Store Observation Query Response System Using CNN Pre-processing
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Solution Overview
Problem
Current approaches to providing answers to store observation questions are inefficient due to high latency, inaccuracy, and resource wastage, primarily caused by human discretion and limited datasets in Visual Question Answering (VQA) techniques, leading to errors in data collection and analysis.
Innovation Solution
A system utilizing a broadened dataset and machine-learning (ML) techniques to generate question-answer pairs, employing a Convolutional Neural Network (CNN) model and an accelerator compiler to improve the accuracy and efficiency of responses to store observation queries, reducing reliance on human operators and enhancing generalizability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human operators are used to answer store observation questions, then flexibility and adaptability are maintained, but latency increases and accuracy decreases
Solution Approach 1:
The system performs preliminary actions by pre-processing store observation images and pre-generating potential answers with confidence scores before queries are submitted. The CNN model analyzes images in advance, creating a ready pool of annotated data that can be quickly retrieved and matched to queries, eliminating the need for real-time human analysis.
Solution Approach 2:
The system creates copies of human expert knowledge by training the CNN model on datasets annotated by human operators. The model learns to replicate human judgment and decision-making processes, producing answers that mirror human expertise without requiring actual human operators to be present during query resolution.
2Ease of operation
If human operators manually analyze store observation images, then complex visual questions can be answered, but resource wastage and computational expense increase
Solution Approach 1:
The system replaces expensive, resource-intensive human operator involvement with a cost-effective automated CNN model. The model can be deployed on standard computing infrastructure rather than requiring human labor resources, significantly reducing computational expense and resource wastage while maintaining the ability to answer complex visual questions.
Solution Approach 2:
The system substitutes the mechanical process of human visual analysis with an automated computer vision system. The CNN model automatically processes images, detects objects, and generates answers without requiring human operators to manually examine each image, thereby reducing resource consumption while preserving analytical capability.
3Productivity
If limited datasets are used in VQA techniques, then model training is faster and requires fewer resources, but accuracy and generalizability decrease
Solution Approach 1:
The system performs preliminary data collection and annotation by automatically processing large volumes of store observation images through the CNN model to generate training datasets. This pre-processing creates comprehensive training data that can be used to improve model accuracy and generalizability without requiring manual human annotation of each image.
Solution Approach 2:
The system changes the parameter of dataset size by leveraging the CNN model's ability to process and learn from large volumes of images. The model can effectively utilize extensive datasets that would be impractical for manual annotation, improving accuracy and generalizability while maintaining training efficiency through automated processing.
Data Source
AI summary
Methods, apparatus, systems and articles of manufacture are disclosed for providing responses to queries regarding store observation images. An example computer readable medium includes instructions that, when executed, cause a machine to at least obtain first metadata associated with a set of store dictionaries, select ones of the set of store dictionaries for use based on the associated first metadata, obtain second metadata associated with a set of question templates, select ones of the set of question templates for use based on the associated second metadata, generate question-answer pairs using the selected ones of the set of store dictionaries and the selected ones of the set of question templates, train a machine-learning model using the question-answer pairs, and provide query responses using the trained machine-learning model.