Predictive Multi-Modal Interaction for Merchandise Planning
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Solution Overview
Problem
Existing enterprise applications for assortment and merchandise planning require users to manipulate large datasets inefficiently using traditional input methods like mice and keyboards, which are inflexible and distracting, leading to inefficient decision-making.
Innovation Solution
A system utilizing multi-modal interactions that monitor eye, head, and hand movements, gestures, voice inputs, and tactile inputs to predict user actions, allowing for natural and efficient command issuance and data manipulation, with AI-driven personalization and contextual rendering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional input methods (mouse, keyboard) are used for data manipulation, then device complexity is reduced, but ease of operation and productivity deteriorate due to inefficiency and user distraction
Solution Approach 1:
The patent replaces mechanical input devices (mouse, keyboard) with biometric sensing systems including eye tracking, facial recognition, voice recognition, and gesture detection. This substitution eliminates the need for physical interaction devices while capturing user intent through natural biological signals, thereby improving ease of operation without requiring complex mechanical interfaces
Solution Approach 2:
The system introduces an intermediary layer of AI-driven interpretation that translates biometric data into actionable commands. This intermediary processes raw biometric signals (eye movements, facial expressions, voice patterns, gestures) and converts them into meaningful user intentions, bridging the gap between natural human behavior and system control without requiring direct mechanical interaction
2Productivity
If users manually sift through large datasets using traditional methods, then device complexity remains low, but loss of time and productivity increase significantly
Solution Approach 1:
The system performs preliminary actions by continuously monitoring biometric signals and pre-processing data according to detected user intent. Before users explicitly command actions, the system anticipates needs based on patterns in eye movement, facial expression, and gesture data, preparing relevant information and potential actions in advance to eliminate delays
Solution Approach 2:
The system implements continuous feedback loops where biometric data is constantly analyzed and used to adjust system responses in real-time. User reactions to displayed information are captured through biometric sensors, and the system adapts its presentations and recommendations based on this feedback, enabling rapid iterative refinement of data exploration without manual intervention delays
3Adaptability or versatility
If voice recognition software is used for interaction, then ease of operation improves slightly, but adaptability and naturalness remain limited compared to multi-modal biometric interaction
Solution Approach 1:
The patent merges multiple biometric modalities (eye tracking, facial recognition, voice recognition, gesture detection) into a unified interaction system. This combination allows the system to interpret user intent through multiple complementary channels simultaneously, providing greater adaptability to different user preferences and contexts while maintaining natural and intuitive operation through any single modality
Solution Approach 2:
The system achieves universality by designing a multi-functional biometric interface that can operate effectively across diverse interaction scenarios using any combination of biometric modalities. The same system architecture handles eye tracking for selection, facial recognition for identity verification, voice for commands, and gestures for navigation, providing versatile adaptability without requiring separate specialized systems for each function
Data Source
AI summary
A system and method are disclosed for generating intelligent multi-modal system actions based, at least in part, on predicting a user action and one or more stored user inputs. Embodiments include a database and a computer comprising a processor and memory, the computer is configured to monitor user inputs using one or more sensors and one or more tactile interface devices, detect at least two modes of user input and store the user inputs in the database. The computer is further configured to evaluate the stored user inputs in the database and the at least two modes of user input to generate a system action and generate a system action based, at least in part, on predicting a user action and one or more stored user inputs.