Multimodal Function Prediction for Dynamic Behavioral Pattern Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems lack the capability to integrate and interpret multimodal data from various digital interactions cohesively and predictively, failing to capture nuanced and dynamic behavioral patterns, and are vulnerable to subjective bias, leading to inadequate actionable insights.
Innovation Solution
A predictive function model that synthesizes heterogeneous data types such as audio tone, facial expressions, and textual sentiment, using advanced machine learning models to generate actionable insights by dynamically updating models with event markers and annotated training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional evaluation tools are used for static metrics or manual assessments, then implementation simplicity is maintained, but the ability to capture nuanced and dynamic behavioral patterns deteriorates
Solution Approach 1:
The patent replaces manual assessment mechanisms with automated machine learning models that process multimodal data. The system substitutes human evaluation with computational algorithms including large language models, vision models, and audio processing models that automatically analyze digital interactions across multiple modalities to generate predictive function data objects.
Solution Approach 2:
The patent combines multiple data modalities (audio, video, text, interaction patterns) into a unified multimodal data structure. This composite approach integrates heterogeneous data types from various digital interaction channels to create a comprehensive behavioral analysis framework that captures nuanced patterns beyond single-modality systems.
2Device complexity
If systems operate in silos analyzing data from a single modality, then system complexity is reduced, but the synthesis of heterogeneous data types deteriorates
Solution Approach 1:
The patent merges multiple independent data processing streams into a unified analysis framework. The system combines audio processing, video analysis, text processing, and interaction pattern recognition into an integrated model that generates comprehensive predictive insights, eliminating the information loss inherent in siloed systems.
Solution Approach 2:
The patent creates a universal predictive function model that handles multiple data modalities through a single framework. The system uses large language models and other AI models that can process diverse input types (audio, video, text, metadata) uniformly, enabling the same system to analyze various digital interaction formats without requiring separate specialized systems.
3Adaptability or versatility
If existing systems rely on predefined templates, then adaptability to new contexts is limited, but model training complexity is reduced
Solution Approach 1:
The patent implements dynamic model updating mechanisms that allow the system to adapt to evolving behavioral patterns. The machine learning models are continuously refined using feedback from new data, enabling the system to adjust its predictive capabilities in response to changing digital interaction patterns without requiring complete retraining or template updates.
Solution Approach 2:
The patent incorporates feedback loops where predictive function data objects are generated, applied, and used to further train and refine the models. This iterative process allows the system to learn from its predictions and improve its adaptability to new behavioral patterns while managing training complexity through incremental updates rather than comprehensive retraining.
Data Source
AI summary
Systems, methods, and non-transitory computer readable mediums are provided herein for receiving one or more multimodal data objects associated with a subject entity and one or more interactions with an electronic device. The subject entity can include a software module, a digital asset, a system component, an individual, or the like. A multimodal data object is derived from analysis of at least video, audio, and textual data. A predictive function data object is generated based on multimodal data objects by a predictive function model. A predictive function data object is configured to predict how subject entity expressions impact one or more additional entities. One or more actions are performed based on the predictive function data object. An action performed can include reconfiguring a composition of one or more structural data objects, generating electronic communications, and the like. Electronic communications are provided in real-time and/or subsequent to an interactive session.