Segmented AI Processing for Multimodal User Sentiment
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Solution Overview
Problem
Conventional user input processing techniques for image and video data are resource-intensive and error-prone, making it difficult to effectively understand user feedback and sentiments.
Innovation Solution
A method that involves dividing user input data into text and non-text components, converting non-text data to text, and using artificial intelligence techniques to classify both types of data for sentiment analysis, enabling automated actions based on the classification results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional user input processing techniques are used for image and video data, then processing can be performed, but the approach is resource-intensive and error-prone
Solution Approach 1:
The patent segments user input data into distinct components: text data, image data, and video data. Each segment is processed through dedicated processing paths with appropriate AI techniques (NLP for text, computer vision for images and videos), avoiding the resource-intensive approach of processing all data uniformly and reducing errors through specialized processing for each data type
Solution Approach 2:
The patent replaces conventional mechanical processing techniques with artificial intelligence-based processing. AI models and machine learning algorithms substitute traditional image and video processing methods, improving accuracy in sentiment analysis while optimizing resource utilization through intelligent data handling and processing
2Productivity
If conventional user input processing techniques are used, then processing can be performed, but the approach is error-prone
Solution Approach 1:
The patent implements feedback mechanisms where AI processing results are continuously evaluated and refined. Sentiment classification results from text, image, and video data are aggregated and used to improve overall understanding accuracy, with the system learning from processing outcomes to reduce errors and improve efficiency in subsequent operations
Solution Approach 2:
The patent creates a universal processing framework that handles multiple data types (text, images, videos) through a unified sentiment analysis system. This multi-functional approach improves productivity by processing diverse inputs through a single coordinated system while maintaining high reliability through consistent AI-based analysis across all data types
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for determining input sentiment by processing text data and non-text data using artificial intelligence techniques are provided herein. An example computer-implemented method includes dividing user input data into at least a first set of text data and a set of non-text data; converting at least a first portion of the non-text data into at least a second set of text data; classifying at least a portion of the at least a first set of text data and at least a portion of the at least a second set of text data in accordance with sentiment-related categories using a first set of artificial intelligence techniques; classifying at least a second portion of the non-text data in accordance with the sentiment-related categories using a second set of artificial intelligence techniques; and performing automated actions based on the classifying of the text data and/or the non-text data.