. Cloud AI platforms similarly offer powerful multimodal capabilities but generally lack built-in transparency regarding this dynamic, NLP-driven internal reasoning and control process, leaving a gap for systems that integrate explicit semantic control with inherent explainability.
Presently available solutions fall short primarily due to a lack of transparency and integrated explainability regarding the dynamic control mechanism. While advanced multimodal models implicitly use Natural Language Processing (NLP) input to guide attention, they typically operate as "black boxes," failing to expose how specific semantic understanding derived from the text translates into explicit adjustments of focus on other modalities like images or audio. Furthermore, existing Explainable AI (XAI) techniques are often applied post-hoc and focus on general feature importance or attention outcomes, rather than providing a built-in, real-time explanation of the specific internal reasoning process where NLP semantics actively and dynamically modulate the cross-modal fusion strategy itself. Consequently, users and developers lack a clear understanding of why the system focused where it did based on the text, hindering trust, debugging, and targeted improvement.
Compared to previous solutions, the proposed invention offers significant advantages in transparency and dynamic adaptability. It provides clear, integrated explanations detailing how Natural Language Processing (NLP) semantics explicitly control cross-modal focus, fostering trust and simplifying debugging, unlike prior art's opaque models often requiring separate, post-hoc analysis tools that merely show outcomes. Fundamentally, it differs by using deep linguistic understanding as an explicit, real-time controller for attention modulation, tightly coupled with its own explanation generator, whereas existing methods typically rely on implicit influence within black-box models or employ static fusion strategies, lacking both the fine-grained dynamic control and the inherent mechanism to explain that specific control logic.
OBJECTIVES OF THE INVENTION
Main objective of the present invention is to develop an adaptive system that dynamically fuses multimodal data (e.g., text, audio, visual) based on contextual relevance.
Another objective of the present invention is to employ explainable natural language processing (NLP) techniques to modulate attention mechanisms within the fusion process.
Another objective of the present invention is to enhance interpretability and transparency of multimodal AI systems by incorporating explainable context-aware attention models.
Another objective of the present invention is to improve the accuracy and robustness of decision-making in tasks involving multimodal inputs, such as sentiment analysis or human-computer interaction.
Another objective of the present invention is to provide a modular and scalable architecture that can be integrated into various real-time applications requiring adaptive multimodal understanding.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
This invention solves the problem of opaque multimodal fusion by introducing an NLP-Driven Attention Modulator and an integrated Explainability Generator. It works by first deeply analyzing the input text using Natural Language Processing (NLP) to understand its semantic meaning, identifying key entities, actions, and attributes. Based on this analysis, the Attention Modulator generates specific control signals that dynamically adjust the attention mechanisms operating on other modalities (like images or audio), effectively telling the system where to focus based on the text's context. The features are then fused using these adaptively modulated attention weights. Crucially, the Explainability Generator leverages both the NLP analysis and the generated modulation signals to produce a clear, human-readable textual explanation detailing precisely how the linguistic input guided the system's focus across the different data sources, thus providing transparency and addressing the "black box" issue.
Herein enclosed a system for adaptive multimodal fusion using explainable NLP-driven contextual attention modulation, comprising:
a plurality of input modules configured to receive input data from at least one of: text, image, audio, and other modalities;
a plurality of feature extractors configured to extract modality-specific features from the respective inputs;
a Natural Language Processing (NLP) context analyzer configured to analyze semantic information from the text input;
an NLP-driven attention modulator operatively coupled to the NLP context analyzer and configured to generate modulation signals based on the semantic information;
an adaptive fusion module configured to receive and fuse modality-specific features using the modulation signals;
an explainability generator configured to generate a natural language explanation based on the semantic information and the modulation signals;
output modules configured to produce a task-specific output and a corresponding human-readable explanation.
A method for adaptive multimodal fusion using explainable NLP-driven contextual attention modulation comprising the steps of:
receiving multimodal input comprising at least one of text, image, audio, and other data;
extracting modality-specific features from the received inputs;
analyzing the text input using an NLP context analyzer to derive semantic information;
generating attention modulation signals based on said semantic information;
fusing the extracted features using the generated attention modulation signals via an adaptive fusion module;
generating a natural language explanation corresponding to the fused output based on the semantic and modulation information.
The explanation generated enables interpretability of the system by detailing the influence of text-derived semantic cues on attention over other modalities.
The fused representation is used to generate a task-specific output for applications including but not limited to classification, retrieval, or prediction.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SYSTEM ARCHITECTURE
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
In some embodiments of the present invention, this invention solves the problem of opaque multimodal fusion by introducing an NLP-Driven Attention Modulator and an integrated Explainability Generator.
In some embodiments of the present invention, it works by first deeply analyzing the input text using Natural Language Processing (NLP) to understand its semantic meaning, identifying key entities, actions, and attributes.
In some embodiments of the present invention, based on this analysis, the Attention Modulator generates specific control signals that dynamically adjust the attention mechanisms operating on other modalities (like images or audio), effectively telling the system where to focus based on the text's context.
In some embodiments of the present invention, the features are then fused using these adaptively modulated attention weights. Crucially, the Explainability Generator leverages both the NLP analysis and the generated modulation signals to produce a clear, human-readable textual explanation detailing precisely how the linguistic input guided the system's focus across the different data sources, thus providing transparency and addressing the "black box" issue.
Herein enclosed a system for adaptive multimodal fusion using explainable NLP-driven contextual attention modulation, comprising:
a plurality of input modules configured to receive input data from at least one of: text, image, audio, and other modalities;
a plurality of feature extractors configured to extract modality-specific features from the respective inputs;
a Natural Language Processing (NLP) context analyzer configured to analyze semantic information from the text input;
an NLP-driven attention modulator operatively coupled to the NLP context analyzer and configured to generate modulation signals based on the semantic information;
an adaptive fusion module configured to receive and fuse modality-specific features using the modulation signals;
an explainability generator configured to generate a natural language explanation based on the semantic information and the modulation signals;
output modules configured to produce a task-specific output and a corresponding human-readable explanation.
A method for adaptive multimodal fusion using explainable NLP-driven contextual attention modulation comprising the steps of:
receiving multimodal input comprising at least one of text, image, audio, and other data;
extracting modality-specific features from the received inputs;
analyzing the text input using an NLP context analyzer to derive semantic information;
generating attention modulation signals based on said semantic information;
fusing the extracted features using the generated attention modulation signals via an adaptive fusion module;
generating a natural language explanation corresponding to the fused output based on the semantic and modulation information.
The explanation generated enables interpretability of the system by detailing the influence of text-derived semantic cues on attention over other modalities.
The fused representation is used to generate a task-specific output for applications including but not limited to classification, retrieval, or prediction.
EXAMPLE 1
BEST METHOD
This invention solves the problem of opaque multimodal fusion by introducing an NLP-Driven Attention Modulator and an integrated Explainability Generator. It works by first deeply analyzing the input text using Natural Language Processing (NLP) to understand its semantic meaning, identifying key entities, actions, and attributes. Based on this analysis, the Attention Modulator generates specific control signals that dynamically adjust the attention mechanisms operating on other modalities (like images or audio), effectively telling the system where to focus based on the text's context. The features are then fused using these adaptively modulated attention weights. Crucially, the Explainability Generator leverages both the NLP analysis and the generated modulation signals to produce a clear, human-readable textual explanation detailing precisely how the linguistic input guided the system's focus across the different data sources, thus providing transparency and addressing the "black box" issue.
NOVELTY:
The invention's novelty lies in its integrated system where deep NLP semantic analysis generates explicit control signals to dynamically modulate cross-modal attention mechanisms, while simultaneously explaining this specific text-driven attention control process.
 
, Claims:1. A system for adaptive multimodal fusion using explainable NLP-driven contextual attention modulation, comprising:
a plurality of input modules configured to receive input data from at least one of: text, image, audio, and other modalities;
a plurality of feature extractors configured to extract modality-specific features from the respective inputs;
a Natural Language Processing (NLP) context analyzer configured to analyze semantic information from the text input;
an NLP-driven attention modulator operatively coupled to the NLP context analyzer and configured to generate modulation signals based on the semantic information;
an adaptive fusion module configured to receive and fuse modality-specific features using the modulation signals;
an explainability generator configured to generate a natural language explanation based on the semantic information and the modulation signals;
output modules configured to produce a task-specific output and a corresponding human-readable explanation.
2. A method for adaptive multimodal fusion using explainable NLP-driven contextual attention modulation as claimed in claim 1, wherein said method comprising the steps of:
a) receiving multimodal input comprising at least one of text, image, audio, and other data;
b) extracting modality-specific features from the received inputs;
c) analyzing the text input using an NLP context analyzer to derive semantic information;
d) generating attention modulation signals based on said semantic information;
e) fusing the extracted features using the generated attention modulation signals via an adaptive fusion module;
f) generating a natural language explanation corresponding to the fused output based on the semantic and modulation information.
3. The method as claimed in claim 2, wherein the explanation generated enables interpretability of the system by detailing the influence of text-derived semantic cues on attention over other modalities.
4. The method as claimed in claim 2, wherein the fused representation is used to generate a task-specific output for applications including but not limited to classification, retrieval, or prediction.